title: Robust model matching design methodology for a stochastic synthetic gene network.
abstract: Synthetic biology has shown its potential and promising applications in the last decade. However, many synthetic gene networks cannot work properly and maintain their desired behaviors due to intrinsic parameter variations and extrinsic disturbances. In this study, the intrinsic parameter uncertainties and external disturbances are modeled in a non-linear stochastic gene network to mimic the real environment in the host cell. Then a non-linear stochastic robust matching design methodology is introduced to withstand the intrinsic parameter fluctuations and to attenuate the extrinsic disturbances in order to achieve a desired reference matching purpose. To avoid solving the Hamilton-Jacobi inequality (HJI) in the non-linear stochastic robust matching design, global linearization technique is used to simplify the design procedure by solving a set of linear matrix inequalities (LMIs). As a result, the proposed matching design methodology of the robust synthetic gene network can be efficiently designed with the help of LMI toolbox in Matlab. Finally, two in silico design examples of the robust synthetic gene network are given to illustrate the design procedure and to confirm the robust model matching performance to achieve the desired behavior in spite of stochastic parameter fluctuations and environmental disturbances in the host cell.
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: Chia-Hung
authors.initials: CH
authors.lastname: Chang
authors.forename: Yu-Chao
authors.initials: YC
authors.lastname: Wang
authors.forename: Chih-Hung
authors.initials: CH
authors.lastname: Wu
authors.forename: Hsiao-Ching
authors.initials: HC
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics; D025461:Feedback, Physiological; D005786:Gene Expression Regulation / Q000235:genetics; D053263:Gene Regulatory Networks; D005813:Genes, Synthetic / Q000235:genetics*; D007700:Kinetics; D008164:Luminescent Proteins / Q000235:genetics / Q000378:metabolism; D008957:Models, Genetic; D009010:Monte Carlo Method; D014176:Protein Biosynthesis / Q000235:genetics; D012097:Repressor Proteins / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes; D058615:Synthetic Biology / Q000379:methods*; D014158:Transcription, Genetic / Q000235:genetics
chemical_list: D001426:Bacterial Proteins; D008164:Luminescent Proteins; D012097:Repressor Proteins; C034811:tetracycline resistance-encoding transposon repressor protein; C054562:yellow fluorescent protein, Bacteria