%0 Journal Article %1 Jungreuthmayer2018Designing %A Jungreuthmayer, Christian %A Gerstl, Matthias P. %A Pe\ na Navarro, David A. %A Hanscho, Michael %A Ruckerbauer, David E. %A Zanghellini, Jürgen %D 2018 %J Methods in molecular biology (Clifton, N.J.) %K elementary-modes in-silico metabolic-engineering metabolic-networks %P 371--387 %T Designing Optimized Production Hosts by Metabolic Modeling. %U http://view.ncbi.nlm.nih.gov/pubmed/29222763 %V 1716 %X Many of the complex and expensive production steps in the chemical industry are readily available in living cells. In order to overcome the metabolic limits of these cells, the optimal genetic intervention strategies can be computed by the use of metabolic modeling. Elementary flux mode analysis (EFMA) is an ideal tool for this task, as it does not require defining a cellular objective function. We present two EFMA-based methods to optimize production hosts: (1) the standard approach that can only be used for small and medium scale metabolic networks and (2) the advanced dual system approach that can be utilized to directly compute intervention strategies in a genome-scale metabolic model.

@article{Jungreuthmayer2018Designing, abstract = {Many of the complex and expensive production steps in the chemical industry are readily available in living cells. In order to overcome the metabolic limits of these cells, the optimal genetic intervention strategies can be computed by the use of metabolic modeling. Elementary flux mode analysis ({EFMA}) is an ideal tool for this task, as it does not require defining a cellular objective function. We present two {EFMA}-based methods to optimize production hosts: (1) the standard approach that can only be used for small and medium scale metabolic networks and (2) the advanced dual system approach that can be utilized to directly compute intervention strategies in a genome-scale metabolic model.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Jungreuthmayer, Christian and Gerstl, Matthias P. and Pe\ {n}a Navarro, David A. and Hanscho, Michael and Ruckerbauer, David E. and Zanghellini, J\"{u}rgen}, biburl = {https://www.bibsonomy.org/bibtex/2927931efede694c1ab4928aca653886b/karthikraman}, citeulike-article-id = {14510719}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/29222763}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=29222763}, interhash = {9da3c2e5781860159fe6dbe9946fcc79}, intrahash = {927931efede694c1ab4928aca653886b}, issn = {1940-6029}, journal = {Methods in molecular biology (Clifton, N.J.)}, keywords = {elementary-modes in-silico metabolic-engineering metabolic-networks}, pages = {371--387}, pmid = {29222763}, posted-at = {2018-01-01 09:58:57}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Designing Optimized Production Hosts by Metabolic Modeling.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/29222763}, volume = 1716, year = 2018 }