title: Robust design of biological circuits: evolutionary systems biology approach.
abstract: Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.
authors.affiliation: Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan. bschen@ee.nthu.edu.tw
authors.forename: Bor-Sen
authors.initials: BS
authors.lastname: Chen
authors.forename: Chih-Yuan
authors.initials: CY
authors.lastname: Hsu
authors.forename: Jing-Jia
authors.initials: JJ
authors.lastname: Liou
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D012321:DNA-Directed RNA Polymerases / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics; D019143:Evolution, Molecular; D053263:Gene Regulatory Networks; D005798:Genes, Bacterial; D008957:Models, Genetic; D009010:Monte Carlo Method; D013269:Stochastic Processes; D058615:Synthetic Biology / Q000379:methods*; D049490:Systems Biology / Q000379:methods*; D014158:Transcription, Genetic; D014764:Viral Proteins / Q000378:metabolism
chemical_list: D014764:Viral Proteins; C062210:bacteriophage T7 RNA polymerase; D012321:DNA-Directed RNA Polymerases