A number of separate studies have shown that genome-scale flux-balance analysis (FBA) modeling can be useful for the in silico design of engineered strains of microbes that overproduce diverse targets. These engineered strains include Escherichia coli (E. coli) that overproduce lycopene lactic acid succinic acid l-valine l-threonine and strains of Saccharomyces cerevisiae that overproduce ethanol. FBA models allow the result of various genetic manipulations strategies to be predicted. As a result the space of possible genetic manipulations can be computationally searched for the strategy that results in the desired metabolic network state. This space is vast and algorithms must be designed to search the space efficiently. Rutgers University researchers have developed a process for genetically engineering microorganisms for the efficient production of fatty acids. This process allows E. coli and other bacteria to be engineered for high-efficiency production of fatty acids which can then be turned into biofuel. While bacteria can already be engineered to produce fatty acids the greater the efficiency of the process in terms of its ability to produce a high yield of fatty acids for a given amount of feedstock the cheaper the process. This organism is produced using a computational design process to identify favorable genetic modifications. An efficient computational method for in silico design called Genetic Design through Local Search (GDLS) has been developed. GDLS is a scalable heuristic algorithmic method that employs an approach based on local search with multiple search paths resulting in effective low-complexity A number of separate studies have shown that genome-scale flux-balance analysis (FBA) modeling can be useful for the in silico design of engineered strains of microbes that overproduce diverse targets. These engineered strains include Escherichia coli (E. coli) that overproduce lycopene lactic acid succinic acid l-valine l-threonine and strains of Saccharomyces cerevisiae that overproduce ethanol. FBA models allow the result of various genetic manipulations strategies to be predicted. As a result the space of possible genetic manipulations can be computationally searched for the strategy that results in the desired metabolic network state. This space is vast and algorithms must be designed to search the space efficiently. Rutgers University researchers have developed a process for genetically engineering microorganisms for the efficient production of fatty acids.
1) This process allows E. coli and other bacteria to be engineered for high-efficiency production of fatty acids which can then be turned into biofuel. Bacteria can be engineered to produce fatty acids. The greater the efficiency of the process in terms of its ability to produce a high yield of fatty acids for a given amount of feedstock the cheaper the process. 2) GDLS is a scalable heuristic algorithmic method that employs an approach based on local search with multiple search paths resulting in effective low-complexity.