10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications.

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.

          Related collections

          Author and article information

          Journal
          Trends Biotechnol.
          Trends in biotechnology
          Elsevier BV
          1879-3096
          0167-7799
          Aug 2016
          : 34
          : 8
          Affiliations
          [1 ] Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
          [2 ] Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284-3028, USA.
          [3 ] Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
          [4 ] Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA. Electronic address: yinjie.tang@wustl.edu.
          [5 ] Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284-3028, USA. Electronic address: ssfong@vcu.edu.
          [6 ] Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180; Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. Electronic address: koffam@rpi.edu.
          Article
          S0167-7799(16)00044-5
          10.1016/j.tibtech.2016.02.010
          26996613
          c6eab5dd-810e-456a-95ec-b8a30a49e6d5
          History

          (13)C-MFA,chromosomal engineering,genome-scale model,machine learning

          Comments

          Comment on this article