Implicit methods for qualitative modeling of gene regulatory networks.

Détails

ID Serval
serval:BIB_F1E16224E4AB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Implicit methods for qualitative modeling of gene regulatory networks.
Périodique
Methods in Molecular Biology
Auteur(s)
Garg A., Mohanram K., De Micheli G., Xenarios I.
ISSN
1940-6029 (Electronic)
ISSN-L
1064-3745
Statut éditorial
Publié
Date de publication
2012
Volume
786
Pages
397-443
Langue
anglais
Résumé
Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.
Mots-clé
Cell Differentiation, Computational Biology, Gene Expression Regulation/genetics, Gene Regulatory Networks/genetics, Models, Genetic, Proteins/genetics, Proteins/metabolism, Signal Transduction, T-Lymphocytes, Helper-Inducer/cytology, T-Lymphocytes, Helper-Inducer/metabolism
Pubmed
Web of science
Création de la notice
17/10/2012 10:23
Dernière modification de la notice
20/08/2019 16:19
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