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Resolving Cell Composition Through Simple Measurements, Genome-Scale Modeling, and a Genetic Algorithm

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Book cover Systems Metabolic Engineering

Part of the book series: Methods in Molecular Biology ((MIMB,volume 985))

Abstract

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and “easy to measure” culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined 13C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.

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Authors and Affiliations

  1. Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA

    Ryan S. Senger & Hadi Nazem-Bokaee

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  1. Ryan S. Senger
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  2. Hadi Nazem-Bokaee
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Correspondence to Ryan S. Senger .

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Editors and Affiliations

  1. Cockrell School of Engineering, Dept. of Chemical Engineering, The University of Texas at Austin, E. Dean Keeton Street 200, Austin, 78712, Texas, USA

    Hal S. Alper

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Senger, R.S., Nazem-Bokaee, H. (2013). Resolving Cell Composition Through Simple Measurements, Genome-Scale Modeling, and a Genetic Algorithm. In: Alper, H. (eds) Systems Metabolic Engineering. Methods in Molecular Biology, vol 985. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-299-5_5

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  • DOI: https://doi.org/10.1007/978-1-62703-299-5_5

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-298-8

  • Online ISBN: 978-1-62703-299-5

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