The T Cells in an Ageing Virtual Mouse
The multiscale problem that a modeller in biology is presented with, trying to provide a systematic description of many agents, their properties, their internal dynamics and interactions, is daunting. On the other hand, biology provides a natural scale, with individual cells as agents. In agent-based computation, variables representing cell population sizes may be evaluated by counting cells of various types, but the governing dynamical rules are laid down one event at a time (J Theor Biol 231(3):357–376, 2004; CPT: Pharmacometrics Syst Pharmacol 4(11):615–629, 2015). Every cell is an individual, with its own set of attributes (state of activation, surface molecule profile, spatial location, for example). Populations of cells decrease or increase because individual cells die or divide. Here, by way of a tutorial on agent-based immune system modelling, we implement a model of the behaviour of the set of T cells in a body—numbering more than 1011 in an adult human, and more than 107 in an adult mouse (Ann Rev Immunol 28:275–294, 2010).
We are grateful for discussions with, and data provided by, Thea Hogan, Ben Seddon and Andy Yates. GDL thanks the Isaac Newton Institute programme Stochastic Dynamical Systems in Biology: Numerical Methods and Applications. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 317040 (QuanTI). This work has been partially supported by grants FIS2013-47949-C2-2-P (Spain), PIRSES-GA-2012-317893 (7th FP, EU), and BIOCAPS (FP7/REGPOT-2012- 2013.1, EC) under grant agreement no. 316265. MC thanks the Salvador de Madariaga programme through grant PRX16/00287.
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