A Checkpoint-Orientated Model to Simulate Unconstrained Proliferation of Cells Jonathan Pascalie 1,2 , Val´ erie Lobjois 2 , Herv´ e Luga 1 , Bernard Ducommun 2,3 and Yves Duthen 1 1 CNRS - IRIT-UMR5505 - University of Toulouse, France {pascalie;luga;duthen}@irit.fr 2 CNRS - ITAV-UMS3039 - University of Toulouse,France {valerie.lobjois;bernard.ducommun}@itav-recherche.fr 3 CHU de Toulouse - F-31059 Toulouse, France Abstract In this paper we propose a new computational model of cell cycle to study the dynamics of cells population in 2- D monolayer culture. Whereas most of the models are phase-orientated our model deals with a checkpoint orien- tated paradigm and uses the phase orientation as an output to provide the biologists with a relevant view of the simula- tion result. Through this paper we will present the genericity of our model, able to reproduce the exponential growth phase of different cellular processes. Introduction Exploring, designing, understanding the complexity of the living world is of tremendous importance. The accurate as- sessment of its malfunction, especially those related to hu- man diseases is a high stake venture. In silico simulation provides new means of studying and exploring living sys- tems. In complementarity with experiments or when they are difficult to address in vitro, virtual environments can prove to be of interest. The latest computation capacity explosion allows us to tackle these questions with new ap- proaches and new methods. System modelling may there- fore use fitted methodologies to represent living systems at a systemic level. To this aim, the bottom-up approach tends to be the general paradigm for system modelling, focusing on each functional component of the system and in their in- teractions. Cancer is often considered as the result of perturbation in cell cycle regulation associated with mutations that can ap- pear in key regulators that result in abnormal proliferation, leading to tumorogenesis. Increasing the understanding of the cell cycle control is therefore central in cancer research and there are high issues in finding new regulatory mecha- nisms. The pharmacological issues foreseen with the in sil- ico simulation of cellular systems let think that prospective research of new therapies could be addressed in silico. In the different fields of computational and molecular bi- ology, the focus on aspects of the cell cycle differs. Molecu- lar biology models focus on the modelling and simulation of the molecular regulatory network of cycline-dependent ki- nase (CDK) (Novak and Tyson, 2004). These models can be classified into two kinds of models, the discrete and the continuous. Continuous models basically describe the evo- lution of concentration of proteins using a set of ordinary differential equations, whereas discrete models focus on the activation state of each regulatory protein thanks to a prede- fined genetic regulatory network (GRNs) (Kauffman, 1969; Chavoya and Duthen, 2008). These models have been com- monly used to simulate the cell cycle in yeast (Chen et al., 2004; Novak et al., 2001), frog eggs (Novak and Tyson, 1993; Pomerening et al., 2005), fruit flies (Calzone et al., 2007) and different mamalians cells (Aguda and Tang, 1999; Singhania et al., 2011). These models are molecular-based models and do not account for behavioural considerations at a macro-level, their aims being to focus on the regulatory mechanisms. The other family of models used to simulate cell prolifera- tion are called Individual Cell-Based Models (IBMs) (Loef- fler and Roeder, 2004). These are a subset of the agent-based models. Agent-based models have mainly proved their rel- evance in the simulation of different complex systems from social networks to the social behaviour of hive insects. Basi- cally, individual cell based models come under two classes: cellular automaton (CA) models and off lattice models. On the one hand, CA are described by a discretization of the proliferative environment in 2-D/3-D evolution grid, and the cell shape is reduced to a lattice site. In this case, cell be- haviour is composed of the different update rules set up (Pa- tel et al., 2001; Moreira and Deutsch, 2002) . On the other hand, off-lattice models have the advantages of letting evolv- ing cells in a continuous media with continuous shapes. They can introduce topological aspects based on in vitro ob- servation or knowledge. This involves high stakes for some investigative considerations. The IBMs have been success- fully used to study the pattern formation in multicellular cultures (Galle et al., 2005; Gerlee and Anderson, 2007), avascular tumour growth (Hoehme and Drasdo, 2010) and the spatio-temporal organisation of tissues (Meineke et al., 2001; Drasdo and Loeffler, 2001). These models generally consider the cell cycle as a single time unit decision and the update frequency is the global scheduler of the cell cycle.