%0 Journal Article %1 Maia:2016:Microbiol-Mol-Biol-Rev:26609052 %A Maia, P %A Rocha, M %A Rocha, I %D 2016 %J Microbiol Mol Biol Rev %K in-silico metabolic-engineering metabolic-networks %N 1 %P 45-67 %R 10.1128/MMBR.00014-15 %T In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories %U https://www.ncbi.nlm.nih.gov/pubmed/26609052 %V 80 %X Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

@article{Maia:2016:Microbiol-Mol-Biol-Rev:26609052, abstract = {Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed. }, added-at = {2019-09-28T11:21:21.000+0200}, author = {Maia, P and Rocha, M and Rocha, I}, biburl = {https://www.bibsonomy.org/bibtex/2d3f584606b49caea32918648d5788c09/karthikraman}, description = {In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories. - PubMed - NCBI}, doi = {10.1128/MMBR.00014-15}, interhash = {4aece15d2221082d3033fc61e5ede28a}, intrahash = {d3f584606b49caea32918648d5788c09}, journal = {Microbiol Mol Biol Rev}, keywords = {in-silico metabolic-engineering metabolic-networks}, month = mar, number = 1, pages = {45-67}, pmid = {26609052}, timestamp = {2019-09-28T11:21:21.000+0200}, title = {In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories}, url = {https://www.ncbi.nlm.nih.gov/pubmed/26609052}, volume = 80, year = 2016 }