%0 Journal Article %1 Chou2013Programming %A Chou, Howard H. %A Keasling, Jay D. %D 2013 %J Nature communications %K bt3240 control metabolic-engineering regulation %R 10.1038/ncomms3595 %T Programming adaptive control to evolve increased metabolite production. %U http://dx.doi.org/10.1038/ncomms3595 %V 4 %X The complexity inherent in biological systems challenges efforts to rationally engineer novel phenotypes, especially those not amenable to high-throughput screens and selections. In nature, increased mutation rates generate diversity in a population that can lead to the evolution of new phenotypes. Here we construct an adaptive control system that increases the mutation rate in order to generate diversity in the population, and decreases the mutation rate as the concentration of a target metabolite increases. This system is called feedback-regulated evolution of phenotype (FREP), and is implemented with a sensor to gauge the concentration of a metabolite and an actuator to alter the mutation rate. To evolve certain novel traits that have no known natural sensors, we develop a framework to assemble synthetic transcription factors using metabolic enzymes and construct four different sensors that recognize isopentenyl diphosphate in bacteria and yeast. We verify FREP by evolving increased tyrosine and isoprenoid production.

@article{Chou2013Programming, abstract = { The complexity inherent in biological systems challenges efforts to rationally engineer novel phenotypes, especially those not amenable to high-throughput screens and selections. In nature, increased mutation rates generate diversity in a population that can lead to the evolution of new phenotypes. Here we construct an adaptive control system that increases the mutation rate in order to generate diversity in the population, and decreases the mutation rate as the concentration of a target metabolite increases. This system is called feedback-regulated evolution of phenotype ({FREP}), and is implemented with a sensor to gauge the concentration of a metabolite and an actuator to alter the mutation rate. To evolve certain novel traits that have no known natural sensors, we develop a framework to assemble synthetic transcription factors using metabolic enzymes and construct four different sensors that recognize isopentenyl diphosphate in bacteria and yeast. We verify {FREP} by evolving increased tyrosine and isoprenoid production. }, added-at = {2018-12-02T16:09:07.000+0100}, author = {Chou, Howard H. and Keasling, Jay D.}, biburl = {https://www.bibsonomy.org/bibtex/2fca0ce4ee78c9900db3141876f1cd3b5/karthikraman}, citeulike-article-id = {12727176}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/ncomms3595}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/24131951}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=24131951}, day = 17, doi = {10.1038/ncomms3595}, interhash = {5934180a77859e6e469ce1cd46c3de38}, intrahash = {fca0ce4ee78c9900db3141876f1cd3b5}, issn = {2041-1723}, journal = {Nature communications}, keywords = {bt3240 control metabolic-engineering regulation}, month = oct, pmid = {24131951}, posted-at = {2013-11-04 06:56:42}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Programming adaptive control to evolve increased metabolite production.}, url = {http://dx.doi.org/10.1038/ncomms3595}, volume = 4, year = 2013 }