Constraint-based modelling of metabolic networks incorporating resource allocation

Escherichia coli is among the most widely used host organisms for engineered functions due to its well-characterized genetics and physiology, ability to achieve high cell density, fast growth and high-yield nature. However, the expression of recombinant genes often imposes burdens on the host and triggers stress responses, which can lead to growth retardation and eventually low productivity of targeted products. Furthermore, it is ubiquitously observed that fast-growing E. coli secretes a significant amount of acetic acid (acetate) even in the presence of sufficient oxygen, a phenomenon known as the overflow metabolism. The overflow-mediated accumulation of acetate severely inhibits cell growth and constitutes one major obstacle for efficient protein productions.
Despite being extensively studied for decades, the underlying mechanism of the overflow metabolism remains murky until several recent studies shedding light from the perspective of resource allocation. Thanks to the evolving toolbox of systems biology, this PhD project utilized constraint-based modelling, i.e. genome-scale metabolic models (GEMs) and flux balance analysis (FBA) incorporating resource allocation principles to extend the knowledge on the phenotypic patterns and governing mechanisms of E. coli.
Work from the initial stage of this project showed that a concise pathway-level proteome allocation constraint enables the prediction of the metabolic shift from full respiration to the overflow metabolism in various wild-type E. coli grown under aerobic-glucose conditions. Besides, the control of carbon flux into pentose phosphate pathway was predicted to play an important role in the activation of the acetate pathway. The original wild-type model was further adapted to simulate batch cultures of recombinant strains. By modelling the heterologous burdens as increased maintenance energy and reduced proteomic resources, the revised model accurately predicted the dynamic change of cell growth, substrate uptake, by-product secretion and protein production. It also predicted a non-monotonic relationship between the recombinant protein productivity and the specific expression level of the target protein, where the maximum productivity emerged at a moderate expression level. This result indicated that the optimal protein production might rely on a delicate balance between the expression strength of the target gene and the growth rate of the host cell.
Furthermore, we extended the overflow metabolism-centred modelling framework to account for the protein cost of carbon transport. The updated model was able to simultaneously predict the maximal growth rate, the acetate production rate and the glucose uptake rate. Besides, by linking substrate uptake kinetics with the proteome allocation constraint, model simulations suggested that the huge variation of the supposed constant Monod kinetic parameter (Ks) could result from the switch in metabolic strategies.
Additional efforts were made to test the established metabolic models incorporating proteome allocation constraints in three practically relevant case studies, i.e. (i) reducing the overflow metabolism in NAD+-dependent formate-dehydrogenase recombinant E. coli; (ii) an engineered coculture of GFP recombinant E. coli and Acinetobacter baylyi ADP1 with acetate cross-feeding to improve GFP productivity and (iii) lactate-producing human mesenchymal stem cells (hMSCs) to study the potential driver of the Warburg effect. Simulation results of case (i) showed that the key to alleviating the overflow metabolism lies in the existence of alternative energy pathways with lower protein costs per ATP flux than that of the fermentation. Simulation results of case (ii) confirmed the experimental observation that the synthetic co-culture system increases the carbon utilisation and the overall GFP productivity. Finally, from case (iii), flux estimation using the experimentally determined lactate production rate-growth rate profile indicated that lactate production in hMSCs may not necessarily be coupled with a reduced respiration activity, which leads to the discussion of potential limitation of the proteome resource allocation.
Overall, the proposed metabolic models incorporating resource allocation constitute useful computational tools for the optimal design of cell factories. The applications of these models in this PhD thesis contribute to the improved understanding of how E. coli rewires its physiologies to cope with the ever-changing growth environments.

Keywords: proteome allocation
flux balance analysis
constraint-based modelling
metabolic network
genome-scale metabolic models
Advisor: Yang, A