Download or read book A Computational Framework for the Automated Design of Synthetic Genetic Circuits written by Linh Viet Huynh and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Synthetic biology is an emerging research field with potential applications for a variety of disciplines from development of therapeutics, medicine to biofuel production and agriculture. In synthetic biology, we design a genetic circuit (i.e. a set of genetic parts and their interaction) encoded in the genetic material of a natural biological system to make this system exhibit novel functions. However, almost all genetic circuits were still designed by trial-and-error and tinkering approaches. Therefore, computational computer aided design (CAD) tools are essential for designing circuits faster, better and more reliably. Numerous CAD tools have been developed recently, however, there are still at least three challenges that prevent us building a CAD tool that is applicable for experimentalists: (i) How can we make a reliable circuit behavior prediction to guide the design work? (ii) How can we search for an optimal circuit from a huge circuit space due to the increase of both the number of genetic parts and the circuit complexity? (iii) How can we integrate all computational techniques into an unique tool that experimentalists can access? This dissertation proposed novel approaches to address these challenges. First, I proposed a novel parameter inference method to achieve more exact parameter values and thus we could provide a more reliable behavior prediction of a circuit. Second, I proposed two novel methods to search for circuits that behavior best approximated the desired one and the construction was simplest. Third, I introduced a computational framework that integrated both the work of simulating a circuit with inferred parameter values and optimizing the circuit design for a given user specification. Our CAD tool, SBROME, can utilize experimental data systematically to improve circuit behavior prediction, organize parts and their related parameter values in an unified database, and optimize circuit construction by reusing the old ones. In addition, an evaluation with a benchmark of 11 circuits and a curated dataset of experimental data (135 publications that contain 118 circuits and 165 genetic parts) also showed encouraging results. This dissertation contributes computational solutions that can help us move the field of synthetic biology closer to practical applications.