organisation - the Instituto Gulbenkian de Ciência

COMPLEX ADAPTIVE
SYSTEMS AND
COMPUTATIONAL BIOLOGY
Luis Mateus Rocha Principal Investigator
PhD in Systems Science, State University of New York, 1997
Postdoctoral Fellow, Los Alamos National Laboratory (NM. USA)
Associate Professor, Indiana University, USA
Principal Investigator at the IGC since 2000
link to external website
We are interested in the informational properties of natural and artificial systems
which enable them to adapt and evolve. This means both producing computational
models of biological systems to understand the evolutionary role of
information, as well as abstracting principles from biology to produce adaptive
information technology. Our current research projects are on complex dynamics
in biological networks (gene regulation, cell signalling, and metabolic networks),
text and literature mining (in proteomics, protein-protein interaction
and pharmokinetics), computational models of RNA editing, artificial immune
systems, genomic multivariate analysis, evolutionary systems, artificial life, cognitive
science, and biosemiotics.
BIOMEDICAL LITERATURE MINING
The aims are to carry out literature-based automatic discovery, classification
and annotation of protein-protein and gene-disease interactions, pharmokinetic
data, protein sequence family and structure prediction, functional annotation
of transcription data, enzyme annotation publications, etc.
We are currently looking at automated mechanisms for document classification
and obtained preliminary results that are being prepared for publication.
This entails close collaborations with Analia Lourenço (Minho) and Hagit Shatkay
(Queens University, Canada).
COLLECTIVE DYNAMICS IN COMPLEX BIOCHEMICAL NETWORKS
Modelling of the dynamics of complex biochemical networks, to identify control,
modularity, robustness and collective computation. Currently working with
models of genetic regulation in yeast, body segmentation in Drosophila, intracellular
signal transduction in fibroblasts, a genome-scale transcriptional and
metabolic network for E. coli, and others.
GROUP MEMBERS
Manuel Marques-Pita (Post-doc)
Artemy Kolchinsky (PhD Student)
Azadeh Nematzadeh (PhD Student)
Tiago Simas (PhD Student)
Alaa Abi-Haidar (PhD Student, left in January)
Santosh Mannika (PhD Student, started in June)
COLLABORATORS
Anália Lourenço (Universidade do Minho, Portugal)
Hagit Shatkay (Queen's University, Canada)
Marta Cascante (University of Barcelona, Spain)
Jim Crutchfield (University of California, Davis, USA)
Santiago Schnell (Michigan University, USA)
Luis Amaral (Northwestern University, USA)
Chistof Teuscher (Portland State University, USA)
Stefan Mass (Lehigh Univeraity, USA)
Alekos Athanasiadis (IGC, Portugal)
Jorge Carneiro (IGC, Portugal)
Pedro Lima (Instituto Superior Técnico, Portugal)
Porfirio Silva (Instituto Superior Técnico, Portugal)
Nuno Crato (Instituto Superior de Economia e Gestão, Portugal)
FUNDING
Fundação para a Ciência e a Tecnologia (FCT), Portugal
FLAD Computational Biology Collaboratorium, Portugal
Uninova collaboration
PUBLIC ENGAGEMENT IN SCIENCE
Manuel Pita, Public lecture - AR, Champalimaud Centre for the Unknown,
November
We have completed the first year of our FCT funded R&D grant on studying collective
information processing in biochemical networks.
COMPUTATIONAL MODELS OF RNA EDITING
We are building computational models to study the evolutionary implications of
genotype editing in the living organisation. Our goal is twofold:
1. To study the role of RNA Editing regulation in the evolutionary process;
2. To investigate the conditions under which genotype edition improves the
optimisation performance of evolutionary algorithms.
After developing a set of computational tools needed for a number of simulations
and studies on artificial RNA editing systems, we are currently generating
and analysing data in the context of our collaborations with Alekos Athanasiadis
(IGC) and Stefan Mas (Lehigh).
ARTIFICIAL MODELS OF T-CELL CROSS-REGULATION
We use Carneiro's Model of Cross-regulation in T-Cell dynamics to produce bioinspired
algorithms for binary classification. The goal is to gain further insights
into T-cell cross-regulation in the vertebrate immune system, and produce useful
bio-inspired algorithms for text mining and spam detection. After one of our
team members 'completion of his PhD on this project, we are currently redefining
the next goals, and approaches for its continuation, as well as preparing
grant applications to hire new team members.
STOCHASTIC MODELS OF TOPOLOGY CONSTRAINTS ON COMPLEX NETWORKS
This project aims to study of transitive properties of complex networks modelled
as weighted graphs. Studying the impact of alternative distance measures
on scale free and small-World behaviour, and developing stochastic
models of vertex aging in networks, to better predict network growth. Ongoing
analysis of large-scale Wikipedia data, networks of neuronal activity, etc.
Started collaboration with Nuno Crato (ISEG) to model decision-tree searches.
IGC ANNUAL REPORT ‘11
RESEARCH GROUPS
59