Abstract
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.
Highlights
Gene Regulatory Networks (GRNs) [81] are the mechanism that allows cells to react to environmental changes such as the availability of a new nutrient or the reception of a signal from other cells
The paper is structured as follows: in Section 2 we provide the necessary biological background; in Section 3 we recall quantitative modelling approaches, which are based on standard chemical kinetic laws; in Section 4 we describe Boolean networks and their application to the modelling of GRNs; in Section 5 we survey the applications to GRNs of other computer science formalisms such as Petri nets [49, 69, 83], membrane systems [16, 76] and reaction systems [8]; and, in Section 6 we draw our conclusions
We have provided a survey of computational approaches to GRN modeling and analysis
Summary
Gene Regulatory Networks (GRNs) [81] are the mechanism that allows cells to react to environmental changes such as the availability of a new nutrient or the reception of a (chemical) signal from other cells. The aim of this paper is to provide a survey on computational approaches to GRN modelling and analysis, by starting from the biological and quantitative modelling background notions, and by describing more in depth qualitative approaches such as Boolean networks and some computer science formalisms. The paper is structured as follows: in Section 2 we provide the necessary biological background; in Section 3 we recall quantitative modelling approaches, which are based on standard chemical kinetic laws; in Section 4 we describe Boolean networks and their application to the modelling of GRNs; in Section 5 we survey the applications to GRNs of other computer science formalisms such as Petri nets [49, 69, 83], membrane systems [16, 76] and reaction systems [8]; and, in Section 6 we draw our conclusions
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