title: Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications.
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.
authors.affiliation: Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284-3028, USA.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yan
authors.affiliation: 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.
authors.forename: J Andrew
authors.initials: JA
authors.lastname: Jones
authors.affiliation: Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA. Electronic address: yinjie.tang@wustl.edu.
authors.forename: Yinjie J
authors.initials: YJ
authors.lastname: Tang
authors.affiliation: Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284-3028, USA. Electronic address: ssfong@vcu.edu.
authors.forename: Stephen S
authors.initials: SS
authors.lastname: Fong
authors.affiliation: 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.
authors.forename: Mattheos A G
authors.initials: MAG
authors.lastname: Koffas
mesh_terms: D018407:Bacterial Physiological Phenomena; D019149:Bioreactors / Q000382:microbiology; D002244:Carbon / Q000378:metabolism*; D004734:Energy Metabolism / Q000502:physiology; D024861:Genetic Enhancement / Q000379:methods; D060847:Metabolic Engineering / Q000379:methods*; D064688:Metabolic Flux Analysis / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D055442:Metabolome / Q000502:physiology*; D008954:Models, Biological; D058615:Synthetic Biology / Q000379:methods*
chemical_list: D002244:Carbon
keywords: (13)C-MFA; chromosomal engineering; genome-scale model; machine learning