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Escherichia coli exposed to industrial scale heterogeneous mixing conditions respond on external stress by initiating short-term metabolic and long-term strategic transcriptional programs. In native habitats, long-term strategies allow to survive severe stress but are of limited use in large bioreactors where micro environmental 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 takorsfinally allowing to predict additional ATP needs of E. 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 28h. 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 - 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% mRNA upregulation in the upper region of the bioreactor. The phenomenon reflects the time-delayed regulatory response of the cells that propagates through the stirred tank. After 4.2 h cells adapt to environmental changes but still have to bear additional 6% ATP-demand. This article is protected by copyright. All rights reserved.
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Biotechnology_and_bioengineering
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Data-Driven In-silico Prediction of Regulation Heterogeneity and ATP Demands of Escherichia coli in Large-scale Bioreactors.
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