Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-Stage Cancer

摘要: There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm investigate early-stage cancer interactions with immune system. It does not suffer from some limitations ordinary differential equation models, such lack stochasticity, representation individual behaviours rather than aggregates memory. In this paper we contribution when contrasted stochastic versions ODE models using examples. We seek answers following questions: (1) Does new formulation produce similar results version? (2) Can these methods used interchangeably? (3) Do outcomes reveal any benefit compared Gillespie results? To answer research questions three well-established mathematical describing between tumour cells elements. These case studies were re-conceptualised under perspective also converted algorithm formulation. Our interest in work, therefore, establish a methodological discussion usability different approaches, provide further biological insights into investigated studies. show that it possible obtain equivalent implement same mechanisms; however, incapacity retain memory past events affects similarity results. Furthermore, emergent behaviour ABMS produces extra patters system, which was obtained by algorithm.