Geometric analysis of pathways dynamics: Application to versatility of TGF-β receptors. (November 2016)
- Record Type:
- Journal Article
- Title:
- Geometric analysis of pathways dynamics: Application to versatility of TGF-β receptors. (November 2016)
- Main Title:
- Geometric analysis of pathways dynamics: Application to versatility of TGF-β receptors
- Authors:
- Samal, Satya Swarup
Naldi, Aurélien
Grigoriev, Dima
Weber, Andreas
Théret, Nathalie
Radulescu, Ovidiu - Abstract:
- Abstract: We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signalling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGF β signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptorsAbstract: We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signalling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGF β signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types. … (more)
- Is Part Of:
- Bio systems. Volume 149(2016:Nov.)
- Journal:
- Bio systems
- Issue:
- Volume 149(2016:Nov.)
- Issue Display:
- Volume 149 (2016)
- Year:
- 2016
- Volume:
- 149
- Issue Sort Value:
- 2016-0149-0000-0000
- Page Start:
- 3
- Page End:
- 14
- Publication Date:
- 2016-11
- Subjects:
- Tropical geometry -- Cancer systems biology -- Finite state automaton -- Metastability
Biological systems -- Periodicals
Biology -- Periodicals
Biology -- Periodicals
Evolution -- Periodicals
Biologie -- Périodiques
Évolution -- Périodiques
570 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03032647 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.biosystems.2016.07.004 ↗
- Languages:
- English
- ISSNs:
- 0303-2647
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2089.670000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 394.xml