(547d) In Silico Model-Based Cofactor Engineering for Strain Improvement

Cofactors such as NAD(H) and NADP(H) provide the redox carriers for several metabolic reactions, both anabolic and catabolic, and serve as mediators in energy transfer across the cells.  Thus, establishment of overall redox cofactor balance is considered to be one of the key steps in metabolic engineering for achieving the desired cellular physiology. Of several methods to manipulate the intracellular cofactor regeneration rates, altering the cofactor specificity of a particular enzyme is a promising method. However, the identification of relevant enzyme targets for cofactor engineering (CE) is often very difficult and labor intensive. Therefore, development of more systematic approaches to find the appropriate CE targets using the genome-scale in silico metabolic modeling and analysis is highly required. In this regard, herein we propose a novel mathematical framework, cofactor modification analysis (CMA) for the systematic identification of suitable CE targets while considering the global metabolic effects upon cofactor balancing. Subsequently, we demonstrate the applicability of the CMA algorithm by applying it to E. coli via its genome-scale metabolic model iJO1366, thereby identifying the growth-coupled CE targets for overproducing four of its natural products: ethanol, lactate, formate and acetate, and three non-natural products: lycopene, 1,3-propanediol and 1-butanol. In addition, we also demonstrate the use of in silico protein redesign techniques to test the feasibility of altering the cofactor specificity from NAD(H) to NADP(H) or vice-versa before it could be experimentally validated. In general, this study outlines an in silico model-driven approach for improving the overall redox balance of host strain to achieve the preferred bioprocess objective.