Review of stochastic hybrid systems with applications in biological systems modeling and analysis

• Published in: EURASIP journal on bioinformatics & systems biology Vol. 2017; no. 1; p. 8
• Main Authors: Li, Xiangfang, Omotere, Oluwaseyi, Qian, Lijun, Dougherty, Edward R.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2017; Springer Nature B.V
• Subjects: Biological activity; Biological effects; Biological models (mathematics); Biomedical Engineering and Bioengineering; Computational Biology/Bioinformatics; Computer simulation; Engineering; Experimental methods; Gene expression; Gene regulation; Hybrid systems; Hypotheses; In vitro methods and tests; Modelling; Molecular interactions; Probability theory; Review; Self-propagating synthesis; Signal,Image and Speech Processing; Stochasticity; Systems analysis; Systems Biology; Biological systems modeling; Stochastic hybrid systems
• ISSN: 1687-4145; 1687-4153; 1687-4153
• DOI: 10.1186/s13637-017-0061-5

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.