Modeling of cell signaling pathways in macrophages by semantic networks

Modeling of cell signaling pathways in macrophages by
semantic networks
by
Michael Hsing
B.Sc., Department of Molecule Biology and Biochemistry
Simon Fraser University, 2002
THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
in
THE FACULTY OF GRADUATE STUDIES
(CIHR/MSFHR Strategic Training Program in Bioinformatics
And
UBC Genetics Graduate Program)
THE UNIVERSITY OF BRITISH COLUMBIA
February 2005
© Michael Hsing 2005

ABSTRACT
Macrophages are essential components of human immune system that engulf and digest
pathogens using the molecular mechanisms of phagocytosis and phagosome maturation. These
processes are regulated by an essential enzyme – phosphoinositide-3-kinase (PI3K), a key
initiator of signalling cascades in many cellular processes. Importantly, experimental studies
demonstrate that some pathogenic bacteria, such as Mycobacterium tuberculosis (MTB), can
interfere with PI3K pathways in order to survive within host macrophages. Based on the
diverse roles of PI3Ks, it is reasonable to hypothesize that MTB effects upon PI3K signaling
could impact macrophages in numerous ways, more than what are currently studied. It is
anticipated that greater understanding of PI3K signaling mechanisms in macrophages and
bacterial interference could provide insights for developing effective strategies against MTB.
The complexity of PI3K pathways makes the analysis of MTB-macrophage interactions
a challenging task. Although a vast amount of knowledge on the pathways has been
accumulated in literature and databases, the information is encoded in static diagrams that are
difficult to study. While it is necessary to analyze complex systems computationally, the tools
for modeling pathways are inadequate. To address current limitation on pathway manipulation,
we applied an artificial intelligence method called Semantic Networks (SN) to model MTB
interference with PI3K signalling pathways in macrophages.
The advantage of SN is in its capacity to represent abstract concepts in machine friendly
formats termed “semantic agents” and “relationships”. In SN, the behaviour of agents is not
fixed, but instead emerges from their relationships. This characteristic makes SN well suited for
modeling biological systems. Using the SN methods, a model has been created to describe
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PI3K participation in macrophage signaling. The model encompassed a large amount of
information extracted from scientific literature and pertained such complex micro-events as
formation of protein complexes, chemical modifications of proteins, allosteric regulation, and
changes in intracellular localization by the agents. The data integration in the SN-environment
allowed us to reconstruct the molecular mechanisms of macrophage pathogenic invasion, and
the model predicted previously unobserved macrophage responses. The results will be used to
guide and interpret upcoming gene and protein expression studies.
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TABLE OF CONTENTS
Abstract ii
Table of Contents..............................................................................................................iv
List of Tables.....................................................................................................................vi
List of Figures .................................................................................................................vii
Acknowledgements ........................................................................................................ viii
Chapter 1 INTRODUCTION ...........................................................................................1
1.1 The current state of pathway representation and modeling.............................3
1.2 Bacterial infections in macrophages through PI3K and related pathway
interference......................................................................................................7
Chapter 2 METHODS.....................................................................................................13
2.1 Theory of semantic networks ........................................................................13
2.2 Implementation of semantic networks by Visual Knowledge and BioCAD
software .........................................................................................................16
Chapter 3 RESULTS .......................................................................................................20
3.1 A semantic model development for cell signalling pathways.......................20
3.1.1 Biological structures as semantic agents ...................................................21
3.1.2 Localizations and translocations as semantic agents.................................24
3.1.3 Non-covalent interactions as semantic agents...........................................26
3.1.4 Covalent interactions as semantic agents ..................................................29
3.1.5 Allosteric regulations as semantic agents..................................................31
3.1.6 Cellular responses as semantic agents.......................................................36
3.2 Reconstruction of macrophage pathways by semantic modeling .................37
3.2.1 Data sources and pathway reconstruction .................................................37
3.2.2 SN modeling of known MTB interference mechanisms ...........................39
3.2.2.1 MTB promotes actin polymerization and rearrangement in
macrophage............................................................................................................44
3.2.2.2 MTB promotes membrane delivery to plasma membrane in
macrophage............................................................................................................46
3.2.2.3 MTB inhibits phagosome-lysosome fusion in macrophage ..................46
3.2.2.4 MTB inhibits recruitment of oxidase complex to phagosome in
macrophage............................................................................................................47
3.3 Cause-effect SN simulation of macrophage pathways during infection .......48
Chapter 4 DISCUSSION.................................................................................................59
4.1 Use of SN modeling for predicting unknown macrophage responses to
infection.........................................................................................................59
4.1.1.1 MTB increases intracellular glucose uptake in macrophage .................60
4.1.1.2 MTB increases the rate of protein synthesis in macrophage .................60
4.1.1.3 MTB promotes cell division in macrophage .........................................61
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4.1.1.4 MTB promotes survival of macrophage................................................61
4.2 Advantages of using semantic networks for pathway modeling...................63
4.2.1 Specify the spatial organization of molecules...........................................64
4.2.2 Model proteins as logical, integrating and adaptive devices.....................64
4.2.3 Reduce the need for labels and descriptions..............................................64
4.2.4 Provide a direct communication from models to simulations...................65
4.3 Future directions............................................................................................66
4.3.1 A collaborative pathway modeling environment ......................................68
4.3.2 A potential tool for in silico drug discovery..............................................70
Chapter 5 CONCLUSION ..............................................................................................72
References.........................................................................................................................74
Appendices........................................................................................................................82
Appendix A - Definitions of the icons. ..........................................................................82
Appendix B - Semantic Network Environment for Cell-modeling (SNEC)..................84
Utilize current information to customize biological structures..................................84
Define the behavior of molecules by creating different types of events....................85
Analyze and traverse interactions upstream and downstream ...................................85
SNEC - screenshots....................................................................................................87
B1 - Protein search page.............................................................................................92
B2 - Protein's detail page............................................................................................93
B3 - Protein's localization page..................................................................................94
B4 - Protein's domain/site page..................................................................................95
B5 - Allosteric regulation summary page ..................................................................96
B6 - Allosteric regulation's detail - page ...............................................97
B7 - Allosteric regulation's detail - page ................................................98
B8 - Interaction summary page ..................................................................................99
B9 - Non-covalent interaction's detail page .............................................................100
B10 - Covalent interaction's detail page...................................................................101
B11 - Cellular response detail page..........................................................................102
Appendix C - List of molecules and events in the macrophage pathway model .........103
C1 - Molecules in the macrophage model................................................................103
C2 - Non-covalent interactions in the macrophage model.......................................107
C3 - Covalent interactions in the macrophage model ..............................................111
C4 - Allosteric regulations in the macrophage model..............................................113
C5 - Cellular responses and their conditions in the macrophage model ..................115
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LIST OF TABLES
Table 1. Resources incorporated in the BioCAD environment. .....................................18
Table 2. Classification of biological structures in six prototypes in the semantic model.22
Table 3. Classification of biological events in six prototypes in the semantic model. ...24
Table 4. Two types of states in the semantic model. ......................................................28
Table 5. Data sources used in macrophage pathway reconstruction...............................37
Table 6. Biological structure and event prototypes modeled in the macrophage
pathways. ..........................................................................................................39
Table 7. Macrophage responses known to be affected by MTB interference.................44
Table 8. Non-covalent interaction events in the simulation............................................56
Table 9. Covalent interaction events in the simulation...................................................56
Table 10. Allosteric regulation events in the simulation. .................................................57
Table 11. Translocation events in the simulation. ............................................................57
Table 12. Unknown macrophage responses affected by MTB interference.....................59
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LIST OF FIGURES
Figure 1. A typical pathway representation........................................................................2
Figure 2. Phagocytosis of bacteria in macrophages. ..........................................................7
Figure 3. General mechanisms for PI3K activation through cell receptors. ....................10
Figure 4. An example of a semantic network...................................................................14
Figure 5. The basic classes of semantic agents in VK. ....................................................17
Figure 6. Information flow between experimental sources and biological databases
implemented through semantic models. ...........................................................21
Figure 7. Spatial organization of intracellular structures in the semantic model.............23
Figure 8. Localization event of the semantic model. .......................................................25
Figure 9. Translocation event of the semantic model. .....................................................26
Figure 10. Non-covalent interaction event of the semantic model.....................................27
Figure 11. Covalent interaction event of the semantic model............................................31
Figure 12. A visualization of allosteric regulations and interactions between Ras and
PI3K-p110.........................................................................................................33
Figure 13. Allosteric regulation event of the semantic model. ..........................................35
Figure 14. Cellular response of the semantic model. .........................................................36
Figure 15. PI3K interaction map part one. .........................................................................41
Figure 16. PI3K interaction map part two..........................................................................42
Figure 17. PI3K interaction map part three........................................................................43
Figure 18. Interactions between Fc-gamma receptor and Lyn kinase................................49
Figure 19. A SN-based simulator, before a simulation run (time =0)................................50
Figure 20. A SN-based simulator, at the end of a simulation run (time=6). ......................51
Figure 21. The sequence of simulation steps. ....................................................................52
vii

ACKNOWLEDGEMENTS
I would like to acknowledge my scientific supervisor, Artem Cherkasov and my thesis
committee: Wyeth Wasserman and Leah Keshet for their advice and support. I thank Artem
Cherkasov, Joel Bellenson, Conor Shankey, Kyle Recsky and Shawn Anderson for their help
with the model development and implementation. I also thank Upstream Biosciences, Inc. and
Visual Knowledge, Inc. for providing the semantic software environment and database support.
I acknowledge Zakaria Hmama, Neil E. Reiner and Jimmy Lee for their advice on the bacterial
invasion process in macrophages. I thank Fiona Brinkman, David Baillie, Francis Ouellette and
Steven Jones for their advice and encouragement during my training in the CIHR/MSFHR
Strategic Training Program in Bioinformatics.
I would like to acknowledge the training program for providing the training and funding.
I thank Michael Smith Foundation for Health Research and NSERC for additional financial
support.
viii

CHAPTER 1
INTRODUCTION
Physical interactions among genes, proteins and ligands regulate all cellular processes
that are typically studied in networks, where molecules are visualized as nodes and interactions
are represented by edges. The intracellular networks are commonly investigated in the context
of signalling, metabolic and gene regulatory pathways (Ideker and Lauffenburger 2003).
Although it is conventional to study molecular interactions in each of these three contexts
separately, most cellular processes involve components from all pathway types. Therefore, it is
important to integrate all known intracellular interactions into a unified model. Moreover, an
adequate insight into molecular networks should include the dynamic behaviors of participating
entities. Unfortunately, most of the existing network representation and manipulation methods
do not capture the important features of interaction networks, such as protein allosteric
regulation, domain organization within proteins and change of their intracellular localization.
A pathway representation custom for the current databases such as Signal Transduction
Knowledge Environment - STKE (Gough 2002) and the Kyoto Encyclopaedia of Genes and
Genomes - KEGG (Kanehisa and Goto 2000), is illustrated in Figure 1, where an arrow with a
plus sign indicates an activating or promoting relationship, and a line with a minus sign and a
short bar at the end indicates a deactivating or inhibitory relationship.
1

Figure 1. A typical pathway representation.
Figure 1. A typical pathway representation. This pathway drawing represents the
relationships among 5 proteins A, B, C, D, E, and a cellular response, cell survival. An arrow
with a plus sign indicates an activating or promoting relationship. For example, protein A
activates protein C, and protein E promotes cell survival. A line with a minus sign and a short
bar at the end indicates a deactivating or inhibitory relationship. For instance, protein B
inhibits protein C.
The lines on such diagrams designate physical interactions between molecules such as
proteins, or they represent associations between molecules and cellular processes. The
information represented in this way is ambiguous because a non-covalent binding and a
chemical reaction are not clearly distinguished. In addition, it is difficult to determine the true
cause-effect relationships from protein A and B to protein D and E through protein C. For
instance, the diagram doest not reflect on whether the activation of protein C by A promotes the
activation of D or promotes the deactivation of E, or whether the both processes take place.
Even though the limitations and drawbacks of such simplified graphic representation are
obvious, the majority of the current pathway and interaction databases have been developed
with similar representations.
2

1.1 The current state of pathway representation and modeling
The current tools for pathway representation and modeling can be classified into two
broad groups: databases that store information on molecular interactions, and programs that
utilize the information for simulation.
Pathway databases such as KEGG (Kanehisa and Goto 2000), MetaCyc (Krieger et al.
2004) and MPW (Selkov et al. 1998) contain information on metabolic pathways for a great
number of studied species. STKE (Gough 2002), BioCarta (BioCarta 2004) and TRANSPATH
databases (Krull et al. 2003) focus on pathways related to cell signaling and gene regulation.
For instance, the Connection Maps database in STKE contains about 60 pathway diagrams that
were created by pathway authorities and cover major signaling processes such as MAPK, G-
protein, insulin and PI3K pathways (Gough 2002). In addition, the aMAZE project offers an
object- oriented environment that integrates biological entities and interactions from metabolic,
cell-signaling and gene regulatory pathways (Lemer et al. 2004). Although theses databases
contain valuable knowledge on biological pathways, the information is represented in static,
non-linked diagrams that are difficult to analyze computationally.
Protein interaction databases such as BIND (Bader, Betel, and Hogue 2003), IntAct
(Hermjakob et al. 2004b), DIP (Xenarios et al. 2002), HPRD (Peri et al. 2003), GRID
(Breitkreutz, Stark, and Tyers 2003a) and MINT (Zanzoni et al. 2002) put more emphasis on
storage and retrieval of individual protein-protein interactions that are experimentally verified.
In particular, BIND is a valuable source of data that currently contains about 140,000
interactions from 1,050 organisms such as human, mouse, drosophila and yeast (Bader, Betel,
and Hogue 2003). Interactions in BIND are carefully curated with experimental evidence
including co-immunoprecipitation, affinity chromatography, and yeast two-hybrid test. The
data model of BIND includes three main types of objects: interactions, molecular complexes
3

and pathways (Bader and Hogue 2000). An interaction object describes an interaction between
proteins, DNA or RNA, encompassing information on binding sites, chemical actions, kinetics,
and chemical states. A molecular-complex or pathway object defines a collection of interaction
objects that form a complex or pathway, respectively.
In addition, there are several computational approaches that predict protein-protein
interactions and complement the limited experimental data. For example, STRING predicts
protein-protein interactions, which include physical and functional associations, from genomic
context, co-mentioning of gene names in PubMED abstracts and co-regulation of genes in
microarrays (von Mering et al. 2005). STRING developed a scoring system that combines
different types of evidence (including both predictions and high-throughput experimental data)
and assigns each protein-protein interaction a confidence score. Other methods predict proteinprotein
interactions based on interacting protein domains. InterDom utilized a collection of
annotated protein domains from Pfam (Bateman et al. 2004) and derived domain-domain
interactions from sources including protein complexes at PDB (Bhat et al. 2001),
experimentally verified protein interactions in BIND and DIP, and gene fusion (Ng, Zhang, and
Tan 2003). Scansite contains a collection of interaction rules between short sequence motifs
and domains (Obenauer, Cantley, and Yaffe 2003).
These interaction databases provide a good collection of experimentally determined or
predicted protein interactions. However, unlike the pathway databases, most of the interactions
are not associated with each other in the context of cellular processes. In addition, the current
representation for interactions cannot capture the conformational and functional changes of
their participating proteins.
Since these databases do not provide a dynamic insight into the interactions, there exist
a number of computational approaches that simulate cell processes dynamically using the static
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data collections described above. Programs such as E-cell (Tomita et al. 1999), Gepasi 3
(Mendes 1997), Virtual Cell (Loew and Schaff 2001) and BioSPICE (Garvey et al. 2003), use
differential equations to represent molecular interactions quantitatively (Neves and Iyengar
2002). In particular, E-cell developed a whole-cell simulation in Mycoplasma genitalium, based
on a minimal set of 127 genes (Tomita et al. 1999) and has attempted to simulate metabolic
pathways in human erythrocyte (Tomita 2001).
However, many cellular processes are sensitive to the stochastic behavior of a small
number of cell components, which compromise the suitability of differential-equation methods
(Le Novere and Shimizu 2001). Because differential equations treat each molecular species as a
single variable, a molecular event cannot be tracked individually in a simulation.
Several studies have attempted to address the stochastic character of cellular processes.
Vasudeva and Bhalla proposed a hybrid simulation method that combined both deterministic
and stochastic calculations (Vasudeva and Bhalla 2004). A stochastic simulator, StochSim
represented molecules as individual software objects that interact according to probabilities (Le
Novere and Shimizu 2001). Although these two approaches demonstrated the utility of
stochastic simulation on individual molecules, the programs are limited in modeling
intracellular translocations and functional roles of protein domains.
In addition to the pathway/interaction databases and the simulation programs, there
exist several languages that facilitate the exchange of interaction data and pathway models. An
XML-based format, called PSI-MI, has been developed for exchanging and retrieving proteinprotein
interaction data from databases such as BIND, DIP and IntAct (Hermjakob et al. 2004a).
The BioPAX project develops a common format for sharing and exchange of biological
pathway data (BioPAX 2005). The System Biology Markup Language (SBML) has been
5

developed for representing biochemical reaction networks and for communicating models used
for various simulation programs (Hucka et al. 2003).
Tools such as Cytoscape (Shannon et al. 2003) and Osprey (Breitkreutz, Stark, Tyers
2003b) visualize molecular interaction data in various graph layouts, composed of nodes
(molecules) and edges (interactions). In particular, Cytoscape can assign different attributes to
nodes and edges, and can overlay data such as gene expression on top of protein-protein
interaction networks. Cytoscape has several "plug-in" modules that support network analysis
and data import in PSI-ML or SBML format (Shannon et al. 2003). In addition, there are
several pathway environments such as PATIKA (Demir et al. 2002) and the Pathway Tools
software (Karp, Paley, and Romero 2002) that enable automatic pathway creation from
annotated genomes and manual pathway reconstruction by experts. They provide tools for
manipulating and analyzing pathways, but they currently lack simulation capability.
In our research, we attempt to address the shortcomings in pathway manipulation
through semantic modeling of biological pathways. In collaboration with a biotechnology
company, Upstream Biosciences, Inc., we have utilized an artificial intelligence method,
Semantic Networks (SN), to develop and implement a model, which represents and integrates
complicated information on protein complex formations, chemical modifications, allosteric
regulations, intracellular localizations and cause-effect relationships in pathways. We have
considered the cell signalling events involving the phosphoinositide-3-kinases (PI3K) -related
pathways that are affected by Mycobacterium tuberculosis (MTB) during the macrophage
internalization. By doing so, we did not only bring a well-developed SN platform to the field of
biological data integration, but also used the advantage of semantic networks for identification
of previously unappreciated cellular responses occurring during and upon infection.
6

1.2 Bacterial infections in macrophages through PI3K and related pathway
interference
Macrophages express a variety of cell-surface receptors that can bind to bacterial
surface molecules such as lipopolysaccharide and peptidoglycan, or to the Fc portion of
antibodies and the C3b complements associated with pathogenesis (Ernst 1998). Upon ligand
binding, the macrophage cell-surface receptors become activated and trigger the phagocytosis
of bacteria (Tjelle, Lovdal, and Berg 2000). Figure 2 shows the phagocytosis process in
macrophage, which involves actin polymerization and rearrangement at the site of bacterial
contact. As new membrane is delivered by intracellular vesicles, the macrophage plasma
membrane is extended to surround a bacterium, forming a cup structure called a pseudopod.
The pseudopod is further extended until the whole pathogen is engulfed inside a newly formed
organelle called a phagosome.
Figure 2. Phagocytosis of bacteria in macrophages.
Figure 2. Phagocytosis of bacteria in macrophages. The picture shows macrophages ingesting
green fluorescent mycobacteria (indicated by arrows). The host cell membrane was stained by
red fluorochorme PKH to define the limit of the cell. (The picture was provided by Zakaria
Hmama, Division of Infectious Diseases, Dept. of Medicine, University of British Columbia)
The phagosome goes through a maturation process, fusing with lysosomes to form
phagolysosome, which contains lysozymes and acid hydrolases that can degrade bacterial cell
walls and proteins (Tjelle, Lovdal, and Berg 2000). In addition, the NADPH oxidase complex
7

is assembled on the phagosomal membrane to catalyze the production of toxic oxygen-derived
compounds such as hydrogen peroxide, superoxide, hypochlrorite, nitric oxide and hydroxyl
radicals (Stephens, Ellson, and Hawkins 2002). The pathogen is normally killed during
phagosome maturation.
However, it has been observed that some pathogens sustain their infections through
surviving within host macrophages (Meresse et al. 1999; Tjelle, Lovdal, and Berg 2000). The
eukaryotic parasite Trypanosoma cruzi and bacteria including Shigella flexneri and Listeria
monocytogenes can lyse phagosomal membrane and escape into host cytosol. The eukaryotic
pathogen Leishmania mexicana and the bacterium Coxiella burnetii have also developed
mechanisms to survive in the harsh environment inside the phagolysosome. Moreover, some
bacteria such as Salmonella trphimurium and Mycobacterium tuberculosis manage to inhibit
the phagosome maturation and reside inside the immature phagosomes (Finlay and Falkow
1997; Russell 2001).
A remarkable example of major pathogens capable of surviving inside macrophages is
Mycobacterium tuberculosis (MTB) that causes serious lung infection. It is estimated that 1.7
to 2.0 billion people world-wide are currently infected by MTB, and 3 million deaths a year are
attributable to tuberculosis (Health & Development Initiative 2004). In healthy individuals,
infected macrophages are confined in a lesion called tubercle. When the immune system of the
infected individual is weakened by drugs or other diseases, the MTB infection can be
reactivated and spread in the lungs and to other organs.
Both phagocytosis and phagosome maturation are regulated by complicated
intracellular pathways (Stephens, Ellson, and Hawkins 2002). It has been hypothesized that
MTB target and modify components in these pathways to ensure its intracellular survival (Fratti
et al. 2001). Many studies have been done to identify the individual molecular interaction
8

involved in the pathways (Stephens, Ellson, and Hawkins 2002; Gu et al. 2003). Thus, it has
been previously shown that there is a family of enzymes, phosphoinositide 3-kinases (PI3Ks),
that plays critical roles in regulating both phagocytosis and phagosome maturation (Vieira et al.
2001).
The class I PI3K is required for phagocytosis, while the class III PI3K is responsible for
phagosome maturation (Vieira et al. 2001). The class I PI3Ks are composed of a p85 regulatory
subunit and a catalytic subunit p110 (Vanhaesebroeck and Waterfield 1999). The p110 is an
allosteric enzyme that is activated when it binds small G-protein Ras-GTP or when the p85
subunit is bound to phosphotyrosine site by a SH2 domain. Activation of class I PI3K induces
macrophage phagocytosis, and it has been established that some pathogens, including the MTB
initiate this process via interactions with cell receptors (such as Fc-gamma receptor) linked to
class I PI3K. Such interaction leads to auto-phosphorylation of receptor’s phosphotyrosine site,
if the receptor contains tyrosine kinase domain as illustrated in Figure 3 (Wymann, Zvelebil,
and Laffargue 2003). In other cases a receptor can bind and activate additional tyrosine kinases
that phosphorylate the phosphotyrosine sites on adaptor proteins. Over 50 different receptors
are known to activate the class I PI3K by either of these two mechanisms (Wymann, Zvelebil,
and Laffargue 2003).
9

Figure 3. General mechanisms for PI3K activation through cell receptors.
Figure 3. General mechanisms for PI3K activation through cell receptors. The class I PI3K
enzymes are activated through two types of pathways. (1) Upon ligand binding, receptors that
contain kinase domains dimerize and auto-phosphorylate each other at phosphotyrosine sites.
Phosphorylated tyrosine binds to the SH2 domain on p85 and activates p110. (2) Receptors
that lack kinase domains activate additional kinase proteins. Those kinases phosphorylate
phosphotyrosine residues on adaptors proteins, which in turn activate PI3K. Activated PI3K
phosphorylates PIP2 into PIP3, which induce the activation of downstream kinases and
cellular responses. Blue circles represent proteins, and yellow circles are domain and sites.
Black arrows indicate direct interactions including bindings and chemical reactions, while red
arrows indicate the indirect relationships to cellular responses. Abbreviations: R = receptor,
K=kinase domain, pY = phosphotyrosine. This figure is adapted from Figure 1 of Cantley's
paper (Cantley 2002) on PI3K pathways.
Once the PI3K-p110 is activated by either receptors or adaptor proteins, PI3K-p110
phosphorylates membrane-bound lipid phosphatidylinositol-4,5-bisphosphate (PIP2) into
phosphatidylinositol-3,4,5-bisphosphate (PIP3). PIP3 binds to pleckstrin homology (PH)
domains of proteins such as PDK1 and AKT1, and the interactions localize those proteins to the
cell membrane (Vanhaesebroeck et al. 2001). To the date, about 97 human proteins have been
associated with the PH domain, according to the prediction from Pfam (Bateman et al. 2004),
and these proteins can potentially interact with PIP3. Through PIP3, the class I PI3K can
10

egulate a variety of cellular signalling events including cell survival, cell growth, replication,
transcription, and translation (Cantley 2002; Wymann, Zvelebil, and Laffargue 2003). These
studies imply that the activation of the class I PI3K in macrophage by intracellular pathogens
not only lead to the known phagocytosis response, but also cause multiple changes in the cell
that are important to recognize.
Another pathogenic mechanism employed by the MTB for macrophage manipulation
through the PI3K pathways involves deactivation of the class III of the enzyme. The class III
PI3K also consists of two subunits. These are a p150 subunit, which is a Ser/Thr protein kinase,
and an active PIK3C3 (homolog of Vps34p in yeast) unit that phosphorylates
phosphatidylinositol (PI) lipid to phosphatidylinositol-3-phosphate (PI3P) (Vanhaesebroeck
1999). The p150 subunit serves as an anchor linking PIK3C3 to phagosomal or lysosomal
membrane (Murray et al. 2002; Stephens, Ellson, and Hawkins 2002).
It has been suggested that the MTB has developed a mechanism of competitive binding
to PIK3C3 subunit of class III PI3K by producing a pathogenic analogue of PI3P lipid,
ManLAM (Mannose-capped lipoarabinomannan) (Fratti et al. 2001). By establishing such
PI3K binding competition, the MTB prevents the further production of PI3P substrates that lead
to a suppression of the superoxide generating complex and EEA1 (early endosome antigen 1)
recruitments which are essential for normal phagosome maturation. The exact mechanism of
this pathogenic interference is not well studied, and its implications are not fully understood.
Nonetheless, it is clear that the PI3K enzymes have significant implications in bacterial
invasions.
We anticipate that the detailed reconstruction of PI3K pathways by the semantic
networks would allow us to answer the following questions. Firstly, which and how
11

macrophage pathways are affected by MTB? Secondly, what molecules are involved in those
pathways? Thirdly, what cellular responses can be induced by MTB?
These questions are addressed by two bioinformatics objectives. The first objective is to
develop a biological SN-language" or a semantic model for representing and modeling cell
signaling pathways. The second one is to apply the resulting model for reconstructing
macrophage pathways from the literature and for predicting MTB interference.
12

CHAPTER 2
METHODS
2.1 Theory of semantic networks
Semantic networks were first introduced and formalized by Griffith, R.L. in 1982, as a
general method to represent complex information by nodes and edges in a graphic form. Nodes
(semantic agents) represent abstract concepts, and the identity and behaviors of each agent is
defined by its edges (relationships) with other agents in a semantic network (Griffith 1982;
Visual Knowledge 2004).
An example of a semantic network is illustrated on Figure 4, and it contains five agents
and eight relationships. Semantic agents are connected by reciprocal relationships, and agents
that share common properties are classified into the same category. For instance, the agent
[Protein A] has a relationship {instance of} with the agent [Protein] (a prototype), which has
the opposite relationship {prototype of} with [Protein A]. Similarly, [Protein B] has a
relationship {instance of} with [Protein]. Hence, [Protein B] is in the same category as [Protein
A] because they share the same prototype. In addition, the composition relationships allow
agents to be related to their components. For example, [Protein A] and [Protein B] have
relationships {composed of} with [Domain A] and [Domain B] respectively. The opposite
relationship of {composed of} is {component of}.
13

Figure 4. An example of a semantic network.
Figure 4. An example of a semantic network. Characteristics and behaviors of a semantic
agent are defined by its relationships with other agents. Semantic agents are represented as
nodes, and relationships are depicted as edges. This semantic network conveys the information
that Protein A and Protein B are instances of a Protein (a prototype), and they are composed of
Domain A and Domain B respectively.
When a semantic network is implemented within a computing environment, it can
efficiently model complex systems and solve multi-component problems (Griffith 1982). The
underlying design principle of a semantic network is important as it affects its capability to
represent the complexity of the system. For instance, it has been suggested that while some
simple concepts or ideas can be sufficiently represented by a single agent, a more complicated
concept should be modeled by a set of interconnected agents (Griffith 1982). A representation
of concepts by over-complicated agents or relationships can make the semantic network too
14

descriptive, and hence impair its ability to integrate and consolidate similar concepts and to
identify emerging properties. Therefore it is beneficial to represent a complicated concept (such
as a protein or a chemical reaction) with a set of simple, reusable and well-classified agents
interconnected by fundamental relationships such as the prototype and composition
relationships as described.
Since the introduction of semantic networks in the 1980's, this methodology of
knowledge representation has influenced artificial intelligence, relational database technology
and object-oriented programming (Griffith 1982; Visual Knowledge 2004). Recently semantic
networks have gained significant attention of biological community as a powerful tool for
organizing and integrating large amounts of biological information (McCray and Nelson 1995).
For instance, the semantic network in the Unified Medical Language System (UMLS) was
designed to retrieve and integrate biomedical information from various resources (Lindberg,
Humphreys, and McCray 1993). The UMLS semantic network has also been applied and
expanded to include information and knowledge from other domains such as genomics (Yu et
al. 1999). The BioMOBY project has applied the "semantic web" concept for the integration
and communication between bioinformatic tools and databases that use different data types
(Wilkinson and Links 2002). Other studies have suggested a semantic approach where proteins
are viewed as "adaptive and logical agents", whose properties and behaviors are affected by
other agents in their spatial organization including intracellular compartments and protein
complexes (Fisher, Paton, and Matsuno 1999; Fisher, Malcolm, and Paton 2000). Defining the
semantics among agents could characterize both local and global behaviors of a system, and
therefore, it is potentially useful to apply such approach to study cell signalling in biological
systems (Fisher, Malcolm, and Paton 2000).
15

Recently, an SN-based application development environment known as Visual
Knowledge (VK) has been developed by a Vancouver-based software development company,
Visual Knowledge, Inc. The VK environment has been shown capable of different
formalizations and implementations of semantic networks, and it allows information from
various domains to be properly integrated (Visual Knowledge 2004).
2.2 Implementation of semantic networks by Visual Knowledge and
BioCAD software
Visual Knowledge is an application development environment that implements the
theory of semantic networks and other contemporary computational methods including set
theory, frame system, object-oriented modeling theory and systems based on networks of active
software agents (Visual Knowledge 2004). VK is distinguished from other passive knowledge
representation technologies by its dynamics, scalability, and capability of active representation
and integration of different domain knowledge.
To model real-world systems, VK has implemented several fundamental classes of
agents in semantic networks, some of which are presented in Figure 5. VK allows creation of
prototypes within each basic class and enables further classification of agents based on their
common properties. Therefore any form of "semantic models" can be developed by meaningful
connections of agents through specific relationships. The models then act as the medium that
translates and integrates information from different domains into semantic networks.
For instance, a semantic agent of the class "physical thing" models a physical object that
has a shape and occupies space (Visual Knowledge 2004). An agent of the class "event"
represents a phenomenon or a change that occurs on a physical object over a period of time. To
enable application development such as a website, the VK contains application-specific agents
such as triggers, operations, and reports. A trigger is an agent that spawns other agents and
16

changes their relationships. An operation agent searches and collects other agents with certain
properties, and a report agent displays the results in an application.
Figure 5. The basic classes of semantic agents in VK.
Figure 5. The basic classes of semantic agents in VK. Semantic agents in the Visual
Knowledge environment are classified according to their common properties and functions.
Each class contains its own computer-codes and a unique set of relationships that
defines the intrinsic behaviors of all its instances. Each agent is reusable and contains
instructions to act automatically when it is connected to the proper agents. The corresponding
graphic user-interface in VK allows users to conveniently implement SN models and
applications without any computer-code writing, but rather by simple dragging and dropping of
SN agents in-and-out their relationships.
Previously, Visual Knowledge has been successfully used to model and manipulate
various complex systems including corporate enterprise environment, flight scheduling,
hardware maintenance simulators, and integrated currency exchange boards (Visual Knowledge
2004). It has been anticipated that the Visual Knowledge platform can address current
limitations in the modeling of cell signaling pathways. The specialized, biology-oriented VK
17

application package called BioCAD has been developed and delegated to the Vancouver-based
bioinformatics company Upstream Biosciences, Inc. (BioCAD 2004).
BioCAD software provides standard bioinformatics tools for managing large-scale
biological data, and for visualizing and editing biological pathways. BioCAD currently
contains millions of biological concepts that were extracted from publicly available
bioinformatics resources. For instance, BioCAD contains 40,512 proteins from Homo sapiens
as other organisms including Saccharomyces cerevisiae, Drosophila, Mus musculus and Rattus.
Within the BioCAD environment each prototypical protein is connected to various annotations
derived from RefSeq (Pruitt, Tatusova, and Maglott 2005), GenBank (Benson et al. 2005), and
Gene Ontology (Harris et al. 2004). Table 1 shows some of the resources that have been
incorporated into the BioCAD environment and their database version numbers. Information on
protein domains and sites have also been imported and integrated into BioCAD. For example,
the database currently contains 7,316 domains from Pfam (Bateman et al. 2004), 1,331 sites
from Prosite (Sigrist 2002), and domains and sites from eight other domain databases. Each
protein has been connected to its proper protein domains according to the annotation from
InterPro (Mulder 2005). To facilitate prediction of protein-protein interactions, 30,037 domaindomain
interactions have been incorporated from sources such as InterDom (Ng et al. 2003).
Table 1. Resources incorporated in the BioCAD environment.
Database Database version Date of import
RefSeq 3 Feburary, 2004
Unigene May, 2004 (release date) May, 2004
Gene Ontology April, 2003 (release date) April, 2003
InterPro 7.2 May, 2004
Pfam 12.0 May, 2004
PROSITE 18.10 May, 2004
18

PRINTS 37.0 May, 2004
ProDom 2002.1 May, 2004
Smart 4.0 May, 2004
TIGRFAMs 3.0 May, 2004
PIR SuperFamily 2.41 May, 2004
SUPERFAMILY 1.63 May, 2004
InterDom 1.2 August, 2004
The existing biological concepts and resources in BioCAD provide an excellent
environment for studying macrophage pathways in humans. A locally installed client program
allows additional semantic agents to be easily created, stored and queried from a remote central
server located at Upstream Biosciences, Inc. Using the BioCAD environment, we have
developed and implemented a SN-based language or a semantic model that is capable of
representing the complex molecular mechanisms such as the PI3K-controlled regulation of cell
signalling in human macrophage.
To facilitate the reconstruction and analysis of the macrophage pathways and MTB
interference, we have built a web-based application called Semantic Network Environment for
Cell-modeling (SNEC). SNEC utilizes the developed semantic model and the existing
biological entities in BioCAD's database for collaborative pathway reconstruction. The unique
features of SNEC are discussed in Appendix B.
19

CHAPTER 3
RESULTS
3.1 A semantic model development for cell signalling pathways
One of the basic concepts of the SN methodology is ‘a model’ that may refer set of
rules in two independent contexts. A "semantic model" designates specific rules for translating
biological concepts into semantic agents and relationships. A "pathway model" encompasses
rules specifying what, how, when and where molecules can interact. The basic SN methodology
for constructing a pathway model is represented in Figure 6, which illustrates how information
from experimental observations is translated and integrated into pathway models. The semantic
models communicate with the Visual Knowledge environment, storing information in the forms
of semantic agents and relationships. Such organization of biological information by the SN
environment allows effective querying, analysis and inference of the pathway models that can
significantly facilitate testing of biological hypothesis and guide experimental efforts.
“A version of this chapter has been published. Hsing, M., J. L. Bellenson, C. Shankey, and A.
Cherkasov. 2004. Modeling of cell signaling pathways in macrophages by semantic networks.
BMC Bioinformatics 5 (1):156.”
20

Figure 6. Information flow between experimental sources and biological databases
implemented through semantic models.
Figure 6. Information flow between experimental sources and biological databases
implemented through semantic models. Biological data and information are generated from
experimental observations and integrated into pathway models. Semantic models translate the
pathway information into semantic agents and relationships in the Visual Knowledge
environment, which store the information in a database.
3.1.1 Biological structures as semantic agents
All biological structures can be considered as physical objects. Within a semantic
network, specifically, they can be represented as semantic agents of the "physical thing" class.
Six prototypes are introduced to address distinctive subgroups (Table 2).
21

Table 2. Classification of biological structures in six prototypes in the semantic model.
Semantic Agent – Physical thing
Cell
Intracellular Compartment
Macromolecule
Domain and Site
Small Molecule and Molecular
Fragment
Atom
Biological Example
Human macrophage, Mycobacterium tuberculosis
Plasma membrane, cytosol, phagosome, nucleus
Protein, nucleic acid, polysaccharide, fat/lipid
Catalytic domain, SH2 domain, PH domain, binding
site, phosphorylation site, promoter, gene regulatory
site.
Amino acid, nucleotide, sugar, fatty acid
Hydrogen, carbon, oxygen, nitrogen, phosphorus,
sulphur
Table 2. Classification of biological structures in six prototypes in the semantic model. Six
major prototypes classify biological structures that are relevant in the study of cell signaling
pathways in macrophages. The second column lists biological examples of each.
From the highest to the lowest level, they are positioned as the following [Cell],
[Intracellular Compartment], [Macromolecule], [Domain/Site], [Small Molecule/Molecular
Fragment], and [Atom]. The [Macromolecule] prototype is further classified into four subprototypes:
[Protein], [Nucleic acid], [Polysaccharide], and [Fat and Lipid]. The [Domain/Site]
objects represent domains, which are common structural folds in macromolecules, and sites,
which are short-sequence motifs or post-translational modification sites. The [Small
Molecule/Molecular Fragment] has been further divided into four subgroups: [Amino acid],
[Nucleotide], [Sugar], and [Fatty acid]. The final prototype is [Atom] that models individual
chemical elements. Because cell signaling pathways are composed of molecules and their
interactions, atoms are not considered further in the modeling.
Composition relationships relate each biological structure to its components. Figure 7
illustrates the semantic representation of a macrophage cell. The semantic agents are
represented as individual icons (Appendix A contains the definitions of the icons), and the
22

semantic relationships are depicted as solid arrows. Although all agents are related by pairs of
reciprocal relationships in SN, we depict only one direction for simplicity. A solid arrow
represents the {composed of} relationship. A dotted arrow indicates that there are additional
agents and relationships between the icons. For instance, [Cytosol] and [PDK1] are linked
through a [localization event] agent.
Figure 7. Spatial organization of intracellular structures in the semantic model.
Figure 7. Spatial organization of intracellular structures in the semantic model. Biological
structures are modeled by semantic agents, which are related to their components by the
composition relationships. A human macrophage has been modeled as a semantic agent of the
[Cell] prototype, and it is composed of various [Intracellular Compartment] agents, including
plasma membrane, cytosol, nucleus and others. Each compartment such as cytosol has linked
to [Macromolecule] and [Small Molecule//Molecular Fragment] agents including proteins,
ATP and GTP. A macromolecule such as a protein is further composed of [Domain/Site]
agents.
23

3.1.2 Localizations and translocations as semantic agents
Six interaction types have been incorporated into the semantic model, each represented
by a semantic agent of the [Event] class (Table 3).
Table 3. Classification of biological events in six prototypes in the semantic model.
Semantic Agent - Event
Localization
Translocation
Non-covalent Interaction
Covalent Interaction
Allosteric Regulation
Cellular Response
Biological Examples
A protein is located in the cytosol
A protein moves from cytosol to plasma
membrane.
A ligand binds to a receptor.
An enzyme catalyzes a chemical reaction
where substrates are converted to
products.
A ligand binding on site A of a protein
causes a conformational change on site B
of the protein.
A qualitative cellular behavior such as cell
survival, cell death, phagosome formation,
and an increase of intracellular glucose
level.
Table 3. Classification of biological events in 6 prototypes in the semantic model. Six major
event prototypes represent interactions among biological structures. The first column contains
the six prototypes, and the second column contains biological examples of the correspond
prototypes.
Here it is important to distinguish between two kinds of agents in semantic networks:
the prototypical agent and the instance agent. A prototypical protein such as PI3K-p110 can
have many instances that inherit the same properties from the prototype. Similarly, a
prototypical event can have event instances, each of which considers a particular occurrence of
an event on molecular instances. The prototypical agents represent the pathway information
and define how their instances should behave in a simulation program.
24

To capture the complete information associated with an interaction, a corresponding
event considers biological structures at different organizational levels. For instance, a
[localization event], which represents subcellular localization of a molecule, connects not only
a [molecule] agent, but also an [intracellular compartment] agent. A prototypical molecule
agent can associate with multiple cellular compartments through different localization events.
An example of localization events has been illustrated in Figure 8, in which PI3K-p110 can be
located in the cytosol, plasma membrane or phagosome. Although a prototypical molecule has
been linked to multiple locations, we have specified that a given instance can only be present at
one place at any given time in a simulation.
Figure 8. Localization event of the semantic model.
Figure 8. Localization event of the semantic model. Localization events in the model define
the possible locations of a molecule in a cell. In this example, a PI3K-p110 protein agent is
connected to three different intracellular compartments through three localization events.
In addition to the localization event, a [translocation event] defines the movement of a
molecule from one location to another. Figure 9 features an example where a PI3K-p110
25

molecule moves from the cytosol to plasma membrane. The translocation event connects
[PI3K-p110] to [Cytosol] with a {Origin} relationship, while [Plasma membrane] is linked by a
{Destination} relationship. Taken together, the [localization event] defines all possible
locations of a molecule in a cell, while the [translocation event] allows an instance of the
molecule to change its location during a simulation.
Figure 9. Translocation event of the semantic model.
Figure 9. Translocation event of the semantic model. A translocation event agent connects to
a molecule, an original location and a destination. The translocation event allows proteins
such as PI3K-p110 to move in between locations in a simulation.
3.1.3 Non-covalent interactions as semantic agents
Physical interactions occurring between two molecules via non-covalent forces
(electrostatic interactions, van der Waals interactions, hydrogen bonding) have been
represented by [Non-covalent interaction] event agents in the semantic model. A non-covalent
event has been composed of two sub-events: [binding event A] and [binding event B], and each
binding event considers only one molecule. Figure 10 illustrates a non-covalent event that
models binding between PI3K-p110 and Ras.
26

Figure 10.
Non-covalent interaction event of the semantic model.
Figure 10. Non-covalent interaction event of the semantic model. A non-covalent interaction
event models the binding of two molecules. This example features the interaction between Ras
and PI3K proteins. The event links the binding domains and their corresponding states.
Each sub-event has been linked to not only a molecule and its binding domain but also
two types of states. We have assigned two distinct states to indicate the condition and
consequence of each interaction event. Table 4 shows the two types of states: "states required
for interactions" and "states caused by interactions". Each type further contains 2 subgroups to
differentiate between non-covalent and covalent interactions.
27

Table 4. Two types of states in the semantic model.
1. States required for
interactions
(conformational states)
Non-covalent interaction
[Functional for noncovalent]
[Non-functional for noncovalent]
Covalent interaction
[Functional for covalent]
[Non-functional for
covalent]
2. States caused by
interactions
(binding states or
phosphorylation states)
[Bound]
[Not-bound]
[Phosphorylated]
[Not-phosphorylated]
Table 4. Two types of states in the semantic model. Two major types of states are associated
with interactions, and they represent conformational and functional changes during physical
interactions. The first type is conformational states that are required for either a noninteraction
or a covalent interaction. Each sub-type also contains a positive (Functional) and a
negative (Non-functional) state. The second type is states that are changed as the result or
consequence of either a non-covalent or a covalent interaction. Each sub-type contains two
opposite states; positive (e.g. Bound) and negative (e.g. Not-bound).
Each of eight possible states is represented by an individual semantic agent. The first
type of states, "states required for interactions", have been introduced to indicate the
conformation that is required for an interaction to occur. The [Functional for non-covalent
interaction] state or the [Functional for covalent interaction] state denotes that a domain or site
is in a correct conformation that enables the occurrence of a non-covalent interaction or
covalent interaction respectively. On the other hand, the [Non-functional for non-covalent
interaction] or the [Non-functional for covalent interaction] states imply that a domain or site is
present in such a conformation that prevents its interactions with other cell components.
The second state type "states caused by interactions" designates binding states or
phosphorylation states. For example, a domain with the [Bound] state implies that such a
domain is currently bound to a domain on another molecule, while the [Not-bound] state
indicates the domain is not engaged in a non-covalent interaction. The [Phosphorylated] state
28

indicates a modification residue has been phosphorylated, while the [not-phosphorylated] state
means the residue is not phosphorylated.
In the example of Ras and PI3K-p110 binding, the [Binding event A] is connected to a
Ras protein and its corresponding PI3K-p110 binding domain as well as two different states.
The [Functional for non-covalent interaction] state indicates that the [PI3K-p110 binding
domain] on Ras needs to acquire this conformational state before the interaction can occur. On
the other hand, the [Bound] state indicates that this binding domain will acquire the [Bound]
state as the result of the interaction. Thus, the construction of the non-covalent interaction event
allows us to infer the existence of molecular complexes from the connections of the
corresponding binding molecules. No additional agents are required to represent protein
complexes.
An "allosteric regulation event" describes the situation in which interaction at one site
of a protein causes conformational changes on another part of the molecule that have functional
implications. The SN representation of the allosteric regulation is illustrated in Figure 10
through the example of non-covalent interaction occurring between Ras and P110, leading to
the change of the state of the PI3_kinase domain. The use of allosteric regulation events in SN
modeling allows us to represent cause-effect relationships between proteins domains. The
model for allosteric regulation events is presented in detail in Section 3.15.
3.1.4 Covalent interactions as semantic agents
In contrast to non-covalent interactions, covalent interaction events model chemical
reactions (often catalyzed by enzymes) that transform substrates to products by breaking or
creating covalent chemical bonds. A covalent interaction event has been represented with the
SN environment by three types of sub-events: an [enzyme event] that models the involvement
of an enzyme and its active site; a [substrate event] that represents ligands and their
29

modification sites; and a [product event] that links the corresponding products. As an example,
Figure 11 illustrates a chemical reaction catalyzed by PDK1 kinase which phosphorylates
protein AKT1. The enzyme event has been linked to a prototypical PDK1 enzyme and its
kinase domain as well as the [Functional for covalent interaction] state. Such connections imply
the kinase domain has to be "functional" for this reaction to occur. It is possible that an
[allosteric event] regulates the states of the kinase domain through another ligand binding or a
chemical modification on the enzyme. The first substrate event has linked to a prototypical
AKT1 protein and its phosphorylation site on threonine 308 (T308). The connection with the
[Not-phosphorylated] state indicates that the site is not phosphorylated prior to the covalent
interaction event. The first product event connects the same prototypical AKT1, the same
prototypical [T308] site, and the [phosphorylated] state.
Although the same AKT prototype participates in both the substrate and product events,
a new instance of AKT1 and a new instance of the site T308 are created in the simulation.
Similarly, a new instance of a small molecule ADP is created to represent the second product
from the reaction.
30

Figure 11.
Covalent interaction event of the semantic model.
Figure 11. Covalent interaction event of the semantic model. This covalent interaction
represents the phosphorylation of AKT1 by the enzyme PDK1. The event has been connected to
the substrates (AKT1 and ATP), products (AKT1 and ADP) and the phosphorylation site on
threonine 308 (T308).
3.1.5 Allosteric regulations as semantic agents
A protein can adopt multiple conformations in response to non-covalent ligand binding
or chemical modifications of particular residues. Thus the term "allosteric regulation" refers to
the phenomena of interactions at one site of a molecule causing conformational changes at
another (Alberts et al. 2002). We expanded this definition to include conformational changes
caused by either a ligand binding or a chemical modification on any other subunit in the same
31

protein complex. One example of such complicated allosteric regulation is the conformational
change occurred at the kinase domain on PI3K-p110, when its PI3K-p85 subunit binds to the
cell receptors (Vanhaesebroeck and Waterfield 1999).
The conformational states as introduced in Section 3.1.3 are affected directly by the
binding states and/or the phosphorylation sates through allosteric regulation events. Figure 12
features three different allosteric regulations on the Ras protein (Macaluso et al. 2002): the
binding of GDP inhibits the GTP binding on Ras; the binding of SOS causes Ras to switch to a
GTP-binding conformation; the binding of GTP causes the conformational change on the PI3Kp110
binding domain of Ras. Such change enables Ras molecule to bind to PI3K-p110 protein
and activates the PI3_kinase domain on PI3K-110.
32

Figure 12.
PI3K-p110.
A visualization of allosteric regulations and interactions between Ras and
Figure 12. A visualization of allosteric regulations and interactions between Ras and PI3Kp110.
This figure illustrates allosteric regulation of Ras occurring during its interaction with
PI3K-p110. The blue circles designate molecules and the yellow ones are domains or sites. The
double-headed black arrows denote non-covalent interactions, while the single-headed black
arrows are covalent interactions. The red arrows with either a "plus" sign or a "minus" sign
are used to represent the allosteric regulations. This figure is created to facilitate the
visualization of the underlying biological information, and does not reflect all the connections
among the semantic agents and relationships as implemented in the model. (Abbreviation: RBD
= Ras Binding Domain).
The Ras-PI3K example demonstrates that proteins can act as a logic and adaptive
device, that is affected by the "upstream" interactions (ie. the inputs) and affects the
"downstream" interactions (ie. the outputs). In the developed semantic model, we captured such
protein-logics through the creation of "allosteric regulation events". An allosteric regulation
33

event is composed of [condition] events that contain specific information about the input
signals, and [response] events that consider the outputs. Figure 13 illustrates an example of
such an allosteric regulation model, which represents the cause-effect relationship from the
[GEF] domain to [GDP] and [GTP] binding domains on Ras. A condition event has been
connected to the GEF domain on Ras as well as the [Bound] state, and it implies that the
condition is met only when the GEF domain on Ras has been bound. Consequently, the
allosteric regulation contains two response events. If the condition is satisfied, the first response
causes the GDP binding domain on Ras to become [Non-functional for non-covalent
interaction], and inhibits GDP-binding. The second response switches the GTP binding domain
to [Functional for non-covalent interaction] and promotes GTP-binding.
As stated earlier, the binding and phosphorylation states are caused by upstream
interactions (non-covalent and covalent respectively), and the conformational states are
required for the downstream processes. Therefore, by linking the binding/phosphorylation
states to conformational states through allosteric regulation events, upstream interactions are
connected to downstream events. Such connections among interactions have enabled us to
traverse within cell-signaling pathways in both up and down directions.
34

Figure 13.
Allosteric regulation event of the semantic model.
Figure 13. Allosteric regulation event of the semantic model. An allosteric regulation event
agent is composed of condition and response events. Each condition event considers a domain
and its conditional state (binding or phosphorylation state). After the conditions are met, one
or more response events would change the conformational states on other domains.
35

3.1.6 Cellular responses as semantic agents
The final type of interactions we have implemented within the BioCAD environment is
[cellular response]. A [cellular response] has been represented as an event agent that
corresponds to qualitative cellular behaviors such as cell survival, cell growth and phagosome
formation. Activation or deactivation of certain molecules promotes the occurrence of these
cellular response events.
Figure 14 illustrates how a [protein synthesis] response can be induced if a condition is
satisfied. The condition specifies that a p70 S6-kinase (RPS6KB1) is phosphorylated at the
threonine 389 site (T389). It is possible to include additional conditions into the model, such
that the occurrence of a cellular response is dependent on states of multiple molecules.
Figure 14.
Cellular response of the semantic model.
Figure 14. Cellular response of the semantic model. A cellular response (e.g. protein
synthesis) contains one or more condition events. A condition event connects to a molecule, its
domain involved and a conditional state (binding state or phosphorylation state).
36

In semantic networks, the behavior of any semantic agent can be clearly defined by its
relationships or connections to other agents. Therefore, the construction of the six types of
events (localization, translocation, non-covalent interaction, covalent interaction, allosteric
regulation, and cellular response) enables modeling of the behaviors of molecules in pathways.
We have utilized the semantic model to reconstruct MTB interference in macrophage pathways
and the cause-effect relationships between the intracellular events.
3.2 Reconstruction of macrophage pathways by semantic modeling
3.2.1 Data sources and pathway reconstruction
The majority of the currently available public resources such as pathway and interaction
databases contain un-integrated data on cell-signaling pathways that lack information on
allosteric regulation in participating proteins. Therefore, we have focused our efforts on
extracting pathway information from primary research and review articles and from the STKE
PI3K pathway map (Table 5). The corresponding data has been collected from the literature
manually and incorporated into the macrophage model through the use of SNEC (Semantic
Network Environment for Cell-modeling).
Table 5. Data sources used in macrophage pathway reconstruction.
Primsry research
articles
Review articles
References
Arbibe et al. 2002; Datta et al. 1997; Fratti et al. 2001; Gu et al. 2003;
Hmama et al. 1999; Kane et al. 2002; Kumagai and Dunphy 1991;
Lanzetti et al. 2004; Lin et al. 1999; Murray et al. 2002; Muta and
Takeshige 2001; Shapiro and Harper 1999; Tall et al. 2001; Vieira et
al. 2003
Cantley 2002; Downward 2004; Hayden and Ghosh 2004; Macaluso
et al. 2002; Pavletich 1999; Stenmark and Aasland 1999; Stephens,
Ellson, and Hawkins 2002; Vanhaesebroeck and Waterfield 1999;
Vanhaesebroeck et al. 2001; Velasco-Velazquez et al. 2003; Wurmser,
Gary, and Emr 1999; Wymann, Zvelebil, and Laffargue 2003
37

Pathway
database
STKE (Gough 2002), Connections Map - PI3K pathway (last updated,
July, 2003)
It should be noted, that a pathway diagram presented in literature or public database,
does in principle, represent what may happen if every depicted molecule is expressed in the
correct location, at the correct time and with the correct conformations in a cell. Hence, the
aggregation of multiple pathway diagrams describes some, if not all, possible molecular events
that can potentially occur under a given condition. To utilize such information in semantic
modeling, we decomposed pathway diagrams involving PI3K enzyme families into discrete
pieces of information. We then utilized the defined sets of biological structures and events to
integrate the information into a unified macrophage pathway model. We reconstructed the
pathway model by incorporating the essential components regulating phagocytosis and
phagosome maturation processes in human macrophages. In addition, the model encompasses
many PI3K-related interactions that have implications in other cellular processes including cell
survival, cell growth and cell division.
Thus, Table 6 summarizes the overall SN reconstruction of the macrophage model that
involves 59 prototypical proteins, localized in different intracellular compartments. Appendix
C1 contains a complete protein list. The 59 proteins contain 201 domains and sites. Among
them are annotated Pfam domains that include SH2, SH3, PH and PX, as well as
phosphorylation sites (phosphoserine, phosphothreonine, phosphotyrosine). Lipids such as
PIP3 (main product of PI3K-p110), PI3P (product of Vps34p) and ManLAM (MTB's a
phosphatidylinositol analog) and small molecules, GDP and GTP, also play important roles.
38

Table 6. Biological structure and event prototypes modeled in the macrophage pathways.
Biological structure Sum
Cell 1
Intracellular Compartment 4
Protein 59
Domain and site 201
Lipid 5
Polysaccharide 1
small molecule 2
Biological event Sum
Localization 107
Non-covalent interaction 46
Covalent interaction 17
Allosteric regulation 27
Cellular response 8
Table 6. Biological structure and event prototypes modeled in the macrophage pathways.
This table shows the number of prototypical structures and events modeled in the macrophage
analysis.
In addition to the biological entities, various PI3K- associated signalling events have
been extracted from the literature. Currently the macrophage model reflects 107 localization
events, 46 non-covalent interaction events, 17 covalent interaction events, 27 allosteric
regulation events and 8 cellular responses. Each event has been supported by at least one
literature reference (detailed in Appendices C2-C5).
3.2.2 SN modeling of known MTB interference mechanisms
The integration of information on macrophage signalling involving PI3K provides a
detailed picture of cellular processes. Such integration also allows us to visualize the interaction
networks and to reconstruct scenarios of pathogenic MTB interference with the normal
39

macrophage pathways. The semantic network methodology (as implemented in SNEC) allowed
us to traverse among the various prototypical events or to perform "pathway walk", starting
from the MTB surface molecules, propagating through the activated macrophages cell receptors
to the intermediate intracellular proteins such as PI3Ks, and leading to terminal cellular
responses. Three interaction maps have been generated to represent the pathogenesis pathways
in the model (Figure 15, 16, 17).
The SN macrophage model predicts eight cellular pathways that can be affected by
MTB. Table 7 shows that among the eight cellular responses, four have been previously
reported in experimental studies. The interactions leading to those four previously identified
responses are described in detail in the following sections.
40

Figure 15.
PI3K interaction map part one.
41
Figure 15. PI3K interaction map part one. The interaction map was generated manually by traversing among the different molecules in the
macrophage pathway model through SNEC. The starting points of this graph are the three MTB surface molecules IGHG3, C3 and LPS.
The icons represent semantic agents and are defined in Appendix A. All the arrows represent "derived relationships". The black doubleheaded
arrows are used to connect binding molecules to their non-covalent interaction (double-headed indicates the dual directionality in
the interaction). The black single-headed arrows represent the connections among enzymes, substrates, and products. The blue arrows
connect allosteric regulations to the molecular interactions. The map shows the connections from the three starting molecules on MTB to
the production of PIP3 in macrophages.

Figure 16.
PI3K interaction map part two.
42
Figure 16. PI3K interaction map part two. The map shows the connections from PIP3 to various cellular responses. The red arrow with a
"check" sign represents "promoting" relationship (also a derived relationship). The red arrow with a "cross" sign represents "inhibitory"
relationship.

Figure 17.
PI3K interaction map part three.
43
Figure 17. PI3K interaction map part three. The map shows another parallel pathway from PIP3, and the connections to two additional
cellular responses.

Table 7. Macrophage responses known to be affected by MTB interference.
Cellular responses promoted by MTB
Supporting reference
Actin polymerization and rearrangement Schlesinger et al. 1990
Membrane delivery to plasma membrane Schlesinger et al. 1990
Cellular responses inhibited by MTB
Recruitment of oxidase complex to phagosome
Supporting reference
Phagosome-lysosome fusion Xu et al. 1994
Moura, Modolell, and Mariano1997
Table 7. Known macrophage responses affected by MTB interference. The table shows the
cellular responses that can be promoted or inhibited by MTB infection in macrophages, as
predicted by the pathway model. The second column shows literature that supports the
prediction. The supporting reference has been excluded from model reconstruction.
3.2.2.1 MTB promotes actin polymerization and rearrangement in macrophage
It is known that phagocytosis of bacteria involves two major macrophage responses:
[actin polymerization and rearrangement] and [membrane delivery to plasma membrane]
(Tjelle, Lovdal, and Berg 2000). The pathway model has reconstructed a series of events that
link interactions of MTB surface molecules to activation of [actin polymerization and
rearrangement] pathway. The pathway model (Figure 15) shows how each of three MTB
surface molecules (IGHG3, C3, LPS) can activate a different set of macrophage cell-receptors
(including FCGR1A, [ITGAM + ITGB2], [CD14 + TLR2]). Subsequently, those receptors and
the corresponding adaptor protein (GAB2) can bind to PIK3R1 (PI3K-p85) with their
phosphotyrosine residues to activate PIK3CA enzyme (PI3K-p110). On these interaction maps
different types of semantic agents are depicted as individual icons connected by arrows of
"derived relationships". The "derived relationships" have been inferred from the underlying
semantic relationships that connect the agents as described in the semantic model (Section 3.1).
For simplification, we have condensed several semantic agents and relationships into a single
44

arrow. For instance, the double-headed arrows on Figure 15, which connects IGHG3 and
FCGR1A to a non-covalent interaction, have incorporated their individual binding event agents
as well as domain/site agents.
The semantic model incorporates PIK3CA activation leading to conversion of PIP2 into
PIP3 (Figure 15). A single-headed arrow was used to represent the directionality of a covalent
interaction. For instance, the enzyme PIK3CA is linked by an arrow that goes "into" a covalent
interaction icon, while the substrate PIP2 is connected by an arrow that comes out of the
covalent interaction. The substrate has been associated with its product in the reaction by
another single-headed arrow from PIP2 to PIP3. The pathway model incorporates allosteric
regulation events such as the one involving FCGR1A. The binding of IGHG3 can change
FCGR1A's conformation, enabling its binding with LYN. To visualize such information, the
regulation event is represented by a "positive" allosteric regulation icon, which connects to the
two corresponding non-covalent interactions by two single-headed blue arrows.
The constructed SN model illustrated by Figure 15 incorporates cause-effect
relationships between the interactions of MTB surface molecules and the production of PIP3,
which is an essential molecule interacting with downstream proteins. PSCD3 (a guaninenucleotide
exchange protein for ARF6) is one of many proteins that binds to PIP3 (shown on
Figure 16). The subsequent binding of PSCD3 with ARF6 can change ARF6 into a
conformation that is favourable for GTP binding (Cantley 2002). We have created a "positive"
allosteric regulation event that links the upstream non-covalent interaction (PSCD3 ARF6)
to the downstream one (ARF6 GTP). It has been reported that the binding of GTP on
ARF6 can induce actin polymerization and rearrangement (Cantley 2002). Therefore, we
integrated such information by connecting the response to the previous non-covalent event with
a "promoting relationship", depicted as a red arrow with a check sign in Figure 16.
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The resulting pathway model simulates the details of MTB binding to cell receptors that
can promote [actin polymerization and rearrangement] responses in macrophages.
3.2.2.2 MTB promotes membrane delivery to plasma membrane in macrophage
The ARF6-GTP complex activates the [membrane delivery to the plasma membrane]
process (Stephens, Ellson and Hawkins 2002). We have incorporated the [membrane delivery
to the plasma membrane] response into the previously described PI3K-ARF6 pathway (Figure
16). The cause-effect scenarios are incorporated into the SN model. It predicts that MTB
promotes [actin polymerization and rearrangement] and [membrane delivery to the cell
membrane] responses and, therefore, induces phagocytosis in macrophages. The prediction is
supported by Schlesinger's study (Schlesinger et al. 1990), which has shown that
Mycobacterium tuberculosis triggered the phagocytosis process by binding with CR3 (ITGAM
and ITGB2 receptors) on macrophages.
3.2.2.3 MTB inhibits phagosome-lysosome fusion in macrophage
The interaction map on Figure 17 shows another example of MTB interference to the
PI3K pathways. The pathways start from the PIP3 molecule, and continue with the activation of
RAB5A by the GTP binding and with its interaction to PIK3R4. Subsequently, PIK3R4 can
bind with PIK3C3, the class III PI3K enzyme enabling phosphorylation of PI into PI3P.
Previous studies have shown that PI3P interacts with EEA1 (early endosomes antigen 1), which
is an essential anchoring protein that induces fusion between intracellular compartments
including phagosomes and lysosomes (Stenmark and Aasland 1999; Wurmser, Gary, Emr
1999). Therefore, the pathway model has been reconstructed in a way that the binding of EEA1
to PI3P can induce the [phagosome-lysosome fusion] response.
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As it has been discussed earlier, ManLAM (a phosphatidylinositol analog produced by
MTB) can bind to the active site of PIK3C3 and inhibit the catalytic activity of the enzyme
(Fratti et al. 2001). Figure 17 shows that the interference by MTB has been incorporated into
the model by a non-covalent interaction that links ManLAM to PIK3C3 enzyme. A "negative"
allosteric regulation event connects to the downstream covalent interaction, and it signifies the
production of PI3P is inhibited by the ManLAM binding. Because of the inhibitory effect of the
allosteric regulation, the event has been visualized with an icon that has a "cross" sign. The
model suggested that MTB can stop the [phagosome-lysosome fusion] response that is
normally induced by the PIK3C3 enzyme. This prediction has been supported by Xu's study
(Xu et al. 1994), which documented that MTB restricted the fusion capability of intracellular
compartments in macrophages.
3.2.2.4 MTB inhibits recruitment of oxidase complex to phagosome in macrophage
The study by the Stephens' group (Stephens, Ellson, and Hawkins 2002) has indicated
that PI3P can interact not only with EEA1, but also with NCF4 (p40-phox), which plays an
important role in the formation of the oxidase complex on phagosomes. We have incorporated
a non-covalent interaction between PI3P and NCF4 into the SN model (Figure 17), showing
that the interaction can activate the [recruitment of oxidase complex to phagosome] response in
macrophages. However, the ManLAM competitive binding on PIK3C3 enzyme can reduce the
PI3P production and inhibit the response.
It is known that the [recruitment of oxidase complex to phagosome] is required for the
production of toxic oxygen-derived compounds in the organelle, and this event is accompanied
by increased consumption of oxygen in macrophage cells (Moura, Modolell, and Mariano
1997). Moura's study (Moura, Modolell, and Mariano 1997) has indicated a cell wall lipid from
a MTB-related species, Mycobacterium leprae, down-regulated the oxygen consumption in
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macrophages, supporting the model prediction of [recruitment of oxidase complex to
phagosome] response.
The four cases of the MTB interference that have been discussed above in greater detail
demonstrated that the SN-based pathway model could successfully reconstruct the molecular
events that lead to known macrophage responses. The model shows that MTB can promote
[actin polymerization and rearrangement] and [membrane delivery to plasma membrane]
responses, but inhibits [phagosome-lysosome fusion] and [recruitment of oxidase complex to
phagosome] in macrophages.
3.3 Cause-effect SN simulation of macrophage pathways during infection
The above macrophage pathway model represents the qualitative scenarios of MTB
interference in the host pathways. We have implemented an SN-simulation program to further
investigate the dynamic behavior of molecules in the pathways. We allow each individual
molecular "instance" to interact with other molecules, change its conformation, and move
between different locations in a macrophage cell. In the corresponding simulation, every
molecule has been represented by an individual agent, while every instance of a molecular
interaction is represented as an individual event agent. An event agent has been connected to all
the participating entities to record "what", "how" and "when" an interaction occurred in a
simulation. The simulator provides a traceable "trajectory" of all the events that happen to
every molecule. Such an event history allows a detailed analysis of simulated molecular
interactions.
The interactions among IGHG3, FCGR1A, Lyn and Gab2 have been studied in detail
by Gu et al. (2003), providing a simple and well-characterized test for the simulation (Figure
18). The Fc-gamma receptor (FCGR1A) has a binding domain for immunoglobulin gamma 3
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(IGHG3). When FCGR1A binds to IGHG3, the Lyn binding domain is activated and thus
enables its binding to Lyn kinase. The subsequent binding between FCGR1A and Lyn activates
the kinase active site, [Protein_kinase_Tyr], on Lyn, and allows the enzyme to phosphorylate
Gab2 on a phosphotyrosine site (pYxxM).
Figure 18.
Interactions between Fc-gamma receptor and Lyn kinase.
Figure 18. Interactions between Fc-gamma receptor and Lyn kinase. This figure illustrates
the possible interactions and allosteric regulations among the 4 different molecules in the
simulation (IGHG3, FCGR1A, Lyn, Gab2). The figure uses the same visualization schema as in
Figure 12 for Ras and PI3K interactions.
To simulate the above interactions, we created an instance of the macrophage cell,
composed of four compartments: extracellular space, plasma membrane, cytosol, and nucleus.
Different instances of molecules were produced and localized within distinct cellular
compartments at the start (time=0). Two instances of IgG molecules (IGHG3) were present in
the extracellular space, and two instances of Fcγ receptors were located in the plasma
49

membrane (Figure 19). The cytosol contained two copies of Lyn and Gab2. There were no
events occurred at the beginning of the simulation.
Figure 19. A SN-based simulator, before a simulation run (time =0).
Figure 19. A SN-based simulator, before a simulation run (time =0). An instance of a
macrophage cell (human) is composed of four compartments; extracellular space, plasma
membrane, cytosol and nucleus. There are three operations for each compartment, and they
are activated by the three action buttons respectively: "Non-covalent int.", "Covalent int." and
"Translocation." Operations are performed according to the simulation steps as described in
Figure 21. The combo boxes located at the top is used to increment the time. Before the
simulation run, there was no event occurred as shown in the reports at the bottom of the
screen.
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Figure 20.
A SN-based simulator, at the end of a simulation run (time=6).
Figure 20. A SN-based simulator, at the end of a simulation run (time=6). The figure shows
the simulation outcomes at time 6. The molecules have changed their original locations, and
many events have accumulated. There were 11 translocation events, 4 allosteric regulation
events, 4 non-covalent interactions events, and 1 covalent interaction events.
The SN-simulation was executed by running a simulation cycle for each unit of time.
Figure 21 illustrates that one simulation cycle consists of three operation steps.
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Figure 21.
The sequence of simulation steps.
Figure 21. The sequence of simulation steps. This figure illustrates the simulation cycles
involve three operation steps: "non-covalent interaction", "covalent interaction" and
"translocation". Each operation is executed for an individual location in the specified order.
After the completion of the final step, the time (or step) is incremented by one, and the same
cycle repeats.
In the first step, an operation searches for one pair of molecules that has the potential to
interact non-covalently in one location (by checking the non-covalent interactions previously
52

specified in the pathway model). If there are multiple pairs of interacting molecules, the
operation chooses the first molecule randomly and selects its partner from all the interacting
molecules that are present in same location. After a pair has been determined, an event agent
links both of the molecule instances. We record the time when the interaction occurs by linking
the event agent with a "time stamp" agent. Table 8 shows that four non-covalent interactions
have occurred in the simulation at time 2, 3, 4 and 5 respectively.
After the "non-covalent interaction" operation has been executed in the plasma
membrane, cytosol, and nucleus, the simulation program searches for a "covalent interaction".
The operation randomly picks an enzyme whose substrates are present in the same location.
After an enzyme has been determined, a substrate is randomly chosen (if there was more than
one substrate) and the corresponding product is created. The covalent interaction operation is
repeated in plasma membrane, cytosol, and nucleus. Table 9 shows that one covalent
interaction between Lyn and Gab2 occurred at plasma membrane at time 6 in the simulation.
The occurrence for both non-covalent interactions and covalent interactions require not
only the presence of molecules, but also the correct conformational states of those molecules,
as specified by the pathway model. Therefore, a non-covalent interaction requires both
molecules to have "functional" binding domains, while a covalent interaction requires an
enzyme with a "functional" active site. The occurrence of either interaction can trigger
allosteric regulations and changes the conformational states of the participating molecules.
Such state changes allow molecules to adopt new functions and participate in different
interactions. For instance, the non-covalent interaction between IGHG3 and FCGR1A occurred
at time 2 has caused an allosteric regulation, which switched the conformational state of the
Lyn binding site on FCGR1A from the "non-functional" to "functional" state. After the Lyn
molecule has been translocated from cytosol to plasma membrane at time 3, the activated Lyn
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inding site on FCGR1A enabled the receptor to bind with Lyn at Time 4. Table 10 shows that
there were a total of 4 allosteric regulation events in the simulation.
As the final step in the simulation cycle, molecules change their locations through
translocation events. One translocation can occur and move one molecule from each location.
The operation randomly picks a molecule that has the ability to move by checking its
localization events on the prototypical molecule. Once a molecule has been determined, a
destination is randomly chosen if there is more than one location where the molecule can move
to. For example, Table 11 shows that a Gab2 molecule has been translocated from cytosol to
plasma membrane at Time 1. However, the FCGR1A molecule has never moved during the
simulation because it can only be localized at plasma membrane as restricted by its localization
event. The operation for translocations is executed for each location in the order of extracellular
space, plasma membrane, cytosol, and nucleus. After the translocation events, the time
(representing the step) is incremented by one unit, and the simulation cycle is repeated.
At the end of the simulation (time = 6), most molecules have changed their locations
and many events have accumulated (4 non-covalent interactions, 1 covalent interaction, 4
allosteric regulation, and 11 translocations as shown in Figure 20). Those events have
demonstrated how the initial translocation of IGHG3 molecules from extracellular space to
plasma membrane "induced" the subsequent series of events that eventually led to the
phosphorylation of Gab2 molecule to Gab2-p.
It is possible to simulate different biological scenarios by modifying the initial
populations and distribution of molecules in each location. At the current stage, the simulator
enables us to “play” different macrophage pathways and observes the actions of the molecules.
It captures the stochastic behaviors of interactions to through the use of random operations. We
54

anticipate future improvement of the SN-simulator will enhance our ability to predict and
validate MTB interference in macrophages.
55

Table 8. Non-covalent interaction events in the simulation.
Time Molecule A Domain A Molecule B Domain B Location
Time 2 FCGR1A IG IGHG3 Fc-gamma receptor binding domain Plasma Membrane
Time 3 FCGR1A IG IGHG3 Fc-gamma receptor binding domain Plasma Membrane
Time 4 FCGR1A Lyn binding LYN FcgR binding Plasma Membrane
Time 5 LYN FcgR binding FCGR1A Lyn binding Plasma Membrane
Table 8. Non-covalent interaction events in the simulation. This table shows the non-covalent interaction events that occurred in the
simulation, sorted by the time when the event occurred. Each event has been linked to other relevant agents including molecules, domains
and states as well the location where the event happened. Column 1: time when the interaction occurred in the simulation. Column 2: name
of the binding molecule A. Column 3: domain of molecule A involved in the interaction. Column 4: name of the binding molecule B. Column
5: domain of molecule B involved in the interaction. Column 6: location where the interaction occurred.
56
Table 9. Covalent interaction events in the simulation.
Time Enzyme Enzyme's domain Substrate Site State Product Site State Location
Time 6 LYN PROTEIN_KINASE_TYR GAB2 pYxxM Not-phosphorylated GAB2-p pYxxM Phosphorylated Plasma Membrane
Table 9. Covalent interaction events in the simulation. The table shows the covalent interaction events that occurred in the simulation.
Column 1: time when the interaction occurred. Column 2: name of the enzyme. Column 3: the active site or catalytic domain of the enzyme
involved. Column 4: name of the substrate. Column 5: modification site of the substrate. Column 6: phosphorylation state before the
covalent interaction event. Column 7: name of the product. Column 8: modification site of the product. Column 9: phosphorylation state
after the covalent interaction. Column 10: location where the interaction occurred.

Table 10. Allosteric regulation events in the simulation.
Time Molecule affected Domain involved as the condition State satisfied Domain affected as the response State changed to Location
Time 2 FCGR1A IG Bound Lyn binding Func. for non-cov. Plasma Membrane
Time 3 FCGR1A IG Bound Lyn binding Func. for non-cov. Plasma Membrane
Time 4 LYN FcgR binding Bound PROTEIN_KINASE_TYR Func. for cov. Plasma Membrane
Time 5 LYN FcgR binding Bound PROTEIN_KINASE_TYR Func. for cov. Plasma Membrane
Table 10. Allosteric regulation events in the simulation. The table shows the allosteric regulation events that occurred in the simulation.
Column 1: time when the event occurred. Column 2: molecule affected by the allosteric regulation responses. Column 3: domain involved in
the allosteric condition. Column 4: binding state that was satisfied for the condition. Column 5: domain affected by the allosteric response.
Column 6: conformational state changed by the response. Column 7: location where the allosteric regulation occurred.
57
Table 11. Translocation events in the simulation.
Time Molecule moved From To
Time 1 IGHG3 Extracellular space Plasma Membrane
Time 1 GAB2 Cytosol Plasma Membrane
Time 2 IGHG3 Extracellular space Plasma Membrane
Time 2 GAB2 Plasma Membrane Cytosol
Time 2 GAB2 Cytosol Plasma Membrane
Time 3 GAB2 Plasma Membrane Cytosol
Time 3 LYN Cytosol Plasma Membrane
Time 4 LYN Cytosol Plasma Membrane
Time 5 GAB2 Cytosol Plasma Membrane

Time 6 GAB2-p Plasma Membrane Cytosol
Time 6 GAB2 Cytosol Plasma Membrane
Table 11. Translocation events in the simulation. The table shows the translocation events occurred in the simulation. Column 1: time
when the translocation occurred. Column 2: molecule that was translocated. Column 3: the original location of the molecule. Column 4: the
destination of the molecule.
58

CHAPTER 4
DISCUSSION
4.1 Use of SN modeling for predicting unknown macrophage responses to
infection
The semantic modeling studies did not only allow us detailed cause-affect
reconstruction of several known processes by which Mycobacterium tuberculosis interferes
with human macrophages, but also predicted several cellular responses that have not been
previously recognized (Table 12). Namely, as it has been discussed in Section 3.2.2,
interactions between MTB surface molecules and macrophage receptors can activate the class I
PI3K enzyme and induce the production of PIP3. Studies have shown that PIP3 regulates many
other cellular processes in addition to phagocytosis and phagosome maturation (Cantley 2002;
Wymann, Zvelebil, and Laffargue 2003). We have incorporated additional PIP3-related
interactions into the model, which identified four other macrophage responses that can be
affected by MTB. The details of the SN-reconstructions of MTB interference scenarios are
discussed in the following sections.
Table 12. Unknown macrophage responses affected by MTB interference.
Cellular responses promoted by MTB
Cell survival
Cell cycle entry - S phase
Protein synthesis
Intracellular glucose uptake
Table 12. Unknown macrophage responses affected by MTB interference. The table shows
the cellular responses that can be promoted by MTB infection in macrophages, as predicted by
the pathway model.
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4.1.1.1 MTB increases intracellular glucose uptake in macrophage
The developed SN-based pathway model illustrated by Figure 16 includes a noncovalent
interaction between PIP3 and SLC2A4 (glucose transporter type 4). This interaction
recruits SLC2A4 to the plasma membrane and allows the protein to transport extracellular
glucose into the cytosol (Wymann, Zvelebil, and Laffargue 2003). The model encompasses
such information by implementing a cellular response event, [intracellular glucose uptake],
which activation depends on the above non-covalent interaction. When we incorporated these
events into the semantic network, we are able to reconstruct the cause-effect relationships from
PI3K activation by the MTB factors, production of PIP3, and to the increase of [intracellular
glucose uptake] in macrophages (Figure 15 and 16).
4.1.1.2 MTB increases the rate of protein synthesis in macrophage
An essential PIP3-interacting protein is PDPK1, a kinase that phosphorylates many
substrates including RPS6KB1 (ribosomal protein S6 kinase) (Cantley 2002; Wymann,
Zvelebil, and Laffargue 2003). The model incorporates a non-covalent interaction that links
PIP3 to PDPK1 (Figure 16), and also includes a downstream covalent interaction -
phosphorylation of RPS6KB1 by PDPK1. RPS6KB1 kinase becomes active when
phosphorylated by PDPK1, and the activated RPS6KB1 phosphorylates the S6 ribosomal
proteins and increases the rate of protein synthesis (Cantley 2002). Such information has been
integrated into the macrophage model by a [protein synthesis] response, which links to the
previous covalent interaction event with a "promoting relationship". The SN environment
allows us to analyze the outcomes of MTB interference on the PI3K pathways, and predicts that
the rate of protein synthesis in macrophages is increased in response to RPS6KB1 activation.
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4.1.1.3 MTB promotes cell division in macrophage
The previous experimental studies demonstrated that PDPK1 protein can phosphorylate
AKT1, a kinase which regulates cell division (Cantley 2002). After activation, AKT1 in turn
phosphorylates downstream proteins such as GSK3B (Wymann, Zvelebil, and Laffargue 2003).
GSK3B can phosphorylate CCND1 (Cyclin D1), but only when GSK3B is un-phosphorylated
(Cantley 2002). After CCND1 is phosphorylated, it is targeted to the proteasome for
degradation and cell cycle entry is inhibited. Therefore, the phosphorylation of GSK3B by
AKT1 deactivates the catalytic activity of GSK3B, stops the degradation of CCND1 and
promotes cell cycle entry.
The above information demonstrates an example of double-inhibition, which has been
modeled by two covalent interactions in the SN. The first is phosphorylation of GSK3B by
AKT1 (designated as"AKT1-p" on Figure 16). The second is phosphorylation of CCND1 by
GSK3B. To indicate that the first interaction inhibits the second one, we created a "negative"
allosteric regulation event that links the two chemical reactions. Subsequently, the second
covalent interaction inhibits the [Cell cycle entry - S phase] response. The "inhibitory"
relationship is represented by a red arrow with a cross sign in Figure 16. When such complex
series of events have been represented by the semantic agents and relationships in the SN, we
observe that MTB can induce the phosphorylation of GSK3B by AKT1 and promote cell cycle
entry response in macrophages.
4.1.1.4 MTB promotes survival of macrophage
The pathway model accounts for double-inhibition involving BAD and BCL2 (Figure
16). BCL2 promotes cell survival, but the action is prevented by binding with BAD (Cantley
2002; Wymann, Zvelebil, Laffargue 2003). The BAD-BCL2 binding is inhibited by
phosphorylation of BAD by AKT1 (Datta et al. 1997). The model has incorporated the double-
61

inhibition relationships by a "negative" allosteric regulation event, which links the upstream
covalent interaction (phosphorylation of BAD by AKT1-p) to the downstream non-covalent
interaction between BAD and BCL2 (Figure 16). The above non-covalent event has been
connected to the [cell survival] response by an "inhibitory" relationship in the SN. The model
reconstructed another MTB interference scenario, accounting for the activation of AKT1 by
PDPK1, the subsequent phosphorylation of BAD by AKT1, and the occurrence of the [cell
survival] response in macrophages (Figure 16).
The semantic networks enable us to integrate individual molecular interactions and to
reconstruct MTB-interference on the macrophage PI3K-pathways, leading to the activation of
[intracellular glucose uptake], [protein synthesis], [cell cycle entry - S phase] and [cell survival]
responses, none of which are described in the current literature on MTB infection in
macrophages. Because a successful parasite ensures the growth and survival of the host to
sustain its nutrients, these responses in macrophages should be beneficial for the survival of
Mycobacterium tuberculosis. Activating cell division signals in macrophage may also help the
migration and spread of the bacteria to progenies of the host cell. These four cellular responses
will be validated by biological experiments in MTB-infected macrophages.
The pathway model predicts several connections between proteins. For example, Gu et
al. (2003) has observed the phosphorylation of the GAB2 protein by the LYN kinase, and the
subsequent downstream-activation of the class I PI3K by phosphorylated GAB2. In Gu's study,
the kinase activity of LYN was activated by Fcγ-receptor (FCGR1A) through non-covalent
binding. An independent observation from Velasco-Velazquez (2003) suggested that the LYN
can be activated by the CR3 receptor beta subunit, CD18 (ITGB2). Figure 15 illustrates that the
model has integrated the two pieces of information on LYN regulations. As the result, the
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model suggested the possibility that both Fcγ-receptor and CR3 receptor can activate LYN,
which phosphorylates GAB2 and activates class I PI3K.
CD14 and the class I PI3K (PIK3CA) are predicted to interact via TLR2. Hmama et al.
(1999) suggested a model in which PI3K is activated indirectly by the CD14 receptor. However,
the components that link the two proteins were not known. A more recent study by Muta and
Takeshige (2001) has observed a direct interaction between CD14 and TLR2. Aribe et al. (2000)
demonstrated that a phosphotyrosine site on TLR2 can bind to the p85 subunit of PI3K,
activating PI3K catalytic activity. By combining these two pieces of evidence, the pathway
model reconstructs the scenario that CD14 receptor can bind to TLR2 that activates PI3K
(Figure 15).
The two examples demonstrate that the pathway model not only integrate and interpret
current biological observations but can also formalize new hypotheses. Several assumptions
were made when the model connected the individual protein-protein interactions into pathways.
For instance, the model assumed the co-expression of the molecules and their corresponding
activation states in the macrophage cell during MTB infection. Those assumptions will be
validated by simulations and experiments such as gene arrays, protein expression and
phosphorylation profiles. The pathway model can guide those experiments, and the
experimental results will assist in model validation.
4.2 Advantages of using semantic networks for pathway modeling
The results presented above illustrate that SN-based reconstruction of molecular
pathways can predict previously unrecognized scenarios, linking the molecular events to the
cellular responses. The developed semantic model for cell signalling has addressed several
limitations of the conventional diagram-based pathway representation.
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4.2.1 Specify the spatial organization of molecules
The semantic model has specified the hierarchical relationships among the different
biological structures; from cells to compartments, from compartment to molecules and from
molecules to domains/sites. The hierarchy between intracellular compartments and molecules
allowed us to define the spatial organization of molecules in a cell through the localization
events and the translocation events. Therefore, the model represents "where" interactions occur.
4.2.2 Model proteins as logical, integrating and adaptive devices
The organization of domains and sites and the use of allosteric regulation events have
enabled us to model the cause-effect relationships between structures and functions in
macromolecules. Within SN, proteins have been implemented as integrating and logical
devices, and their conformational states can be switched by the occurrence of non-covalent
and/or covalent interaction events. Therefore, the model allowed us to represent the conditions
and consequences of upstream interactions and downstream interactions in pathways, and the
information of "what", "how" and "when" interactions occur has been specified.
4.2.3 Reduce the need for labels and descriptions
In the developed semantic model, conventional descriptions of proteins such as
"enzyme", "activator" or "inhibitor" have been represented by events. For example, in the
model a protein has an "enzyme" role when 1) the protein is participated in a "covalent
interaction event", 2) the presence of a "functional" catalytic domain on the protein is required
for the occurrence of the event, and 3) the protein itself is not modified after the event.
Similarly, a protein A "activates" a protein B, when a non-covalent interaction event from
protein A can turn on the "functional" state of a domain/site on protein B. Thus, the "role" or
"function" of a protein has been effectively represented by the events it participated in.
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Therefore, the model reduces the need for descriptive labels that are often ambiguous in
conventional pathway representation.
4.2.4 Provide a direct communication from models to simulations
In the developed model, the possible behaviors of molecules have been defined by the
various interaction events they involved. Adding or modifying interactions in the pathway
model changes the behaviors of molecular instances in the simulation. The semantic network
environment has established a connection from the model to the simulation program where the
actions of individual molecules can be observed and tested under different scenarios.
The semantic networks have several other technical advantages over static pathway
representation. Those advantages include faster querying capabilities, convenient data addition
and more effective integration of information. With in the SN framework, a given concept
(such as [protein]) is represented by one prototypical agent. Any additional information about
that concept can be represented by other agents and relationships connected to the same
prototype. This construct differs from a relational database where information is stored in tables.
To link tables to one and another, unique identifiers or primary keys are required. The
duplication of primary keys in tables creates data redundancy, introducing issues on data
maintenance and consistency. Semantic networks minimize such issues by reusing existing
agents, and therefore, the databases have smaller size and more functional organization
compared to the relational databases.
The connectivity of semantic agents in the SN also allows very rapid execution of even
very complicated queries as they are not compromised by a large number of tables that are
often present in relational databases. In contrast, within the SN environment a query is
composed by operations which are semantic agents themselves and can be manipulated in the
same way as other agents. Representing queries as semantic agents allows the information to be
65

stored and integrated, and even re-used in a different context. On the other hand a query such as
a SQL query is written in the form of static computer scripts, and the information on the data
interpretation is difficult to be utilized in a relational database.
Semantic databases further distinguish themselves by their capability to incorporate new
data types through flexible creation of prototypes. Adding new prototypes does not affect the
existing data models. Therefore, a single semantic database can support multiple semantic
models that represent information in different knowledge domains.
4.3 Future directions
The developed semantic model of human macrophage will be expanded to include more
metabolic pathways, by incorporating other types of metabolic events including methylation,
acetylation and glycosylation, in addition to the current phosphorylation and dephosphorylation
processes. To model gene regulation, the non-covalent interaction events will
be expanded for the binding between individual transcription factors and their corresponding
gene regulatory sites. The covalent interaction event can represent transcription processes that
lead to the production of mRNAs as well as translations that produce proteins. The use of noncovalent
interactions will also help us to effectively represent large transcription complex that
may involve more than one hundred proteins, assembled in various orders.
The gene regulation logic will be modeled by leveraging the current allosteric
regulation events. We anticipate representing a gene locus by a macromolecular agent that is
composed of transcription factor binding sites, promoter sites, exons and introns. An event that
is analogous to the allosteric regulation will link the specific transcription factor binding events
to the activation/deactivation of promoter sites, which will then direct the production of specific
transcripts.
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During the pathway reconstruction, we have extracted data mainly from the literature.
Because the underlying semantic model is compatible with most public available pathway and
interaction databases, the number of biological entities and interactions in the pathways can be
increased through automatic data integration from those sources. However, information on
allosteric regulations is still presented primarily in the literature, and therefore, extraction of
such information will rely on both text-mining techniques and expert curation.
It is also possible to predict protein interactions in the macrophage pathways based on
domain-domain interactions, which has been considered in the model. It has been shown that
putative protein-protein interactions can be inferred from domain-domain or domain-motif
rules (Ng, Zhang, Tan 2003; Obenauer and Yaffe 2004). Such prediction can complement
experimental determined but limited protein interaction data. Thus, database including
InterDom (Ng et al. 2003), iPfam (2004) and Scansite (Obenauer, Cantley, and Yaffe 2003)
have compiled a list of experimental or computational derived domain-domain and domainmotif
interactions. Those resources will soon be utilized to predict protein interactions not only
in a single organism but also between organism such as MTB and macrophages (based on
proteins with common interacting domains.)
The available gene and protein expression data on macrophages will also be
incorporated into the model to determine "active" or "shortest" paths in the interaction network.
Previous study has shown that active sub-networks can be identified by overlaying gene
expression profiles with protein-protein interaction data (Ideker et al. 2002). Within a cell, there
are many paths that can activate a downstream protein. The "length" of each path varies as it
consists of different number of nodes. Therefore, we anticipate that the shortest path (the one
with the least number of nodes that require activation), is more likely to be "utilized" than a
longer route when the cell receives a stimulus. The integration of gene/protein expression data
67

with the pathway model will allow us to estimate the protein activation states for "pathway
filtering".
4.3.1 A collaborative pathway modeling environment
The developed SNEC website will soon be available for the research community, when
the underlying biological model is improved and the necessary computer resources are
allocated. SNEC provides a collaborative environment where different researchers can share
and exchange their knowledge on intracellular pathways in different organisms. The current
web implementation has already allowed different users to customize biological entities and
interactions, and user-specific changes have been represented effectively by creating semantic
agents in the database.
It is essential to reuse and exchange pathway information between researchers. The
developed macrophage model represents a particular interpretation of the entire pathways in the
cells. Users will be able to reuse the interactions as parts of their own pathway models, or they
can copy and modify each component to reflect their own interpretation. Each interaction can
then be analyzed for its "trust" or "support" by other researchers. The analysis will help us to
determine the canonical part of the pathways as well as parts that are still under debates and
contradiction. The research community can focus on incomplete pathways, forming new
hypotheses and designing new experiments.
The exchange and translation of complicated pathway information rely on a good
visualization method. The PI3K-interaction maps on Figure 15-17 have attempted to visualize
different types of interactions in pathways. Cook, Farley, and Tapscott (2001) and Kitano (2002)
discussed several advanced visualization techniques, and some of them have been adopted by
Figure 12 and Figure 18. Those visualization techniques will be utilized to implement
68

automatic graphing tools for both 2-D maps of the pathways and 3-D animations of events in
simulation.
We anticipate that the pathway modeling environment will provide not only advanced
visualization tools by also a simulation program for pathway testing. The current SN-simulator
will be improved in four aspects. Firstly, various quantitative factors will be considered in the
simulation. For instance, binding affinity that is associated with non-covalent events will affect
the probability and the duration on the binding of molecules. Reaction kinetics, associated with
covalent events, will determine the rate of production. Both interaction rates will determine the
time each interaction takes and how many events can accumulate during each time unit.
Secondly, the population and distribution of molecules in each intracellular compartment will
be supported by experimental data. For instance, gene expression data from microarrays
supports the relative abundance of transcripts, and protein expression data provide the relative
abundance of proteins. Computer algorithms such as PSORT (Nakai and Horton 1999) can also
assist in predicting the localization of proteins. Thirdly, the proximity of molecules will be
enhanced. Current, proximity has been represented by creating intracellular compartments,
which can be further divided into smaller sub-locations. Increasing the number of locations and
reducing the size of each location will improve the approximation on localization of molecules.
The proximity can also be estimated through molecular complexes. We anticipate that a
molecule will have a higher probability to interact with its subunits in the same complex, than
other molecules outside the complex.
Finally, the cellular response events will be implemented into the SN simulator, in a
way that the events are triggered by the accumulation of different molecular interactions. Such
construct allows us to represent the transition from quantitative to qualitative behaviors in a cell.
69

Cellular responses will serve as the final simulation outcomes for measuring the effects of
extracellular stimuli including drugs.
4.3.2 A potential tool for in silico drug discovery
The drug discovery process involves many stages of development, including drug target
identification, drug target validation, drug design, drug identification, and drug testing. A good
drug target validation process should identify the functional roles of a target in a cell or an
organism and establish its cause-effect relationships to a disease (Smith 2003). Currently, such
information is obtained experimentally in vitro and in vivo through gene knockouts, antisense
technology, RNA interference (RNAi), and antibodies that target and inhibit the protein.
However, experimental methods are both time and resource consuming. Analogous to "in
silico" drug design approach that has assisted and reduced the cost in conventional drug
screening, we anticipate that in silico drug target validation can help and fasten the drug
discovery process.
Semantic networks provide a suitable environment for drug target validation because
they can determine the function of proteins in the context of interaction networks. Proteinprotein
interaction network has scale-free property that there are a few but essential proteins
with many connections, while most of the other proteins only have a few linkages (Barabasi
and Oltvai 2004). Thus, targeting a highly-connected protein should be disruptive to the
pathogen, and such target can be identified from the protein-interaction networks already
established in SN.
In addition, semantic networks can determine spliced variants and proteins that are able
to compensate the disruption of the pathogenic target. In SN, spliced variants are characterized
by their connections to the common gene agents. Proteins with similar functions can be
identified from their common domains and functional sites. If the protein is too robust to attack,
70

another strategy is to target pathogenic proteins that directly interact with host proteins.
Combining the protein interaction networks between the pathogen and the host will provide a
list of such cross-interacting proteins.
In addition to drug target validation, the SN-simulator can be used for in silico drug
testing. Most drugs interact with the active sites or binding domain of host proteins. Thus crossreactions
can occur on proteins that share the same drug-binding domains. Such drug-specific
interactions can be incorporated into the pathway model and the effect of drugs can be
simulated. The simulation will provide a list of molecular events and cellular responses that
occur as the result of such drug interference.
71

CHAPTER 5
CONCLUSION
The application of semantic networks has enabled us to develop a new biological
language (semantic model) for reconstructing Mycobacterium tuberculosis interference
strategies in macrophage pathways. The semantic model has several advantages in
characterizing complex interactions between macromolecules in cell signalling pathways, and it
addressed the limitations of the conventional diagram-based pathway representation. These
advantages include specifying the spatial organization of molecules; modeling proteins as
logical, integrating and adaptive devices; reducing the need for labels and descriptions;
providing a direct communication from models to simulations.
The unique features of the semantic model allowed us to effectively reconstruct the
cause-effect relationships of MTB interference in human macrophage pathways. The current
knowledge on PI3K interactions and their corresponding cellular responses have been extracted
from the literate and integrated into the macrophage pathway model. The SN representation
enabled the traverse within the pathways, starting from the MTB surface molecules, to
activated receptors, downstream proteins and occurring cellular responses in macrophages.
The pathway model has predicted that MTB factors can promote [actin polymerization
and rearrangement], [membrane delivery to plasma membrane], [cell survival], [cell cycle entry
- S phase], [protein synthesis] and [intracellular glucose uptake] responses in macrophages. On
the other hand, [recruitment of oxidase complex to phagosome] and [phagosome-lysosome
fusion] are inhibited by MTB. Some of the predicted responses have been supported by
previous studies, while the others have not yet been appreciated in current literature on MTB
72

infection in macrophages. New experiments will be formalized based on the pathway models to
validate the responses further.
The web-based application, Semantic Network Environment for Cell-modeling (SNEC),
has implemented the semantic model for effective pathway building and customization. SNEC
facilitates collaborative pathway studies in macrophages as well as other cellular systems. To
explore the dynamics of semantic networks, we have developed a SN-based cell simulator,
which captures the stochastic behaviors in a cell and produce a detailed history of events that
can be traced and analyzed afterward.
In the future, we anticipate enhancing the pathway model and simulation, utilizing the
SN environment for in silico drug discovery, and assisting the development of new therapeutic
strategies against MTB infections in macrophages.
73

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81

Appendix A - Definitions of the icons.
APPENDICES
82

83

Appendix B - Semantic Network Environment for Cell-modeling (SNEC)
We have implemented a website, Semantic Network Environment for Cell-modeling
(SNEC), using the VK application development environment. Through this interactive website,
users can unitize the developed semantic model and resources in the BioCAD database to build
intracellular pathways collaboratively. SNEC allows data from external sources such as
literature to be incorporated into the pathway models (including data on localization of proteins,
organization of domains and sites in macromolecules, allosteric regulations, conditions and
effects of interactions, and promotion and inhibition of cellular response). The unique features
of SENC are described in following sections.
Utilize current information to customize biological structures
SNEC allows users to create different biological structures including cell, intracellular
compartments, proteins, domains and small molecules, based on the annotated biological
concepts in the database. For example, as a first step to create a user-specific pathway model, a
user searches for existing cell prototypes such as human macrophages in the BioCAD database,
and a user-specific instance of the prototypical cell is created. SNEC also creates three
intracellular compartments, which are plasma membrane, cytosol and nucleus for the user-cell
by default, and additional compartments can be added by the user later.
The user can search the protein database in BioCAD by protein synonym or accession
number, and an example of PIK3CA is shown on Appendix B1. When the user finds and adds a
protein, a user-specific instance of the prototypical protein is created in the database. The
hyperlink on the user-protein directs the user to a protein detail page (Appendix B2), which
contains annotations that have been previously incorporated into BioCAD. Those annotations
include gene locus name, protein synonyms, accession number (RefSeq ID, GenBank ID),
descriptions on protein functions and Gene Ontology classifications.
84

When the user-protein is created, instances of any Pfam domains, which are
components of the prototypical protein, are also created and connected to the protein instance.
Appendix B4 shows that the user can add additional domains and sites from sources such as
Prosite, Prefile, Prints, Prodom, Profile, Prosite, Smart, SSF and TigrFams. The user also has
the option to create a user-defined domain or a phosphorylation site.
Define the behavior of molecules by creating different types of events
After the user has added the cells, proteins or any other biological components, different
types of events can be created. The navigation buttons on the left panel of the website allows
users to move to detailed web pages for events such as localizations. Appendix 3 shows that
localization events are defined by selecting an intracellular compartment in a user-specific cell.
The user can also create allosteric regulations on proteins by adding an allosteric
regulation event and its corresponding condition and responses events (Appendix B5, B6 and
B7). Non-covalent and covalent interactions are defined by specifying the participating
molecules, domains and sites and their states involved (Appendix B8, B9 and B10). In addition,
the user can create or re-use a cellular response event in the database, and define the occurrence
of a cellular response by adding a condition, which considers a particular molecule and its
required states (Appendix B11).
Analyze and traverse interactions upstream and downstream
One of the most distinguishable features in SNEC is the connection between molecular
interactions (non-covalent and covalent interactions) and the conformational and functional
changes (allosteric regulations) on the participating molecules caused by those interactions. For
every allosteric regulation, SNEC identifies the "upstream" interactions that can satisfy the
allosteric conditions, and "downstream" interactions that are effected by the allosteric responses.
85

For instance, Appendix B5 shows that one of the many allosteric regulations on PIK3CA
(PI3K-p110) is the activation of "PI3_PI4_kinase" domain when the "PI3K_RBD" is bound.
The hyperlink of the condition event directs the user to a detail page (Appendix B6), which
identifies upstream interactions that can satisfy this condition. In this example, the condition is
satisfied by the non-covalent interaction between HRAS and PIK3CA. It should be noted that
the molecular interactions and the allosteric regulations are connected indirectly through their
common domains and states. Such an indirect connection allows the information on the
interaction and the information on the allosteric regulation to be specified independently. It
facilitates the reduction and storage of complex knowledge into several individual but
interconnected pieces of information. Dynamic operations were implemented to search and
report back for any molecular interaction that can satisfy or be affected by the allosteric
regulations, and thus allow the information to be integrated.
The allosteric response detail page (Appendix B7) shows the molecular interactions that
are affected (enabled or disabled) by the response. In this example, the switch of the
conformation state to "functional for covalent interaction" on "PI3_PI4_kinase" domain enables
the covalent interaction between PIK3CA and PIP2 molecule. The hyperlink on the covalent
interaction brings up a detail page for the covalent interaction (Appendix B10).
On both the non-covalent interaction and the covalent interaction pages (Appendix 9
and 10), the dynamic reports show the allosteric conditions that either activate or deactivate the
molecular interaction, and the allosteric responses that are caused by the interaction. For
example, the occurrence of the non-covalent interaction between Hras and PIK3CA (Appendix
B9) requires the "SMALL_GTP" binding site on HRAS to be bound. After the non-covalent
interaction between Hras and PIK3CA has occurred, the allosteric response indicates that
"PI3_PI4_kinase" domain on PIK3CA will become "functional".
86

The context of a molecular interaction page (Appendix B9 or B10) is an interaction
event, and the interaction web page contains hyperlinks to allosteric regulation pages. The
context of an allosteric regulation page is either the allosteric condition (Appendix B6) or the
response (Appendix B7) event, and the allosteric regulation page contains hyperlinks to the
molecular interactions. This design allows users to traverse between the upstream interactions
and the downstream interactions in either direction, through allosteric regulations as the
intermediate and integrating points.
On the molecular interaction page, participating molecules including binding molecules,
enzymes, substrates and products also have hyperlinks to their own interaction-summary pages
such as the one shown on Appendix B8. It allows users to change the current context to another
molecule and explore parallel interactions or cellular response that involve the other molecule.
SNEC enabled us to build and traverse the macrophage pathways and to identify
cellular responses that can be affected by MTB. The pathways and their participating
components are described in Section 3.2 of the main text.
SNEC - screenshots
B1 - Protein search page
A protein can be searched by its synonym or accession number. The search can be
filtered based on organisms. The top report shows proteins in the BioCAD database, and the
second report shows proteins in the user's pathway models. The navigation buttons on the left
side are used to navigate between different biological concepts such as cells and interactions.
The hyperlink on a protein links to a protein's detail page.
B2 - Protein's detail page
The context of this page is a user-created protein. The reports show the annotations that
are derived from various sources. They include synonyms, database accession numbers, general
87

descriptions of the protein, and GO classifications. The navigation tree on the left has been
expanded to show the additional pages regarding the protein. Each navigation button directs the
user to a specific web page for "Localization", "Domain & Sites", "Allosteric regulation" or
"Interactions".
B3 - Protein's localization page
The context of this page is a user-created protein. The search on the top of the page
allows user to search for cells that he has created. After a cell has been selected, a user adds a
compartment as a possible location for the protein. The bottom report shows the localizations
that have been previously specified for the protein.
B4 - Protein's domain/site page
The context of this page is a user-created protein. The first report shows all annotated
domains and sites on this protein (e.g. Pfam domains). The second report shows any userdefined
domain or site. The three action buttons create a domain, a site or a PTM respectively.
A user can also add additional domain and sites as annotated by other sources.
B5 - Allosteric regulation summary page
The context of this page is a user-created protein. The first report shows all the
allosteric regulations that involve this protein. A new allosteric regulation can be added by an
action button. The hyperlinks in this report bring up the detail information of an allosteric
regulation, and display the condition and response reports located at the bottom of the page. A
new condition or response can be added via the corresponding action buttons. The reference
button brings up a reference page where a research article can be added to support the allosteric
88

egulation. The hyperlink in the condition report links to a condition detail page, and the
hyperlink in the response report links to a response detail page.
B6 - Allosteric regulation's detail - page
The context of this page is a condition event of an allosteric regulation. The first report
shows the information that is currently associated with a condition event. The information can
be modified by selecting any protein that can interact with the original protein. This allows an
allosteric regulation to be defined across proteins in the same complex. The combo boxes are
used to specify the domain, binding state and phosphorylation state that are required for the
condition. After the information has been specified, the two dynamic reports at the bottom will
be updated and show any non-covalent or covalent interaction that can "satisfy" the condition.
In this example, a non-covalent interaction between HRAS and PI3K3A can satisfy the
condition. The hyperlinks in the reports link to interaction detail pages.
B7 - Allosteric regulation's detail - page
The context of this page is a response event of an allosteric regulation. The first report
shows the information that is currently associated with this response event. The information can
be modified by selecting any protein that can interact with the original protein. This allows an
allosteric regulation to be defined across proteins in the same complex. The combo boxes are
used to specify the domain, and conformational states that are changed to as the result of the
response. After the information has been specified, the dynamic reports are updated and show
any non-covalent or covalent interaction that is "enabled or disabled" by the response. In this
example, a covalent interaction catalyzed by the enzyme PIK3CA is enabled by the response
(due to the kinase domain and the functional state associated with the response). The hyperlinks
in the reports link to interaction detail pages.
89

B8 - Interaction summary page
The context of this page is a user-created protein. This page shows non-covalent
interactions, covalent interactions and cellular responses that involve this protein. Each report
shows the detail of the interactions, and new interactions can be added by the different action
buttons. The hyperlinks in the report link to the detail pages for interactions or cellular
responses.
B9 - Non-covalent interaction's detail page
The context of this page is a non-covalent interaction event. To specify the interaction, a
user first searches for a molecule in his models. The molecule is then added as one of the
binding molecules (A or B). The two reports in the middle show the current binding molecules
associated with this event. The binding domains and additional phosphorylation states (optional)
are specified by combo boxes. The dynamic reports at the bottom show any activating
condition that is required for this interaction to occur as well as any deactivating condition that
prevents this interaction. For example, the occurrence of this non-covalent interaction depends
on the "SMALL_GTP" binding domain on HRAS to be in "bound' state. The bottom report also
shows the effects or consequences of the interaction if it occurs. For instance, the occurrence of
this interaction can cause the kinase domain on PIK3CA to become "functional for covalent
interaction". The hyperlinks in the condition report link to condition events of allosteric
regulations, and the hyperlinks in the effect report link to response events of allosteric
regulations.
B10 - Covalent interaction's detail page
90

The context of this page is a covalent interaction event. To specify the interaction, a
user first searches for a molecule in his models. The molecule is then added as an enzyme, a
substrate or a product. The three reports in the middle show the current molecules associated
with this event. An active site or a catalytic domain can be specified for the enzyme. In addition,
a modification site and its corresponding phosphorylation state can be specified if the substrate
is a protein. The dynamic reports at the bottom show any activating condition that is required
for this interaction to occur as well as any deactivating condition that prevents this interaction.
For example, the occurrence of this covalent interaction depends on the "PI3K_RBD" (the Ras
binding domain) on PIK3CA to be at "bound' state. On the other hand, this interaction is
inhibited if the "PI3K_P85B" (the p85 binding domain) on PIK3CA is bound. The bottom
report also shows the effects or consequences of the interaction. The hyperlinks in the condition
report link to condition events of allosteric regulations, and the hyperlinks in the effect report
link to response events of allosteric regulations.
B11 - Cellular response detail page
The context of this page is a cellular response event. The report shows all conditions
that are required for the occurrence of this cellular response. For example, the response "actin
polymerization and rearrangement" occurs when the "GTP binding domain" is bound on ARF6.
91

92
B1 - Protein search page

93
B2 - Protein's detail page

94
B3 - Protein's localization page

95
B4 - Protein's domain/site page

96
B5 - Allosteric regulation summary page

97
B6 - Allosteric regulation's detail - page

98
B7 - Allosteric regulation's detail - page

99
B8 - Interaction summary page

100
B9 - Non-covalent interaction's detail page

101
B10 - Covalent interaction's detail page

102
B11 - Cellular response detail page

Appendix C - List of molecules and events in the macrophage pathway model
C1 - Molecules in the macrophage model
Name Synonym Type Refseq
ACTN1
AKT1
AKT2
AKT3
AKT3
ALPHA-ACTININ CYTOSKELETAL ISOFORM, F-ACTIN
CROSS LINKING PROTEIN, ALPHA-ACTININ 1, NON-
MUSCLE ALPHA-ACTININ 1
EC 2.7.1.-, PROTEIN KINASE B, PKB, RAC-ALPHA
SERINE/THREONINE KINASE, C-AKT, RAC-PK-ALPHA
RAC-PK-BETA, EC 2.7.1.-, PKB BETA, RAC-BETA
SERINE/THREONINE PROTEIN KINASE, PROTEIN
KINASE AKT-2, PROTEIN KINASE B, BETA
RAC-PK-GAMMA, PKB GAMMA, RAC-GAMMA
SERINE/THREONINE PROTEIN KINASE, EC 2.7.1.-,
PROTEIN KINASE AKT-3, PROTEIN KINASE B, GAMMA,
STK-2
RAC-PK-GAMMA, PKB GAMMA, RAC-GAMMA
SERINE/THREONINE PROTEIN KINASE, EC 2.7.1.-,
PROTEIN KINASE AKT-3, PROTEIN KINASE B, GAMMA,
STK-2
Protein
Protein
Protein
Protein
Protein
NP_001093.1
NP_005154.1
NP_001617.1
NP_859029.1
NP_005456.1
ARF6 ADP-ribosylation factor 6 Protein NP_001654.1
BAD
BAD, BCL-2-LIKE 8 PROTEIN, BCL-XL/BCL-2
ASSOCIATED DEATH PROMOTER, BCL-2 BINDING
COMPONENT 6, BCL2-ANTAGONIST OF CELL DEATH
Protein
NP_004313.1
BCL2 APOPTOSIS REGULATOR BCL-2 Protein NP_000624.1
C3 COMPLEMENT C3 PRECURSOR Protein NP_000055.1
CCNB1 G2/MITOTIC-SPECIFIC CYCLIN B1 Protein NP_114172.1
CCND1
CD14
CDC2
CDC25A
CDC27
BCL-1 ONCOGENE, PRAD1 ONCOGENE, G1/S-SPECIFIC
CYCLIN D1
MYELOID CELL-SPECIFIC LEUCINE-RICH
GLYCOPROTEIN, MONOCYTE DIFFERENTIATION
ANTIGEN CD14 PRECURSOR
EC 2.7.1.-, CDK1, CYCLIN-DEPENDENT KINASE 1, CELL
DIVISION CONTROL PROTEIN 2 HOMOLOG, P34
PROTEIN KINASE
EC 3.1.3.48, M-PHASE INDUCER PHOSPHATASE 1, DUAL
SPECIFICITY PHOSPHATASE CDC25A
CELL DIVISION CYCLE PROTEIN 27 HOMOLOG, H-NUC,
CDC27HS
CDK10 EC 2.7.1.-, CELL DIVISION PROTEIN KINASE 10,
SERINE/THREONINE-PROTEIN KINASE PISSLRE
CDK2
EC 2.7.1.-, P33 PROTEIN KINASE, CELL DIVISION
PROTEIN KINASE 2
Protein
Protein
Protein
Protein
Protein
Protein
Protein
NP_444284.1
NP_000582.1
NP_001777.1
NP_001780.1
NP_001247.2
NP_003665.2
NP_001789.2
103

CDK7 CDK-ACTIVATING KINASE, EC 2.7.1.-, CAK, STK1, 39
KDA PROTEIN KINASE, TFIIH BASAL TRANSCRIPTION
FACTOR COMPLEX KINASE SUBUNIT, P39 MO15, CAK1,
CELL DIVISION PROTEIN KINASE 7
CHUK
CKS2
EC 2.7.1.-, INHIBITOR OF NUCLEAR FACTOR KAPPA-B
KINASE ALPHA SUBUNIT, NUCLEAR FACTOR
NFKAPPAB INHIBITOR KINASE ALPHA, I-KAPPA-B
KINASE 1, IKK1, IKK-ALPHA, CONSERVED HELIX-
LOOP-HELIX UBIQUITOUS KINASE, IKK-A, IKAPPAB
KINASE, I KAPPA-B KINASE ALPHA, NFKBIKA, IKBKA
CKS-2, CYCLIN-DEPENDENT KINASES REGULATORY
SUBUNIT 2
Protein
Protein
Protein
NP_001790.1
NP_001269.2
NP_001818.1
EEA1 early endosome antigen 1 Protein NP_003557.1
FCGR1A
FGR
CD64 ANTIGEN, FCRI, HIGH AFFINITY
IMMUNOGLOBULIN GAMMA FC RECEPTOR I
PRECURSOR, FC-GAMMA RI, IGG FC RECEPTOR I
EC 2.7.1.112, P55-FGR, PROTO-ONCOGENE TYROSINE-
PROTEIN KINASE FGR, C-FGR
Protein
Protein
NP_000557.1
NP_005239.1
GAB2 GRB2-associated binding protein 2 Protein NP_536739.1
GDP guanosine diphosphate Nucleotide
GRB2 growth factor receptor-bound protein 2 Protein NP_002077.1
GSK3B
EC 2.7.1.37, GSK-3 BETA, GLYCOGEN SYNTHASE
KINASE-3 BETA
Protein
GTP guanosine triphosphate Nucleotide
HCK
HEMOPOIETIC CELL KINASE, EC 2.7.1.112, TYROSINE-
PROTEIN KINASE HCK, P59-HCK/P60-HCK
Protein
NP_002084.2
NP_002101.2
HRAS TRANSFORMING PROTEIN P21/H-RAS-1, C-H-RAS Protein NP_789765.1
IGHG3
ITGAM
ITGB2
HDC, IG GAMMA-3 CHAIN C REGION, HEAVY CHAIN
DISEASE PROTEIN
NEUTROPHIL ADHERENCE RECEPTOR, CR-3 ALPHA
CHAIN, CELL SURFACE GLYCOPROTEIN MAC-1 ALPHA
SUBUNIT, INTEGRIN ALPHA-M PRECURSOR, CD11B,
LEUKOCYTE ADHESION RECEPTOR MO1
CD18, COMPLEMENT RECEPTOR C3 BETA-SUBUNIT,
CELL SURFACE ADHESION GLYCOPROTEINS LFA-
1/CR3/P150,95 BETA-SUBUNIT, INTEGRIN BETA-2
PRECURSOR
Protein
Protein
Protein
LPS lipopolysaccharide Phospholipid
NG_001019
NP_000623.1
NP_000202.1
LYN EC 2.7.1.112, TYROSINE-PROTEIN KINASE LYN Protein NP_002341.1
ManLAM Mannose-capped lipoarabinomannan Phospholipid
MAP3K14
EC 2.7.1.37, NF-KAPPA BETA-INDUCING KINASE,
HSNIK, SERINE/THREONINE PROTEIN KINASE NIK,
MITOGEN-ACTIVATED PROTEIN KINASE KINASE
KINASE 14
Protein
NP_003945.1
104

MAP3K8
NCF4
NFKB1
NFKB2
NFKBIA
PDK1
PDK2
PDPK1
EC 2.7.1.-, C-COT, MITOGEN-ACTIVATED PROTEIN
KINASE KINASE KINASE 8, COT PROTO-ONCOGENE
SERINE/THREONINE-PROTEIN KINASE, CANCER
OSAKA THYROID ONCOGENE
NCF-4, P40PHOX, P40-PHOX, NEUTROPHIL CYTOSOL
FACTOR 4, NEUTROPHIL NADPH OXIDASE FACTOR 4
EBP-1, DNA-BINDING FACTOR KBF1, NUCLEAR
FACTOR NF-KAPPA-B P105 SUBUNIT
ONCOGENE LYT-10, LYT10, NUCLEAR FACTOR NF-
KAPPA-B P100/P49 SUBUNITS, H2TF1
MAJOR HISTOCOMPATIBILITY COMPLEX ENHANCER-
BINDING PROTEIN MAD3, IKB-ALPHA, I-KAPPA-B-
ALPHA, IKAPPABALPHA, NF-KAPPAB INHIBITOR
ALPHA
EC 2.7.1.99, PYRUVATE DEHYDROGENASE KINASE
ISOFORM 1, [PYRUVATE DEHYDROGENASE
[LIPOAMIDE]] KINASE ISOZYME 1, MITOCHONDRIAL
PRECURSOR
EC 2.7.1.99, PYRUVATE DEHYDROGENASE KINASE
ISOFORM 2, [PYRUVATE DEHYDROGENASE
[LIPOAMIDE]] KINASE ISOZYME 2, MITOCHONDRIAL
PRECURSOR
EC 2.7.1.37, PROTEIN PRO0461, 3-PHOSPHOINOSITIDE
DEPENDENT PROTEIN KINASE-1, HPDK1
Protein
Protein
Protein
Protein
Protein
Protein
Protein
Protein
PI phosphorylates phosphatidylinositol Phospholipid
PI3P phosphatidylinositol-3-phosphate Phospholipid
NP_005195.2
NP_000622.1
NP_003989.1
NP_002493.2
NP_065390.1
NP_002601.1
NP_002602.2
NP_002604.1
PIK3C3 phosphoinositide-3-kinase class 3, Vps34 Protein NP_002638.1
PIK3CA
PIK3R1
PIK3R1
PTDINS-3-KINASE P110, PI3-KINASE P110 SUBUNIT
ALPHA, PHOSPHATIDYLINOSITOL-4,5-BISPHOSPHATE
3-KINASE CATALYTIC SUBUNIT, ALPHA ISOFORM, EC
2.7.1.153, PI3K
PTDINS-3-KINASE P85-ALPHA, PI3K,
PHOSPHATIDYLINOSITOL 3-KINASE REGULATORY
ALPHA SUBUNIT, PI3-KINASE P85-ALPHA SUBUNIT
PTDINS-3-KINASE P85-ALPHA, PI3K,
PHOSPHATIDYLINOSITOL 3-KINASE REGULATORY
ALPHA SUBUNIT, PI3-KINASE P85-ALPHA SUBUNIT
Protein
Protein
Protein
NP_006209.1
NP_852664.1
NP_852665.1
PIK3R4 phosphoinositide-3-kinase, regulatory subunit 4, p150 Protein NP_055417.1
PIP2 phosphatidylinositol-4,5-bisphosphate Phospholipid
PIP3 phosphatidylinositol-3,4,5-bisphosphate Phospholipid
PSCD3 GENERAL RECEPTOR OF PHOSPHOINOSITIDES 1,
CYTOHESIN 3, GRP1, ARNO3 PROTEIN, ARF
NUCLEOTIDE-BINDING SITE OPENER 3
Protein
NP_004218.1
PXN PAXILLIN Protein NP_002850.1
RAB5A RAS-RELATED PROTEIN RAB-5A Protein NP_004153.2
105

RB1
RELA
PP110, RETINOBLASTOMA-ASSOCIATED PROTEIN, RB,
P105-RB
NUCLEAR FACTOR NF-KAPPA-B P65 SUBUNIT,
TRANSCRIPTION FACTOR P65
Protein
Protein
NP_000312.1
NP_068810.1
RELB TRANSCRIPTION FACTOR RELB, I-REL Protein NP_006500.1
RIN1
RPS6KB1
SLC2A4
RAS INHIBITOR JC99, RAS
INTERACTION/INTERFERENCE PROTEIN 1, RAS AND
RAB INTERACTOR 1
EC 2.7.1.-, P70-S6K, RIBOSOMAL PROTEIN S6 KINASE,
S6K
SOLUTE CARRIER FAMILY 2, FACILITATED GLUCOSE
TRANSPORTER, MEMBER 4, GLUCOSE TRANSPORTER
TYPE 4, INSULIN-RESPONSIVE
Protein
Protein
Protein
NP_004283.1
NP_003152.1
NP_001033.1
SOS1 SOS-1, SON OF SEVENLESS PROTEIN HOMOLOG 1 Protein NP_005624.2
TLN1 TALIN 1 Protein NP_006280.2
TLR2
TOLL-LIKE RECEPTOR 2 PRECURSOR,
TOLL/INTERLEUKIN 1 RECEPTOR-LIKE PROTEIN 4
YWHAB PROTEIN KINASE C INHIBITOR PROTEIN-1, KCIP-1, 14-
3-3 PROTEIN BETA/ALPHA, PROTEIN 1054
Protein
Protein
NP_003255.2
NP_647539.1
Appendix C1. Molecules in the macrophage model. The table shows the list of prototypical
molecules that have been included in the pathway model. Those proteins include cell receptors
such as FCGR1A (Fcγ), ITGAM (CD11b), ITGB2 (CD18), CD14, TLR2 that are relevant to the
process of bacterial internalization of macrophages. Two distinct classes of PI3Ks have been
modeled: the class I PI3K composed of p85 regulatory (PIK3R1) and p110 catalytic subunits
(PIK3CA), and the class III PI3K composed of p150 (PIK3R4) and PIK3C3 subunits. The
pathway model contained various kinases such as Lyn, PDK1 (PDPK1) and AKT1, and small
GTP proteins including Ras (HRAS), ARF6 and Rab5a. Adaptor proteins, Gab2 and Grb2, and
transcription factors, NF-kB, have also been incorporated into the model. Column 1: the
molecule name (gene locus names for proteins). Column 2: the synonyms or EC number if the
molecule is an enzyme. Column 3: the type of the molecule. Column 4: the accession number
from RefSeq.
106

C2 - Non-covalent interactions in the macrophage model
107
Molecule A Domain A Domain A -
accession
number
Molecule B Domain B Domain B -
accession
number
ACTN1 integrin-binding domain ITGB2 Cytoskeleton protein
binding domain
AKT1 PH PF00169 PIP3 binding site for PH
domain
Reference
Velasco-Velazquez
et al. 2003
Cantley 2002;
Wymann, Zvelebil,
and Laffargue 2003
AKT2 PH PF00169 PIP3 Downward 2004
AKT3 PH PIP3 Downward 2004
ARF6 GTP binding domain GTP Cantley 2002;
Stephens, Ellson,
and Hawkins 2002
ARF6 Grp1 binding domain PSCD3 SEC7 PF01369 Cantley 2002
ARF6 GDP binding domain GDP Cantley 2002;
Stephens, Ellson,
and Hawkins 2002
BCL2 BH3 PS01259 BAD BH3 Cantley 2002
CCNB1 Cdc2 binding CDC2 Cyclin binding Pavletich 1999
CD14 LPS bidning domain LPS Hmama et al. 1999
EEA1 Rab5 binding RAB5A EEA1 binding Vieira et al. 2003
EEA1 FYVE PI3P Stenmark and
Aasland 1999;
Wurmser, Gary, and
Emr 1999
FGR CD18 binding domain ITGB2 Src-family tyrosine
kinase binding
domain
Velasco-Velazquez
et al. 2003

108
GAB2 PH PIP3 binding site for PH
domain
Gu et al. 2003
GAB2 pYxxM PIK3R1 SH2 PF00017 Cantley 2002
HCK CD18 binding domain ITGB2 Src-family tyrosine
kinase binding
domain
Velasco-Velazquez
et al. 2003
HRAS GDP binding domain GDP Macaluso et al. 2002
HRAS SMALL_GTP TIGR00231 GTP Macaluso et al. 2002
HRAS
IGHG3
PI3K-p110 binding
domain
Fc-gamma receptor
binding domain
PIK3CA PI3K_RBD PF00794 Vanhaesebroeck and
Waterfield 1999;
Cantley 2002
FCGR1A IG PF00047 Gu et al. 2003
ITGAM VWA PF00092 C3 Velasco-Velazquez
et al. 2003
ITGB2 CD11b binding domain ITGAM CD18 binding
domain
ITGB2
Src-family tyrosine kinase
bidning domain
LYN
CD18 binding
domain
Velasco-Velazquez
et al. 2003
Velasco-Velazquez
et al. 2003
LYN FcgR binding FCGR1A Lyn binding Gu et al. 2003
NCF4 PX PF00787 PI3P Stephens, Ellson,
and Hawkins 2002
NFKB1 RHD RELA RHD PF00554 Hayden and Ghosh
2004
NFKB2 RHD PF00554 RELB RHD PF00554 Hayden and Ghosh
2004
NFKBIA NF-kB binding RELA RHD for IkB
binding
PDPK1 PH_RELATED SSF50729 PIP3 binding site for PH
domain
Hayden and Ghosh
2004
Cantley 2002

109
PIK3C3 PI kinase ManLAM Fratti et al. 2001
PIK3CA PI3K_P85B PF02192 PIK3R1 p110-binding
domain
Wymann, Zvelebil,
and Laffargue 2003
PIK3R4 Vps34p-binding PIK3C3 p150-binding Vanhaesebroeck and
Waterfield 1999
PIK3R4 WD40 RAB5A p150 binding Murray et al. 2002
PSCD3 PH PF00169 PIP3 binding site for PH
domain
PXN integrin-binding domain ITGB2 Cytoskeleton protein
binding domain
Cantley et al. 2002
Velasco-Velazquez
et al. 2003
RAB5A PIP3 binding domain PIP3 Stephens, Ellson,
and Hawkins 2002
RAB5A GDP-binding GDP Murray, 2002,
Lanzetti, 2004, Tall,
2001
RAB5A SMALL_GTP TIGR00231 GTP Murray et al. 2002;
Lanzetti et al. 2004;
Tall et al. 2001
RIN1 Rab5 GEF RAB5A GEF binding Tall et al. 2001
SLC2A4 PIP3 binding domain PIP3 Wymann, Zvelebil,
and Laffargue 2003
SOS1 RASGEF PF00617 HRAS GEF binding domain Wymann, Zvelebil,
and Laffargue 2003
SOS1 Proline-rich GRB2 SH3 PF00018 Wymann, Zvelebil,
and Laffargue 2003
TLN1 integrin-binding domain ITGB2 Cytoskeleton protein
binding domain
Velasco-Velazquez
et al. 2003
TLR2 pYxxM PIK3R1 SH2 PF00017 Arbibe et al. 2000
TLR2 CD14 binding domain CD14 TLR2 binding
domain
Muta and Takeshige
2001

YWHAB
phosphoserine binding
domain
BAD S99 Cantley 2002
Appendix C2. Non-covalent interactions in the macrophage model. The table shows the list of non-covalent event prototypes that were
incorporated in the pathway model. Column 1: name of the binding molecule A (gene locus names for proteins). Column 2: domain of
molecule A that participates in the interaction. Column 3: accession number for annotated domains (A) (abbreviation: PF, Pfam; PS,
PROSITE; TIGR, TIGRFAMs; SSF, SUPERFAMILY). Column 4: name of the binding molecule B (gene locus names for proteins). Column
5: domain of molecule B that participates in the interaction. Column 6: accession number for annotated domains (B) Column 7: literature
that supports the interaction.
110

C3 - Covalent interactions in the macrophage model
111
Enzyme Enzyme's domain Enzyme's
domain -
accession
number
Substrate
Substrate's
site
Substrate's site
state - before
Product
Product's
site
Product's site
state - after
Reference
AKT1 PROTEIN_KINASE_ST PS00108 MAP3K8 S400 Not-phosphorylated MAP3K8 S400 Phosphorylated Kane et al.
2002
AKT1 PROTEIN_KINASE_ST PS00108 BAD S118 Not-phosphorylated BAD S118 Phosphorylated Datta et al.
1997
AKT1 PROTEIN_KINASE_ST PS00108 GSK3B S9 Not-phosphorylated GSK3B S9 Phosphorylated Wymann,
Zvelebil, and
Laffargue
2003
AKT1 PROTEIN_KINASE_ST PS00108 BAD S99 Not-phosphorylated BAD S99 Phosphorylated Datta et al.
1997
CDC2 PROTEIN_KINASE_ST PS00108 RB1 S807 Not-phosphorylated RB1 S807 Phosphorylated Shapiro and
Harper 1999
CDC25A RHODANESE PF00581 CDC2 Y15 Phosphorylated CDC2 Y15 Notphosphorylated
Kumagai and
Dunphy 1991
CDK7 PROTEIN_KINASE_ST PS00108 CDC2 T161 Not-phosphorylated CDC2 T161 Phosphorylated Pavletich 1999
CHUK PROTEIN_KINASE_ST PS00108 NFKB2 S Not-phosphorylated NFKB2 S Phosphorylated Hayden and
Ghosh 2004
GSK3B PROTEIN_KINASE_ST PS00108 CCND1 S Not-phosphorylated CCND1 S Phosphorylated Cantley 2002
LYN PROTEIN_KINASE_TYR PS00109 GAB2 Y Not-phosphorylated GAB2 Y Phosphorylated Gu et al. 2003
MAP3K14 PROTEIN_KINASE_ST PS00108 CHUK S180 Not-phosphorylated CHUK S180 Phosphorylated Hayden and
Ghosh 2004
MAP3K8 PROTEIN_KINASE_ST PS00108 MAP3K14 S Not-phosphorylated MAP3K14 S Phosphorylated Lin et al. 1999
PDPK1 PKINASE PF00069 AKT2 T Not-phosphorylated AKT2 T Phosphorylated Downward
2004

PDPK1 PKINASE PF00069 RPS6KB1 T389 Not-phosphorylated RPS6KB1 T389 Phosphorylated Cantley 2002
PDPK1 PKINASE PF00069 AKT1 T308 Not-phosphorylated AKT1 T308 Phosphorylated Cantley 2002
PIK3C3 PI kinase PI PI3P Vanhaesebroec
k et al. 2001
PIK3CA PI3_PI4_KINASE PF00454 PIP2 PIP3 Vanhaesebroec
k and
Waterfield
1999
Appendix C3. Covalent interactions in the macrophage model. The table shows the list of covalent event prototypes that were incorporated
in the pathway model. Column 1: name of the enzyme. Column 2: active site or catalytic domain of the enzyme. Column 3: accession
number for annotated domains (for the Enzyme; abbreviation: PF, Pfam; PS, PROSITE). Column 4: name of the substrate. Column 5:
modification site of the substrate. Column 6: phosphorylation state before the covalent interaction. Column 7: name of the product. Column
8: modification site of the product. Column 9: phosphorylation state after the covalent interaction. Column 10: literature that supports the
interaction.
112

C4 - Allosteric regulations in the macrophage model
113
Protein in
condition
Domains required States required Protein in
response
Domains affected State changed to Reference
AKT1 T308 Phosphorylated AKT1 PROTEIN_KINASE_ST Func. for cov. Downward 2004
ARF6 GDP binding domain Bound ARF6 GTP binding domain Non-func. for non-cov. Cantley 2002;
Stephens, Ellson, and
Hawkins 2002
ARF6 Grp1 binding domain Bound ARF6 GDP binding domain Non-func. for non-cov. Cantley 2002
BAD S118 Phosphorylated BAD BH3 Non-func. for non-cov. Cantley 2002; Datta et
al. 1997
BAD S99 Bound,
Phosphorylated
CDC2 Cyclin binding, Y15,
T161
Bound, Notphosphorylated,
Phosphorylated
BAD S118 Func. for cov. Datta et al. 1997
CDC2 PROTEIN_KINASE_ST Func. for cov. Pavletich 1999;
Kumagai and Dunphy
1991
CHUK S180 Phosphorylated CHUK PROTEIN_KINASE_ST Func. for cov. Hayden and Ghosh
2004
FCGR1A IG Bound FCGR1A Lyn binding Func. for non-cov. Gu et al. 2003
FGR CD18 binding domain Bound FGR PROTEIN_KINASE_TYR Func. for cov. Velasco-Velazquez et
al. 2003
GAB2 Y Phosphorylated GAB2 Y Func. for non-cov. Wymann, Zvelebil, and
Laffargue 2003; Gu et
al. 2003
GSK3B S9 Phosphorylated GSK3B PROTEIN_KINASE_ST Non-func. for cov. Wymann, Zvelebil, and
Laffargue 2003
HRAS GDP binding domain Bound HRAS SMALL_GTP Non-func. for non-cov. Macaluso et al. 2002
HRAS SMALL_GTP Bound HRAS PI3K-p110 binding domain Func. for non-cov. Wymann, Zvelebil, and
Laffargue 2003

114
HRAS GEF binding domain Bound HRAS GDP binding domain,
SMALL_GTP
ITGAM VWA Bound ITGB2 Src-family tyrosine kinase
binding domain
Non-func. for non-cov.,
Func. for non-cov.,
Func. for non-cov.
Cantley 2002,
Wymann, Zvelebil, and
Laffargue 2003
Velasco-Velazquez et
al. 2003
LYN CD18 binding domain Bound LYN PROTEIN_KINASE_TYR Func. for cov. Velasco-Velazquez et
al. 2003
LYN FcgR binding Bound LYN PROTEIN_KINASE_TYR Func. for cov. Gu et al. 2003
MAP3K14 S Phosphorylated MAP3K14 PROTEIN_KINASE_ST Func. for cov. Lin et al. 1999
MAP3K8 S400 Phosphorylated MAP3K8 PROTEIN_KINASE_ST Func. for cov. Kane et al. 2002
PIK3C3 PI kinase Bound PIK3C3 PI kinase Non-func. for cov. Fratti et al. 2001
PIK3CA PI3K_P85B Bound PIK3CA PI3_PI4_KINASE Non-func. for cov. Vanhaesebroeck and
Waterfield 1999
PIK3CA PI3K_RBD Bound PIK3CA PI3_PI4_KINASE Func. for cov. Vanhaesebroeck and
Waterfield 1999;
Cantley 2002
PIK3R1 SH2 Bound PIK3CA PI3_PI4_KINASE Func. for cov. Vanhaesebroeck and
Waterfield 1999
RAB5A SMALL_GTP Bound RAB5A p150 binding Func. for non-cov. Murray et al. 2002
RAB5A GDP-binding Bound RAB5A SMALL_GTP Non-func. for non-cov. Murray et al. 2002
RAB5A GEF binding Bound RAB5A GDP-binding Non-func. for non-cov. Tall et al. 2001
RELA RHD for IkB binding Bound RELA Nuclear localization
sequence (NLS)
Non-func. for non-cov.
Hayden and Ghosh
2004
Appendix C4. Allosteric regulations in the macrophage model. The table shows the list of allosteric regulation prototypes that were
incorporated in the pathway model. Column 1: name of the protein involved in the condition events. Column 2: domain and sites of the
protein, required for the conditions. Column 3: states (binding or phosphorylation states) required for the conditions. Column 4: name of
the protein affected by the response events. Column 5: domains and sites of the protein, affected by the response. Column 6: states
(conformational states) affected by the responses. Column 7: literature that supports the allosteric regulation.

C5 - Cellular responses and their conditions in the macrophage model
115
Cellular Response
Molecule in
condition
Domain/Site involved Binding state Phosphorylation
state
Reference
Actin polymerization and rearrangement ARF6 GTP binding domain Bound Cantley 2002
Cell cycle entry - S phase CCND1 phospho-S or T Not-phosphorylated Cantley 2002;
Wymann, Zvelebil,
and Laffargue 2003
Cell survival BCL2 BH3 Not Bound Cantley 2002;
Wymann, Zvelebil,
and Laffargue 2003
Intracellular glucose uptake SLC2A4 PIP3 binding domain Bound Wymann, Zvelebil,
and Laffargue 2003
Membrane delivery to plasma membrane ARF6 GTP binding domain Bound Stephens, Ellson,
and Hawkins 2002
Phagosome and lysosome fusion EEA1 FYVE Bound Fratti et al. 2001
Protein synthesis RPS6KB1 Threonine phosphorylation site Phosphorylated Cantley 2002;
Wymann, Zvelebil,
and Laffargue 2003
Recruitment of oxidase complex to phagosome NCF4 PX Bound Stephens, Ellson,
and Hawkins 2002
Appendix C5. Cellular responses and their conditions in the macrophage model. The table shows cellular response prototypes that were
incorporated in the pathway model. Column 1: name of the cellular response. Column 2: molecule that is required as the condition to
induce the cellular response. Column 3: domain or site of that molecule, involved in the condition. Column 4: Binding state required for the
condition. Column 5: phosphorylation state required for the condition. Column 6: literature that supports the cellular response and its
condition.