Data‐driven in silico prediction of regulation heterogeneity and ATP demands of Escherichia coli in large‐scale bioreactors

Abstract

Escherichia coli exposed to industrial‐scale heterogeneous mixing conditions respond to external stress by initiating short‐term metabolic and long‐term strategic transcriptional programs. In native habitats, long‐term strategies allow survival in severe stress but are of limited use in large bioreactors, where microenvironmental conditions may change right after said programs are started. Related on/off switching of genes causes additional ATP burden that may reduce the cellular capacity for producing the desired product. Here, we present an agent‐based data‐driven model linked to computational fluid dynamics, finally allowing to predict additional ATP needs of Escherichia coli K12 W3110 exposed to realistic large‐scale bioreactor conditions. The complex model describes transcriptional up‐ and downregulation dynamics of about 600 genes starting from subminute range covering 28 h. The data‐based approach was extracted from comprehensive scale‐down experiments. Simulating mixing and mass transfer conditions in a 54 m3 stirred bioreactor, 120,000 E. coli cells were tracked while fluctuating between different zones of glucose availability. It was found that cellular ATP demands rise between 30% and 45% of growth decoupled maintenance needs, which may limit the production of ATP‐intensive product formation accordingly. Furthermore, spatial analysis of individual cell transcriptional patterns reveal very heterogeneous gene amplifications with hot spots of 50%-80% messenger RNA upregulation in the upper region of the bioreactor. The phenomenon reflects the time‐delayed regulatory response of the cells that propagate through the stirred tank. After 4.2 h, cells adapt to environmental changes but still have to bear an additional 6% ATP demand.