Modeling Spatio-Temporal Dynamics of Metabolic Networks with Cellular Automata and Constraint-Based Methods

Increasing experimental evidence suggests that the behaviour of multi-cellular systems, such as tissues and organs, might be largely driven by the complex interplay occurring among metabolic networks. Computational approaches are required to unravel this complexity. However, they currently deal with either the simulation of the spatial dynamics of cell populations or with the simulation of metabolism of individual cells. In order to integrate the modeling of these two key biological processes, we here introduce FBCA (Flux Balance Cellular Automata) a new multi-scale modeling framework that combines a cellular automaton representation of the (higher-level) spatial/morphological dynamics of multi-cellular systems, i.e., the Cellular Potts Model, with a model of the (lower-level) metabolic activity of individual cells, as modeled via Flux Balance Analysis. The representation via cellular automata allows to identify and analyze complex emergent properties and patterns of real-world multi-cellular systems, in a variety of distinct experimental settings. We here present preliminary tests on a simplified model of intestinal crypt, in which cell populations with distinct metabolic properties compete for space and nutrients. The results may allow to cast a new light on the mechanisms linking metabolic properties to clonal dynamics in tissues.