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Evaluating Factors That Influence Microbial Synthesis Yields by Linear Regression with Numerical and Ordinal Variables

Authors

  • Peter F. Colletti,

    1. Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211
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  • Yogesh Goyal,

    1. Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211
    Current affiliation:
    1. Department of Chemical Engineering, Indian Institute of Technology, Gandhinagar, Gujarat 382424, India.
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  • Arul M. Varman,

    1. Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211
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  • Xueyang Feng,

    1. Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211
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  • Bing Wu,

    1. Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211
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  • Yinjie J. Tang

    Corresponding author
    1. Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211
    • Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, Missouri 63130; telephone: 314-935-3441; fax: 314-935-7211.
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  • Peter F. Colletti and Yogesh Goyal contributed equally to this work.

Abstract

In the production of chemicals via microbial fermentation, achieving a high yield is one of the most important objectives. We developed a statistical model to analyze influential factors that determine product yield by compiling data obtained from engineered Escherichia coli developed within last 10 years. Using both numerical and ordinal variables (e.g., enzymatic steps, cultivation conditions, and genetic modifications) as input parameters, our model revealed that cultivation modes, nutrient supplementation, and oxygen conditions were the three significant factors for improving product yield. Generally, the model showed that product yield decreases as the number of enzymatic steps in the biosynthesis pathway increases (7–9% loss of yield per enzymatic step). Moreover, overexpression of enzymes or removal of competitive pathways (e.g., knockout) does not necessarily result in an amplification of product yield (P-value >0.1), possibly because of limited capacity in the biosynthesis pathway to accommodate an increase in flux. The model not only provides general guidelines for metabolic engineering and fermentation processes, but also allows a priori estimation and comparison of product yields under designed cultivation conditions. Biotechnol. Bioeng. 2011; 108:893–901. © 2010 Wiley Periodicals, Inc.

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