Relationships between models of genetic regulatory networks with emphasis on discrete state stochastic models

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Genetic Regulatory Networks (GRNs) represent the interconnections between genomic entities that govern the regulation of gene expression. GRNs have been represented by various types of mathematical models that capture different aspects of the biological system. This chapter discusses the relationships among the most commonly used GRN models that can enable effective integration of diverse types of sub-models. A detailed model in the form of stochastic master equation is described, followed by it coarse-scale and deterministic approximations in the form of Probabilistic Boolean Networks and Ordinary Differential Equation models respectively.

Original languageEnglish
Title of host publicationData Analytics in Medicine
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI-Global
Pages226-248
Number of pages23
Volume1
ISBN (Electronic)9781799812050
ISBN (Print)9781799812043
DOIs
StatePublished - Dec 6 2019

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