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

摘要

The chemicals industry is a major contributor to greenhouse gas emissions and desperately requires more sustainable alternatives. Genetically engineered microorganisms can be used as 'bio-factories' to manufacture chemicals, replacing those currently sourced from fossil fuels or unsustainable tropical plant agriculture. However, due to the complexity of biology, the features that render one bio-factory design more efficient than others are difficult to identify. Computational modelling of such designs can enable the selection of optimally performing designs, but it remains challenging as biology is complex and not fully understood. Microorganisms require energy for their own growth and maintenance, but also to convert molecules into desired target products. The supply and consumption of such energy is through co-factors, and the balance of such co-factors influences the performance of the engineered bio-factories. This study developed a computer-aided approach for quantification of the co-factor balance of bio-factories. Using the chemical n-butanol as a case study, our study explores the impact of variant bio-factory designs with differing co-factor balance on the potential efficiency of biomanufacturing. We provide insights into the relative balance of different designs and provide a computational framework to select the best-performing designs.