Journal Article

Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications

Fong, Stephen S., Yan, Qiang, Tang, Yinjie J., Wu, Gang, Koffas, Mattheos A.G., Jones, J. Andrew

2016

Overview

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. To commercialize recombinant organisms for renewable chemical production, it is essential to characterize the cost and benefit of metabolic burden using metabolic flux analysis tools. Genome-scale modeling can incorporate 13C-fluxome information and machine learning to predict the metabolic burden of synthetic biology modules. Modularized expression of native or recombinant pathways using a variety of experimental tools for controlling expression can substantially reduce the metabolic burden introduced by these pathways. The development of a standard synthetic-biology publication database may allow the use of machine learning or artificial intelligence to harness past knowledge for future rational design. Detailed computational methods have been developed to model macromolecule synthesis (DNA, RNA, proteins) to account for the maintenance costs associated with basal cellular function. Systems-level dynamic simulations and design algorithms can inform new approaches to engineering microbial production strains.

• Publisher: Elsevier Ltd,Elsevier Limited,Elsevier
• Subject: 13C-MFA / adenosine triphosphate / artificial intelligence / Bacterial Physiological Phenomena / Bioreactors - microbiology / Biosynthesis / Carbon / Carbon - metabolism / cell growth / chromosomal engineering / E coli / energy / Energy Metabolism - physiology / Enzymes / Genetic engineering / Genetic Enhancement - methods / genome-scale model / Glucose / Internal Medicine / machine learning / metabolic engineering / Metabolic Engineering - methods / Metabolic Flux Analysis - methods / Metabolic Networks and Pathways - physiology / Metabolism / Metabolome - physiology / Models, Biological / Mutation / Nutrient utilization / physiological response / Physiological responses / Physiology / Plasmids / prediction / Productivity / Respiration / Scale models / Synthetic biology / Synthetic Biology - methods