@article{200336,
  keywords = {modeling optogenetic circuit yeast metabolic engineering},
  author = {Robert J. Lovelett and Evan M. Zhao and Makoto A. Lalwani and Jared E. Toettcher and IoannisG. Kevrekidis and Jose L. Avalos},
  title = {Dynamical modeling of optogenetic circuits in yeast for metabolic engineering applications},
  abstract = { Dynamic control of engineered microbes using light via optogenetics has been demonstrated as an effective strategy for improving the yield of biofuels, chems., and other products. An advantage of using light to manipulate microbial metabolism is the relative simplicity of interfacing biol. and computer systems, thereby enabling in silico control of the microbe. Using this strategy for control and optimization of product yield requires an understanding of how the microbe responds in real-time to the light inputs. Toward this end, the authors present mechanistic models of a set of yeast optogenetic circuits. The authors show how these models can predict short- and long-time response to varying light inputs and how they are amenable to use with model predictive control (the industry standard among advanced control algorithms). These models reveal dynamics characterized by time-scale separation of different circuit components that affect the steady and transient levels of the protein under control of the circuit. Ultimately, this work will help enable real-time control and optimization tools for improving yield and consistency in the production of biofuels and chems. using microbial fermentations },
  year = {2021},
  journal = {ACS Synth. Biol.},
  volume = {10},
  number = {2},
  pages = {219-227},
  publisher = {American Chemical Society},
  isbn = {2161-5063},
  url = {https://doi.org/10.1021/acssynbio.0c00372},
  language = {eng},
}