Skip to main navigation Skip to search Skip to main content

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
  • Virginia Commonwealth University
  • Rensselaer Polytechnic Institute
  • Washington University St. Louis

Research output: Contribution to journalReview articlepeer-review

576 Scopus citations

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), 13C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.

Original languageEnglish
Pages (from-to)652-664
Number of pages13
JournalTrends in Biotechnology
Volume34
Issue number8
DOIs
StatePublished - Aug 1 2016

Keywords

  • C-MFA
  • chromosomal engineering
  • genome-scale model
  • machine learning

Fingerprint

Dive into the research topics of 'Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications'. Together they form a unique fingerprint.

Cite this