%0 Journal Article %1 Long2015Computational %A Long, Matthew R. %A Ong, Wai Kit K. %A Reed, Jennifer L. %D 2015 %J Current opinion in biotechnology %K in-silico metabolic-engineering review %P 135--141 %R 10.1016/j.copbio.2014.12.019 %T Computational methods in metabolic engineering for strain design. %U http://dx.doi.org/10.1016/j.copbio.2014.12.019 %V 34C %X Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms. Copyright \copyright 2015 Elsevier Ltd. All rights reserved.

@article{Long2015Computational, abstract = {Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms. Copyright {\copyright} 2015 Elsevier Ltd. All rights reserved.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Long, Matthew R. and Ong, Wai Kit K. and Reed, Jennifer L.}, biburl = {https://www.bibsonomy.org/bibtex/2f26d1b8eade218802664b69484c58a71/karthikraman}, citeulike-article-id = {13503167}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.copbio.2014.12.019}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/25576846}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=25576846}, day = 7, doi = {10.1016/j.copbio.2014.12.019}, interhash = {e3c60d314c7f8377e61c9468daf4eb59}, intrahash = {f26d1b8eade218802664b69484c58a71}, issn = {1879-0429}, journal = {Current opinion in biotechnology}, keywords = {in-silico metabolic-engineering review}, month = jan, pages = {135--141}, pmid = {25576846}, posted-at = {2015-01-28 15:56:13}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Computational methods in metabolic engineering for strain design.}, url = {http://dx.doi.org/10.1016/j.copbio.2014.12.019}, volume = {34C}, year = 2015 }