Qualitative Modeling of Cellular Networks with CellNetAnalyzer and ...

� structural network analysis vs. ODE based modeling
� mass flows (metabolic networks) vs. signal flows (signaling networks)
� general set-up and basic usage of CellNetAnalyzer
� methods for mass-flow networks (with live demonstration of CellNetAnalyzer): graph-theoretical
properties, conservation relations, null-space analysis, constraint-based modeling, metabolic flux
analysis, flux balance analysis, pathway analysis based on elementary modes, minimal cut sets
� methods for signal-flow networks (with live demonstration of CellNetAnalyzer and Odefy):
- interaction graphs: feedback loops and signaling paths, network-wide interdependencies and
dependency matrix, predicting qualitative effects of perturbations
- logical/Boolean networks: analyzing the qualitative input/output behavior of signaling networks
using logical steady states, structural couplings of signal flows, minimal intervention sets (identification
of combinatorial interventions preventing or provoking certain qualitative responses)
- Odefy: methodology for transforming Boolean models into ODEs; simulation of Boolean and ODE
models with Odefy; large-scale example
� other functionalities of CellNetAnalyzer (displaying experimental data, API, import/export)
For academic use, CellNetAnalyzer (together with plugin Odefy) can be downloaded for free from:
www.mpi-magdeburg.mpg.de/projects/cna/cna.html. MATLAB (version 7.1 or higher) is required.
Part II: From high-throughput protein activity data to cell-specific logic models
using DataRail and CellNetOptimizer
Julio Saez-Rodriguez, Leonidas Alexopoulos
Purpose and background: The process of constructing and testing models against high-throughput data,
particularly those models that incorporate significant prior knowledge, involves multiple steps that are
currently very poorly integrated. Together with colleagues in the groups of Peter Sorger and Douglas
Lauffenburger at the CellDecisionProcess Center at M.I.T. and Harvard Medical School, we have
developed SB-Pipeline to create an effective workflow based on public standards and modern software
practice. SB-Pipeline is a multi-faceted software platform that pulls together all of the steps involved in
collecting and transforming primary data; constructing, annotating and calibrating models; and distributing
and sharing simulations and analyses. SB-Pipeline is primarily concerned with data and model management
for the purpose of calibration, and implements a robust system for tracking the provenance of data, links
between data and models, and the origins of model assumptions in data or the literature. SB-Pipeline is a
collection of discrete but interoperable software tools, rather than a single integrated system, and
incorporates standard protocols for import and export of data.
In this second part of the tutorial we will present two modules of SB-Pipeline: DataRail, and
CellNetOptimizer and use a data-set of high-throughput functional data of signal transduction in liver cells
(Alexopoulos et al., submitted) to illustrate its use.
- DataRail is an open source MATLAB toolbox for managing, transforming, visualizing, and modeling
data, in particular the varied high-throughput data encountered in Systems Biology (Saez-Rodriguez et al.,
Bioinformatics, 24(6):840-7, 2008). It supports data-driven models, in particular Multiple Linear
Regression (MLR), Partial Least Squares Regression (PLSR), and Bayesian Inference.
- CellNetOptimizer (CNO) is a MATLAB toolbox to turning pathway maps into logical models (Boolean or
Fuzzy) that can be calibrated against experimental data (Saez-Rodriguez et al., Mol. Syst. Biol. 5:331,
2009), generating functional, predictive, cell-type specific models of mammalian signal transduction. Representation
and simulation of logical networks follows the same rules as in CellNetAnalyzer.
DataRail and CellNetOptimizer are complementary and compatible with CellNetAnalyzer and Odefy, tools
focused on the analysis of cellular networks presented in the first part of the tutorial.
SB-Pipeline resources can be downloaded from http://code.google.com/p/sbpipeline/ and
http://www.cdpcenter.org/resources/. MATLAB is required to run DataRail and CellNetOptimizer.