Modeling of Gene Regulatory Networks Using State Space Models

Abstract

Modeling of gene regulatory networks is becoming popular to understand the complex molecular mechanism of gene regulation due to the availability of high throughput genomic data. Such techniques have encouraged the researchers to understand not only the structure of gene regulatory networks and proteomicnetworks but also gene-gene associations or interactions. State space models are a relatively new approach to infer gene regulatory networks. It has the unique feature of capturing the dynamicity of the gene regulation which is inherent to the biological networks as well as computationally efficiency. The nonlinear state space models also considered the non-linear associations between genes, which all linear modeling approaches fails to capture. Performance evaluation criteria for the approaches used for modeling genetic regulatory networks are also discussed.