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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks

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Author(s):
Antoneli, Fernando [1] ; Ferreira, Renata C. [2] ; Briones, Marcelo R. S. [3, 4]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Paulo UNIFESP, Escola Paulista Med EPM, Dept Informat Saude, Sao Paulo, SP - Brazil
[2] Penn State Univ, Coll Med, Hershey, PA - USA
[3] Univ Fed Sao Paulo UNIFESP, Escola Paulista Med EPM, Dept Microbiol Imunol & Parasitol, Sao Carlos, SP - Brazil
[4] Univ Fed Sao Paulo, Lab Genom Evolut & Biocomplexidade, EPM, Ed Pesquisas 2, Rua Pedro Toledo 669, BR-04039032 Sao Paulo - Brazil
Total Affiliations: 4
Document type: Journal article
Source: MATHEMATICAL BIOSCIENCES; v. 276, p. 82-100, JUN 2016.
Web of Science Citations: 3
Abstract

Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs. (C) 2016 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 13/07838-0 - Mitochondrial microdiversity of Candida albicans and its implications in hospital-acquired infections and patterns of mitochondrial genome macroevolution
Grantee:Marcelo Ribeiro da Silva Briones
Support type: Regular Research Grants
FAPESP's process: 08/04531-2 - Dynamical properties of multi-gene models applied to the regulatory network of Saccharomyces cerevisiae
Grantee:Francisco de Assis Ribas Bosco
Support type: Regular Research Grants