New Methods for Computational Strain Design

Computational strain design is a subfield of metabolic engineering which uses mathe­matical models and dedicated algorithms to identify metabolic intervention strategies optimizing the production performance of micro­bial strains. In particular, stoichiometric and constraint-based modeling approaches have been used to rationally design microbial cell factories. In my talk I will present recent developments in this field achieved by us and our collaborators:

1. Minimal cut set (MCS) based algorithms for the enumeration of suitable intervention strategies in genome-scale metabolic networks [1].
2. Generalization of MCS to regulatory MCS allowing combinations of gene/reaction knockouts with flux up- and downregulations [2].
3. Metabolic network properties enabling growth-coupled product synthesis [3]. We found that a growth-coupled production of almost all metabolites in E. coli is possible.
4. By a rigorous use of the above methods in a realistic application we designed an E. coli strain for high-yield production of itaconic acid [4]. In a fed-batch cultivation, this strain produced 32 g/l itaconic acid with an overall yield of 0.68 mol/(mol glucose) and a peak productivity of 0.45 g/l/h. These values are by far the highest that have ever been achieved for heterologous itaconic acid production.

References:
[1] von Kamp A, Klamt S (2014) Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Computational Biology 10(1):e1003378.
[2] Mahadevan R, von Kamp A, Klamt S (2015) Genome-scale strain designs based on regulatory minimal cut sets. Bioinformatics 31:2844-2851.
[3] Klamt S, Mahadevan R (2015) On the feasibility of growth-coupled product synthesis in microbial strains. Metabolic Engineering 30:166-178.
[4] Harder B-J, Bettenbrock K, Klamt S (2016) Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Under revision.