A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network.

Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE

Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press,

Gene Regulatory Networks* ; Models, Genetic*; Computational Biology/methods ; Algorithms

Inference of gene regulatory network (GRN) from time series microarray data remains as a fascinating task for computer science researchers to understand the complex biological process that occurred inside a cell. Among the different popular models to infer GRN, S-system is considered as one of the promising non-linear mathematical tools to model the dynamics of gene expressions, as well as to infer the GRN. S-system is based on biochemical system theory and power law formalism. By observing the value of kinetic parameters of S-system model, it is possible to extract the regulatory relationships among genes. In this review, several existing intelligent methods that were already proposed for inference of S-system-based GRN are explained. It is observed that finding out the most suitable and efficient optimization technique for the accurate inference of all kinds of networks, i.e., in-silico, in-vivo, etc., with less computational complexity is still an open research problem to all. This paper may help the beginners or researchers who want to continue their research in the field of computational biology and bioinformatics.
(© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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Keywords: Bio-chemical system theory; Cardinality; Decoupling; Gene regulatory network; Microarray data; Optimization; Power law function; Regularization; S-system

Date Created: 20231006 Date Completed: 20231101 Latest Revision: 20231107

20231215

10.1007/978-1-0716-3461-5_8

37803116