Mapping metabolic networks to physical observables in space and time

The spatiotemporal development of metabolic networks underlies collective microbial behavior but has remained unexplored due to the previous lack of suitable datasets. In Bacillus subtilis swarms, dynamic gene expression drives localized metabolic adaptation during colony expansion. Leveraging newly available spatio-temporal RNA sequencing data from B. subtilis swarms, we construct and compare spatio-temporally resolved metabolic networks to identify metabolic transitions that reflect how upstream mechanisms influence metabolic reorganization over space and time. Gene expression fields are transformed through gene-protein-reaction rules to infer reaction presence and generate metabolic networks, which are refined through consistency and connectivity analyses. Relationships between metabolic transitions and physical observables from swarming are then examined, enabling the discovery of mappings between measurable phenotypic patterns and underlying metabolic organization. Our framework provides a foundation for investigating metabolism as a spatially and temporally evolving system.