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

Gang Wu, Qiang Yan, J Andrew Jones, Yinjie J Tang, Stephen S Fong, Mattheos A G Koffas
Author Information
  1. Gang Wu: Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
  2. Qiang Yan: Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284-3028, USA.
  3. J Andrew Jones: 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. Yinjie J Tang: Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA. Electronic address: yinjie.tang@wustl.edu.
  5. Stephen S Fong: Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284-3028, USA. Electronic address: ssfong@vcu.edu.
  6. Mattheos A G Koffas: 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.

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.

Keywords

MeSH Term

Bacterial Physiological Phenomena
Bioreactors
Carbon
Energy Metabolism
Genetic Enhancement
Metabolic Engineering
Metabolic Flux Analysis
Metabolic Networks and Pathways
Metabolome
Models, Biological
Synthetic Biology

Chemicals

Carbon

Word Cloud

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