A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models

• Published in: Journal of membrane computing Vol. 2; no. 3; pp. 207 - 226
• Main Authors: Barbuti, Roberto, Gori, Roberta, Milazzo, Paolo, Nasti, Lucia
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.10.2020
• Subjects: Artificial Intelligence; Computation by Abstract Devices; Computational Biology/Bioinformatics; Computer Science; Optimization; Survey Paper; Theory of Computation; Gene regulatory networks; Petri nets; Membrane systems; Boolean networks; Reaction systems
• ISSN: 2523-8906; 2523-8914
• DOI: 10.1007/s41965-020-00046-y

Gene Regulatory Networks (GRNs) represent the interactions among genes regulating the activation of specific cell functionalities, such as reception of (chemical) signals or reaction to environmental changes. Studying and understanding these processes is crucial: they are the fundamental mechanism at the basis of cell functioning, and many diseases are based on perturbations or malfunctioning of some gene regulation activities. In this paper, we provide an overview on computational approaches to GRN modelling and analysis. We start from the biological and quantitative modelling background notions, recalling differential equations and the Gillespie’s algorithm. Then, we describe more in depth qualitative approaches such as Boolean networks and some computer science formalisms, including Petri nets, P systems and reaction systems. Our aim is to introduce the reader to the problem of GRN modelling and to guide her/him along the path that goes from classical quantitative methods, through qualitative methods based on Boolean network, up to some of the most relevant qualitative computational methods to understand the advantages and limitations of the different approaches.