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

Area of Science:

• Metabolic Engineering and Synthetic Biology
• Biochemical Engineering
• Systems Biology

Background:

• Cellular metabolism must accommodate synthetic pathways, which impact energy and electron homeostasis.
• Microorganisms balance key co-factors such as adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NAD(P)H) for cellular function.
• Co-factor balance is critical for optimizing biotechnological performance in engineered microorganisms.

Purpose of the Study:

• To investigate the network-wide effects of butanol and butanol precursor production pathways on product yield.
• To develop and assess a novel algorithm for evaluating co-factor balance in metabolic engineering.
• To identify strategies for improving pathway and host selection in biocatalyst design.

Main Methods:

• Stoichiometric modeling, including Flux Balance Analysis (FBA), parsimonious FBA (pFBA), Flux Variability Analysis (FVA), and Minimization of Metabolic Adjustment (MOMA).
• Utilized the Escherichia coli (E.coli) core stoichiometric model.
• Developed a FBA-based Co-factor Balance Assessment (CBA) algorithm to track ATP and NAD(P)H pool dynamics.

Main Results:

• Stoichiometric models, when unconstrained, predicted unrealistic futile co-factor cycles and excessive flux flexibility compared to experimental data (13C-metabolic flux analysis).
• Constraining models to minimize futile cycling diverted surplus energy and electrons towards biomass formation, impacting theoretical yield.
• Optimal theoretical yields are achieved with balanced pathways that minimize diversion of surplus resources, emphasizing the interconnectedness of co-factor balancing (ATP, NAD(P)H, AMP, ADP).

Conclusions:

• Co-factor imbalance significantly impacts biotechnological performance and must be accounted for in metabolic engineering.
• The developed CBA algorithm aids in revealing sources of co-factor imbalance, facilitating pathway and host selection.
• Effective biocatalyst design requires considering the coordinated balance of multiple co-factors, not just ATP and NAD(P)H in isolation.