An Overview on Computational Approaches to Modeling Mammalian Cell Cycle

Biological systems such as cell cycle are “complex systems “consisting of an enormous number of elements interacting in ways that produce nonlinear and complex systems behavior. Computational modelling a promising approach to study such systems. However, representing structural and functional complexity of these systems is a major challenge to these models that can range from simpler discrete models such as Boolean networks to more complex mathematical model. Modeling methods and techniques have become popular for modeling biological systems because they can provide a deep understanding and insight of the complex biological system issues. These techniques can also be used in prediction, diagnosis and treatment of diseases such as cancer. This paper overlays current and existing computational modeling approaches used in modeling mammalian cell cycle (discrete, continuous, stochastic and hybrid). In addition, it introduces a set of opened research questions related to cell cycle system. Furthermore, this paper exposes the pros and cons of the existing modelling approaches and presents a more flexible and intuitive fuzzy logic based system framework to modeling cell cycle system