# A method for the generation of standardized qualitative dynamical systems of regulatory networks.

## Détails

ID Serval

serval:BIB_108747AA33F9

Type

**Article**: article d'un périodique ou d'un magazine.

Collection

Publications

Fonds

Titre

A method for the generation of standardized qualitative dynamical systems of regulatory networks.

Périodique

Theoretical Biology and Medical Modelling

ISSN

1742-4682 (Electronic)

ISSN-L

1742-4682

Statut éditorial

Publié

Date de publication

2006

Volume

3

Pages

13

Langue

anglais

Résumé

BACKGROUND: Modeling of molecular networks is necessary to understand their dynamical properties. While a wealth of information on molecular connectivity is available, there are still relatively few data regarding the precise stoichiometry and kinetics of the biochemical reactions underlying most molecular networks. This imbalance has limited the development of dynamical models of biological networks to a small number of well-characterized systems. To overcome this problem, we wanted to develop a methodology that would systematically create dynamical models of regulatory networks where the flow of information is known but the biochemical reactions are not. There are already diverse methodologies for modeling regulatory networks, but we aimed to create a method that could be completely standardized, i.e. independent of the network under study, so as to use it systematically.

RESULTS: We developed a set of equations that can be used to translate the graph of any regulatory network into a continuous dynamical system. Furthermore, it is also possible to locate its stable steady states. The method is based on the construction of two dynamical systems for a given network, one discrete and one continuous. The stable steady states of the discrete system can be found analytically, so they are used to locate the stable steady states of the continuous system numerically. To provide an example of the applicability of the method, we used it to model the regulatory network controlling T helper cell differentiation.

CONCLUSION: The proposed equations have a form that permit any regulatory network to be translated into a continuous dynamical system, and also find its steady stable states. We showed that by applying the method to the T helper regulatory network it is possible to find its known states of activation, which correspond the molecular profiles observed in the precursor and effector cell types.

RESULTS: We developed a set of equations that can be used to translate the graph of any regulatory network into a continuous dynamical system. Furthermore, it is also possible to locate its stable steady states. The method is based on the construction of two dynamical systems for a given network, one discrete and one continuous. The stable steady states of the discrete system can be found analytically, so they are used to locate the stable steady states of the continuous system numerically. To provide an example of the applicability of the method, we used it to model the regulatory network controlling T helper cell differentiation.

CONCLUSION: The proposed equations have a form that permit any regulatory network to be translated into a continuous dynamical system, and also find its steady stable states. We showed that by applying the method to the T helper regulatory network it is possible to find its known states of activation, which correspond the molecular profiles observed in the precursor and effector cell types.

Mots-clé

Cell Differentiation/physiology, Computer Simulation, Feedback, Physiological/physiology, Models, Biological, Signal Transduction/physiology, Software, Th1 Cells/metabolism, Th2 Cells/metabolism

Pubmed

Web of science

Open Access

Oui

Création de la notice

18/10/2012 8:30

Dernière modification de la notice

20/08/2019 12:37