%0 Journal Article %1 Wu2016Metabolic %A Wu, Gang %A Yan, Qiang %A Jones, J. Andrew %A Tang, Yinjie J. %A Fong, Stephen S. %A Koffas, Mattheos A. %D 2016 %J Trends in biotechnology %K flux-analysis metabolic-engineering review %R 10.1016/j.tibtech.2016.02.010 %T Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications. %U http://dx.doi.org/10.1016/j.tibtech.2016.02.010 %X 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. Copyright \copyright 2016 Elsevier Ltd. All rights reserved.

@article{Wu2016Metabolic, 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. Copyright {\copyright} 2016 Elsevier Ltd. All rights reserved.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Wu, Gang and Yan, Qiang and Jones, J. Andrew and Tang, Yinjie J. and Fong, Stephen S. and Koffas, Mattheos A.}, biburl = {https://www.bibsonomy.org/bibtex/2e35f0584d49d31c6cc90c8132b6565a0/karthikraman}, citeulike-article-id = {14089597}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.tibtech.2016.02.010}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/26996613}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=26996613}, day = 17, doi = {10.1016/j.tibtech.2016.02.010}, interhash = {a20016f7d41f7eb56d1f969f385cb3c1}, intrahash = {e35f0584d49d31c6cc90c8132b6565a0}, issn = {1879-3096}, journal = {Trends in biotechnology}, keywords = {flux-analysis metabolic-engineering review}, month = mar, pmid = {26996613}, posted-at = {2016-07-05 08:20:33}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications.}, url = {http://dx.doi.org/10.1016/j.tibtech.2016.02.010}, year = 2016 }