PhysiCelldFBA: Linking single-cell genome-scale metabolism to spatially explicit multicellular dynamics
Genome-scale metabolic models can predict how individual cells allocate resources and respond to their environment, yet few frameworks link single-cell metabolism to the spatial organisation of multicellular systems. Here we introduce PhysiCelldFBA, an extension of the PhysiCell agent-based framework that couples genome-scale dynamic flux balance analysis to off-lattice multicellular simulations.…
PhysiCelldFBA is a new method that links individual cell metabolism to the spatial organization of multicellular systems. This innovative approach builds upon the PhysiCell framework, which is an agent-based model, and couples it with dynamic flux balance analysis at the genome scale. Each simulated cell comes with its own metabolic model, enabling local environmental factors to influence metabolism, while the metabolic activity in turn impacts the surrounding environment, cellular behavior, and spatial organization.
To test the validity of this coupling, the researchers first focused on a closed E. coli system. They observed that glucose consumption, CO2 production, and biomass accumulation remained mass-balanced while the simulated biomass matched analytical predictions within a 1% margin of error. This demonstrated that the coupling of genome-scale metabolic models to spatially explicit multicellular dynamics is feasible.
Next, the researchers explored the emergence of metabolic phenotypes across various microbial and mammalian systems. In growing E. coli colonies, spatial nutrient gradients led to metabolic stratification and acetate cross-feeding. Diffusion-limited metabolism produced proliferative, hypoxic, and necrotic zones in a tumour-like tissue, with different metabolites exhibiting distinct metabolic profiles.
In a two-species consortium, distinct metabolic networks facilitated syntrophic cross-feeding and spatial niche formation. Additionally, the metabolic state of cells influenced energy availability, leading to transitions between cellular motility and growth.
PhysiCelldFBA offers a general framework for simulating genome-scale metabolism at the single-cell resolution level and linking intracellular metabolic state to cellular behavior and emergent organization across different scales. This method allows researchers to gain insights into the complex interactions between cellular metabolism and the spatial organization of multicellular systems, paving the way for a deeper understanding of various biological processes.
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