Synthetic Biology is a nascent field with applications that range from bio-fabrication to alternative energy. Despite its significance, engineering of biological circuits still relies on trial-and-error tinkering techniques, with limited computational support. If Synthetic Biology is to advance to more complex synthetic systems that go beyond a handful of interacting parts, a scalable, integrative, methodological approach is necessary. In an analogy to integrated circuits, when it comes to circuit engineering, the role of detailed computer models, optimization methods, simulators and design tools is paramount.
Intellectual Merit: This project aims to pave the way towards an optimization-based, automated design framework for synthetic gene circuits that adhere to user-defined constraints. A synthetic gene circuit is a collection of one or more genes, together with elements (promoters, ribosome binding sites, etc.) that influence gene expression. The wiring, i.e. the order and position of every element, within a synthetic gene circuit determines the gene expression pattern, and overall behavior of the circuit. These circuits are introduced, usually as part of a plasmid(s), in a host organism that can be readily manipulated in order to achieve a desired outcome (e.g. specific temporal behavior, or production of an enzyme). To facilitate faster time-to-market solutions and more robust, predictable designs, PIs will develop a design and optimization tool prototype. To that end, PIs propose a new optimization formulation that encompasses multiple biological models relevant to synthetic genetic circuit design. In addition, they propose a hybrid optimization-simulation technique to capture additional effects related to cell division, noise, and evolutionary processes. The investigation will focus on how state-of-the-art techniques from combinatorial optimization can be applied to find the optimal circuit for a specific task. Since the tool will need a library of well-characterized components to operate, PIs will create a mutant library of three widely-used regulators, then quantitatively characterize them, and store this information in a publicly available database. As a proof-of-concept experiment, they will assess their integrative approach by constructing an automatically-designed synthetic circuit, measuring its output and deviation from the desired goal, and then comparing it to other similar designs that have been already available in literature.
Broader Impact: An optimization-based, design tool for synthetic biology has the potential to provide a service to the academic community by reducing drastically the time-to-market aspect of synthetic designs, and providing insight on biological function, thus accelerating research in an exponentially growing field. All components and characterized libraries that will be developed as part of this award will be publicly available, deposited in the synthetic biology community?s standard Parts Registry. Furthermore, this award will partially support the work and training of the UC Davis IGEM team, a synthetic biology undergraduate team who competes in the annual IGEM competition. Knowledge from this project will be directly transferred into classrooms through the course ECS 289K "Computational Challenges in Systems and Synthetic Biology" (UC Davis), and the course CSC 450/550 "Algorithms for Bioinformatics" (U. Arizona).
This project developed prototype software for automatically designing a system of interacting genes in a bacterium, called a synthetic gene circuit, that have a desired expression profile over time for a reporter protein. The user specifies the expression level of the reporter protein at discrete time points, together with a library of available genes and promoter elements with corresponding reaction rate constants, and the software selects genes and promoter elements from the library to form the synthetic gene circuit. The software uses the technique of nonlinear integer programming to design a synthetic circut that gives the closest match to the desired expression profile, where the expression level of the system of genes is modeled by a system of ordinary differential equations. The total squared error between the desired expression profile and the predicted expression profile for the circuit is minimized by the nonlinear integer program. The project resulted in one journal paper in the Public Library of Science ONE online journal in 2012, and one conference publication in the International Workshop on Biodesign Automation in 2011. This was a collaborative grant between John Kececioglu at the University of Arizona, and Ilias Tagkopoulos and Matthias Koeppe at the University of California at Davis.
