System modelling of mammalian cell cycle regulation using multi-level hybrid petri nets

Abstract

An important process in the growth of any biological organism is its ability to proliferate, a tightly controlled process in which a cell divides into two daughter cells. This happens within a dynamic environment, where a cell responds to various internal and external signals through a well-ordered sequence of events called cell cycle. Underlying these events are a complex and elegantly orchestrated web of interactions that function as an integrated system with various sub-systems that specialize in various tasks. The important task is the response of cell cycle machinery to proliferative signals in order to initiate a process which eventually leads to production of two daughter cells. Malfunction of cell cycle system causes diseases, such as cancer.

This study proposes a novel systems approach to modelling mammalian cell cycle to gain insights into how it coordinates such an intricate system of interactions in a robust and timely manner. It involves identification of the most essential controllers of mammalian cell cycle (as primary elements) and regulators of these controllers (as secondary elements) at different levels of abstraction to develop a minimal yet comprehensive model. In this paper, a Multi-Level Hybrid Petri Net (MLHPN), a graphical Petri Net-based modelling method, is proposed to model the mammalian cell cycle regulation system. Intuitive nature of MLHPN makes it possible to represent biological properties and processes with different time scales through a combination of continuous and discrete paradigms at different levels of abstraction. The goal is to gain a deep understanding of the mechanism of mammalian cell cycle regulation in the presence of growth factors.