(586h) Identifying Thermodynamic Bottlenecks Caused By Osmotic Stress in the Genome-Scale Metabolic Model of Escherichia coli

Bacterial growth relies on a complex network of biochemical reactions governed by the laws of thermodynamics. Maintaining a proper thermodynamic driving force can be particularly difficult in environments with extreme osmolarity. In such conditions, bacteria are forced to secrete or import ions to maintain membrane integrity, affecting the total pool of available reactants within the cytoplasm. Our work explores the biochemical mechanisms that lead to the preservation of growth through osmotic stress. To do so, we developed a mixed-integer linear programming (MILP) constraint-based modelling approach that identifies thermodynamic bottlenecks in environments with different osmolarities. The algorithm is an extension to OptMDFpathway and includes a constraint that limits the total sum of metabolites available for growth. For all analyses, we used an adapted version of the genome-scale stoichiometric metabolic model of E. coli iML1515. We found that there exists an exponential relationship between growth and the max-min driving force, that there is a step-wise relationship between maximal growth and osmolarity and that the central carbon metabolism is disrupted during osmotic stress, forcing E. coli to use alternative pathways to maintain high growth. Overall, these results coincide with in vivo observations that find that the maximal growth rate is affected by osmotic stress and that survival depends on the accumulation of metabolites specific to the osmotic state of the cells. These insights are fundamental to microbiology and bioprocess engineering, especially in the context of biochemical production, which often relies on high-density cell culture, overexpression of proteins and rich media, all components that can lead to osmotic stress.