Alexandre Tremblay

As the world recognizes the need for more sustainable practices, greater effort has been put into improving the economic viability of bioprocesses. Fields like metabolic engineering explore strategies to improve the production of bioproducts of industrial relevance, such as the use of alternative carbon sources. Regarding the performance assessment of a bioprocess, the product yield can be helpful, but other parameters like product titer and productivity (rate of production) are just as important. Furthermore, the highest performance may not come from a scenario with the highest yield, titer or productivity, but from a titer, rate, yield (TRY) set that maximizes profitability when the production costs are considered. Our previous work introduced the DySEEP approach, which uses an economic metric based on TRY parameters for evaluation of the potential of bioprocesses. This work expands the DySEEP program in key areas such as adding the possibility of exploring fed-batch processes and using strain design algorithms. Taking batch and fed-batch poly-3-hydroxybutyrate (PHB) production with E. coli as the case study, the scenarios that would lead to economic viability and the theoretical maximum performance for three different carbon sources, glucose, glycerol, and xylose, were identified. The results were then set in a strain design algorithm using Minimal Cut Sets (MCS) to find genetic interventions that could achieve the desired performance. The genetic interventions found with the MCS algorithm successfully led to strains that achieve the economic viability scenario, based on in silico simulations. This work illustrates how the expanded approach can be used to guide metabolic engineering strategies to improve the production of important bioproducts.

An organism’s survival hinges on maintaining the right thermodynamic conditions. Osmotic constraints limit the concentration range of metabolites, affecting essential cellular pathways. Despite extensive research on osmotic stress and growth, understanding remains limited, especially in hypo-osmotic environments. To delve into this, we developed a novel modeling approach that considers metabolic fluxes and metabolite concentrations along with thermodynamics. Our analysis of E. coli adaptation reveals insights into growth rates, metabolic pathways, and thermodynamic bottlenecks during transitions between hypo- and hyper-osmotic conditions. Both experimental and computational findings show that cells prioritize pathways that have higher thermodynamic driving force, like the pentose phosphate or the Entner–Doudoroff pathway, under low osmolarity. This work offers a systematic and mechanistic explanation for reduced growth rates in hypo- and hyper-osmotic conditions. The developed framework is the first of its kind to incorporate genome wide constraints that consider both natural logarithm and actual metabolite concentrations. Abstract Figure: