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| − | [[Strasbourg Complex Systems Roadmap ( | + | [[Strasbourg Complex Systems Roadmap]] |
| + | ====Participants==== | ||
| + | * [http://icube-bfo.unistra.fr/fr/index.php/Julie_Thompson Julie Thompson], Institute of Genetics and Molecular and Cellular Biology (IGBMC), Illkirch | ||
| + | * [http://www-ecpm.u-strasbg.fr/umr7509/labo_davioudcharvet/ 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==== | ====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 genes, proteins, | + | 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==== | ====Objects==== | ||
| − | :[[ | + | :[[Genomes and gene products]] |
| − | |||
| − | |||
:[[Protein networks, metabolic pathways, flux control analysis]] | :[[Protein networks, metabolic pathways, flux control analysis]] | ||
| − | |||
:[[Phenotypic effects]] | :[[Phenotypic effects]] | ||
| − | |||
:[[Physio-pathologies]] | :[[Physio-pathologies]] | ||
| − | |||
:[[Interaction host-pathogens]] | :[[Interaction host-pathogens]] | ||
| − | |||
:[[Mimetism of genetic mutations by low-weight compounds]] | :[[Mimetism of genetic mutations by low-weight compounds]] | ||
| − | |||
:[[Environmental Factors]] | :[[Environmental Factors]] | ||
| − | |||
:[[Enzymology]] | :[[Enzymology]] | ||
| − | |||
:[[Pharmacology]] | :[[Pharmacology]] | ||
| − | |||
:[[Medicinal Chemistry]] | :[[Medicinal Chemistry]] | ||
| − | + | ==== Challenges==== | |
| − | ==== | ||
| − | |||
| − | |||
| − | |||
:[[Multi-scale analysis and modelling from molecules to populations]] | :[[Multi-scale analysis and modelling from molecules to populations]] | ||
| − | |||
| − | |||
| − | |||
:[[Modelling of noisy systems: intrinsic, experimental error]] | :[[Modelling of noisy systems: intrinsic, experimental error]] | ||
| − | |||
:[[Reconstruction of multi-scale dynamics]] | :[[Reconstruction of multi-scale dynamics]] | ||
| − | |||
:[[Emergence and immergence processes]] | :[[Emergence and immergence processes]] | ||
| − | |||
:[[Deterministic versus random factors]] | :[[Deterministic versus random factors]] | ||
| − | :[[ | + | :[[Perturbations and robustness of complex systems]] |
| + | :[[Comparison of complex systems: 'similarity'/'distance' measures]] | ||
:[[Designing Artificial Complex Systems: "synthetic biology"]] | :[[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]] | [[Notes]] | ||
Version actuelle datée du 13 novembre 2013 à 09:08
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).
- 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.