In silico co-factor balance estimation using constraint-based modelling informs metabolic engineering in Escherichia coli.

In the growing field of metabolic engineering, where cells are treated as 'factories' that synthesize industrial compounds, it is essential to consider the ability of the cells' native metabolism to accommodate the demands of synthetic pathways, as these pathways will alter the homeostasis of cellular energy and electron metabolism. From the breakdown of substrate, microorganisms activate and reduce key co-factors such as ATP and NAD(P)H, which subsequently need to be hydrolysed and oxidized, respectively, in order to restore cellular balance. A balanced supply and consumption of such co-factors, here termed co-factor balance, will influence biotechnological performance. To aid the strain selection and design process, we used stoichiometric modelling (FBA, pFBA, FVA and MOMA) and the Escherichia coli (E.coli) core stoichiometric model to investigate the network-wide effect of butanol and butanol precursor production pathways differing in energy and electron demand on product yield. An FBA-based co-factor balance assessment (CBA) algorithm was developed to track and categorise how ATP and NAD(P)H pools are affected in the presence of a new pathway. CBA was compared to the balance calculations proposed by Dugar et al. (Nature Biotechnol. 29 (12), 1074-1078). Predicted solutions were compromised by excessively underdetermined systems, displaying greater flexibility in the range of reaction fluxes than experimentally measured by 13C-metabolic flux analysis (MFA) and the appearance of unrealistic futile co-factor cycles. With the assumption that futile cycles are tightly regulated in reality, the FBA models were manually constrained in a step-wise manner. Solutions with minimal futile cycling diverted surplus energy and electrons towards biomass formation. As an alternative, the use of loopless FBA or constraining the models with measured flux ranges were tried but did not prevent futile co-factor cycles. The results highlight the need to account for co-factor imbalance and confirm that better-balanced pathways with minimal diversion of surplus towards biomass formation present the highest theoretical yield. The analysis also suggests that ATP and NAD(P)H balancing cannot be assessed in isolation from each other, or even from the balance of additional co-factors such as AMP and ADP. We conclude that, through revealing the source of co-factor imbalance CBA can facilitate pathway and host selection when designing new biocatalysts for implementation by metabolic engineering.