%0 PDF %T Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design. %A Boghigian, Brett A.; Shi, Hai.; Lee, Kyongbum.; Pfeifer, Blaine A. %D 2016-08-16T18:22:29.807Z %8 2016-08-16 %R http://localhost/files/bc386w814 %X Background: Microbial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest.; Keywords: standard change in Gibbs free energy of formation, standard change in Gibbs free energy across a pathway/EM, standard change in Gibbs free energy across a reaction, standard change in entropy across a pathway/EM, elementary mode, elementary mode analysis, flux balance analysis, genetic algorithm, metabolic flux analysis, multi-objective genetic algorithm, minimization of metabolic adjustment, non-dominated sorting genetic algorithm-II, regulatory on/off minimization, transcriptional regulatory network, transcriptional and translational.; Springer Open. %[ 2018-10-11 %9 Text %~ Tufts Digital Library %W Institution