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Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

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Abstract

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

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  1. Genomatica, Inc., 5405 Morehouse Drive, Suite 210, 92130, San Diego, CA, USA

    Radhakrishnan Mahadevan, Anthony P. Burgard, Iman Famili, Steve Van Dien & Christophe H. Schilling

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  1. Radhakrishnan Mahadevan
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Mahadevan, R., Burgard, A.P., Famili, I. et al. Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals. Biotechnol. Bioprocess Eng. 10, 408–417 (2005). https://doi.org/10.1007/BF02989823

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