A review of computational models of mammalian cell cycle

Abstract

Cell cycle, which comprises an ordered sequence of phases (G1, S, G2, and M) that leads to growth and division of a cell, is an essential part of life and its malfunction may cause formation of tumors and cancer. Therefore, study of cell cycle system has been the topic of many computational modelling research studies. In this paper, a thorough review of all modelling methods for mammalian cell cycle and corresponding models are presented. Due to its high complexity, mammalian cell cycle system has been less modelled than other organisms, such as yeast. Majority of models have investigated various parts of mammalian cell cycle, with few covering the whole system including all the phases. There are four main modelling types (discrete, deterministic continuous, stochastic continuous, and hybrid) that enable researchers to explore and understand system properties, such as dynamics of key regulators, oscillation behaviors, feedback loops, etc. Discrete models provide an abstract view of the system where different nodes interact with each other based on discrete logic. On the other hand, continuous models usually utilize Ordinary Differential Equations (ODEs) to incorporate continuous dynamics of the system elements (i.e., protein concentrations). The effect of noise in biological systems has been modelled through stochastic models. Hybrid models combine aforementioned modelling methods to overcome limitations of individual methods. The paper covers all the above methods highlighting their strengths and weaknesses and presents some open questions as promising future prospects for modelling cell cycle.