OptSwap optimizes microbial strain design for production-scale bioprocessing
Using a new in silico method called OptSwap scientists can predict how to engineer microorganisms to increase the yield of high-value biobased chemicals produced by industrial-scale cell factories. This example of how advanced computational tools are being applied to genome-scale metabolic modeling in microbes illustrates the important contributions from the field of Systems Biology, as highlighted in a special research section in Industrial Biotechnology, a peer-reviewed journal from Mary Ann Liebert Inc., publisher. The articles are available on the Industrial Biotechnology website.
The article "Optimizing Cofactor Specificity of Oxidoreductase Enzymes for the Generation of Microbial Production Strains—OptSwap," by Zachary King and Adam Feist, University of California, San Diego and Technical University of Denmark, Lyngby, describes the development of OptSwap. The authors identified microbial strain designs with significant advantages for the production of L-alanine, succinate, acetate, and D-lactate under the modeled conditions.
"The OptSwap method of King and Feist provides a great example of how systems approaches can enable more effective design and simulation of microbial strains," says Guest Editor Nathan D. Price, PhD, Associate Director, Institute of Systems Biology (Seattle, WA) and a member of the Industrial Biotechnology Editorial Board.
The IB IN DEPTH special section on Systems Biology includes review articles by Kieran Smallbone (University of Manchester, UK) and Pedro Mendes (Virginia Tech, Blacksburg): "Large-Scale Metabolic Models: From Reconstruction to Differential Equations"; Jacob Koskimaki, Anna Blazier, Andres Clarens, and Jason Papin (University of Virginia, Charlottesville): "Computational Models of Algae Metabolism for Industrial Applications"; Scott Harrison and Markus Herrgård (Technical University of Denmark, Hørsholm): "The Uses and Future Prospects of Metabolomics and Targeted Metabolite Profiling in Cell Factory Development"; and Manual Alberto Garcia-Albornoz and Jens Nielsen (Chalmers University of Technology, Göteborg, Sweden): "Application of Genome-Scale Metabolic Models in Metabolic Engineering."
Research articles by Hnin Aung, Susan Henry, and Larry Walker (Cornell University, Ithaca, NY): "Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism,"; and by Patrick Hyland and Radhakrishnan Mahadevan (University of Toronto, Canada) and Serene Lock-Sow Mun (Universiti Teknologi Petronas, Tronoh Malaysia): "Prediction of Weak Acid Toxicity in Saccharomyces cerevisiae Using Genome-Scale Metabolic Models" round out the special section.
Provided by Mary Ann Liebert, Inc
