The genome encodes thousands of genes whose pro ducts enable cell survival and numerous cellular func tions. The amounts and the temporal pattern in which these products appear in the cell are crucial to the pro cesses of life. Gene regulatory networks govern the levels of these gene products. A

We focus on gene regulatory networks an the robustness of such networks as the magni We investigate the robustness of biological perturbation (in rates and concentrations) th networks, emphasizing gene regulatory networks. carried Weout without changing the steady stat define the robustness of a

Estimation of Gene Regulatory Network (GRN) from gene expression time series data is a major research in Bioinformatics. One of the main estimation method is calculating the parameters of di®erential equation models. Various models such as S-system, Linear model, Weaver model, and SDE model

Gene regulatory networks (GRNs) are complex control systems that deal with the interaction of genes, which eventually control cellular processes at the protein level. The investigation of gene regulatory network will provide huge information on cellular processes and gene functions, and at last

et nf at ac l c oc ever, reconstruction of complex networks of genetic inter- most biological networks are unknown or incomplete. Li et al. EURASIP Journal on Bioinformatics and Systems Biology 2014, 2014:12

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This limited review is intended as an introduction to the fast growing subject of mathematical modelling of cell metabolism and its biochemical pathways, and more precisely on pathways linked to apoptosis of cancerous cells. Some basic mathematical models of chemical kinetics, with emphasis on stochastic models, are presented.

The complete genome sequence of Corynebacterium pseudotuberculosis FRC41 isolated from a 12-year-old girl with necrotizing lymphadenitis reveals insights into