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Strasbourg Complex Systems Roadmap

Participants

  • Julie Thompson, Institute of Genetics and Molecular and Cellular Biology (IGBMC), Illkirch
  • Elisabeth Davioud-Charvet, Ecole Européenne de Chimie, des Polymères et des Matériaux (EPCM), Strasbourg

Keywords

transcriptional networks, metabolic pathways, macromolecular complexes, genetic mutation, physiology, pathology, systems biology, synthetic biology, evolution,

Introduction

Advances in genomics technologies and the availability of very large, diverse datasets have led to the recent study of complex biological systems, focusing on the study of molecular networks, involving genes, proteins and metabolites, in the larger context of the cell or organism. Systems biology aims to explore the biological properties resulting from the interactions of the individual components, investigating processes at different scales and their overall systemic integration. At the highest level, physiology and pathology are determined by these interactions between many processes at different scales.

Questions
  • Integrated model of the complete network, from the molecule to the organism
  • Multi-scale space: physics, geometry, large dimensions, degrees of freedom
  • Multi-scale time: reaction/diffusion rates, mutation frequency, evolutionary rates
  • Dynamic aspects (molecules, interactions, signaling,...)
  • Collective effects: emergent, immergent, irreversible versus equilibrium
  • Robustness and optimality of protein networks and their function(Evolution/Development)
  • Analysis of perturbations in the system (genetic mutations, environmental factors)

Objects

Genomes and gene products
Protein networks, metabolic pathways, flux control analysis
Phenotypic effects
Physio-pathologies
Interaction host-pathogens
Mimetism of genetic mutations by low-weight compounds
Environmental Factors
Enzymology
Pharmacology
Medicinal Chemistry

Challenges

Multi-scale analysis and modelling from molecules to populations
Modelling of noisy systems: intrinsic, experimental error
Reconstruction of multi-scale dynamics
Emergence and immergence processes
Deterministic versus random factors
Perturbations and robustness of complex systems
Comparison of complex systems: 'similarity'/'distance' measures
Designing Artificial Complex Systems: "synthetic biology"

Education

Handbook of complex systems modeling and analysis for biologists

European tempus project for North African bioinformatics education centre (Ahmed Mansour, Zagazig University, Egypt; Amal MAURADY, FST Tanger, Maroc; Mourad Elloumi, Faculté des Sciences Economiques et de Gestion de Tunis, Tunisia)

Platforms

Centre for Integrative Biology, Illkirch: the objective is to study biological mechanisms from the atom all the way up to organisms, particularly in mice, and to select substances intended to become medications.

EASEA (EAsy Specification of Evolutionary Algorithms) is an Artificial Evolution platform that allows scientists with only basic skills in computer science to exploit the massive parallelism of many-core architectures in order to optimize virtually any real-world problems (continous, discrete or combinatorial).


Shared Multi-scale data

  • SM2PH: database of all proteins involved in human genetic diseases. For each protein, 3D structural and evolutionary data are provided, coupled to mutation and phenotypic data (decrypthon.igbmc.fr/sm2ph)
  • MSV3d: database of human missense variants mapped to 3D protein structures (decrypthon.igbmc.fr/msv3d)
  • EvoluCode: database of vertebrate evolutionary histories for 20,000 human proteins (lbgi.igbmc.fr/barcodes)

References

  • Kitano H. Computational systems biology. Nature. 2002 Nov 14;420(6912):206-10.
  • Tozzini V. Multiscale modeling of proteins. Acc Chem Res. 2010 Feb 16;43(2):220-30.
  • Komurov K. Modeling community-wide molecular networks of multicellular systems. Bioinformatics. 2011 Dec 30.
  • Ideker T, Krogan NJ. Differential network biology. Mol Syst Biol. 2012 Jan 17;8:565.

Notes