automatic design of synthetic gene circuits through mixed integer non-linear programming自动设计的合成基因电路通过混合整数非线性规划.pdf

Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming 1,2 3 ¨ 4 1,2 Linh Huynh , John Kececioglu , Matthias Koppe , Ilias Tagkopoulos * 1 Department of Computer Science, University of California Davis, Davis, United States of America, 2 Genome Center, University of California Davis, Davis, California, United States of America, 3 Department of Computer Science, University of Arizona, Tucson, Arizona, United States of America, 4 Department of Mathematics, University of California Davis, Davis, California, United States of America Abstract Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluat