SHF: Small: Design Tools and their Experimental Validation for Synthetic Biological Systems

点击翻译按钮获取中文摘要

The broader aim of synthetic biology is to design and build organisms specialized to perform pre-designated functions ranging from production of commercially desirable fuels, chemicals, and pharmaceuticals to remediating contaminating soil and water. The work proposed here is to develop and validate computational tools to increase the role of automation in designing synthetic biological systems. These tools are aimed at creating a streamlined methodology that integrates biochemical, regulatory, and phylogenetic information into a single platform to expedite the design and creation of function-based organisms. These tools will significantly shorten the time taken to re-engineer organisms and will enable wider usage of synthetic organisms. Students will be engaged in this research through novel course material, hands-on experimentation through the iGEM (International Genetically Engineering Machines) competition, and relevant research projects with the principle investigators. Recruiting efforts will be put forward to attract women and underrepresented minority undergraduate students to participate in this research. We address in this proposal two cellular engineering problems that enable maximizing cellular yield. The first problem consists of identifying and ranking non-native synthesis pathways that yield desirable biomolecules such as biofuels or therapeutics. In the second problem, we identify reactions whose fluxes must be modified to increase cellular yield. While these two problems have been addressed within the metabolic engineering community, a holistic approach for engineering biological systems requires extending these approaches to consider underlying gene interactions and limitations. While the synthetic biology community has embraced a bottom-up approach for creating design methodologies and tools to enable creating genetic circuitry, the metabolic engineering community has relied on pathway-based design approaches. Our proposed approach lies at the intersection of these two differing approaches. In this proposal, traditional pathway-based approaches are extended to exploit knowledge and limitations of the underlying genetic networks. To rank synthesis pathways, gene similarity and transcript secondary structure are utilized. To identify gene modifications, fold changes in gene expressions as design variables are used, while accounting for uncertainties that arise with engineering interventions. To validate the outcomes of the proposed computational tools, we build synthesis pathways to produce isoprenoids and implement identified gene modifications, using Escherichia coli as a host organism.