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

合成生物学的更广泛的目标是设计和构建专门执行预先指定的功能的生物体，从生产商业上需要的燃料、化学品和药品到修复受污染的土壤和水。这里提出的工作是开发和验证计算工具，以增加自动化在设计合成生物系统中的作用。这些工具旨在创建一种简化的方法，将生化、监管和系统发育信息整合到一个单一平台中，以加快基于功能的生物体的设计和创建。这些工具将大大缩短重新设计生物体所需的时间，并将使合成生物体得到更广泛的使用。学生将通过新颖的课程材料，通过iGEM(国际基因工程机器)竞赛的实践实验，以及与原理研究人员的相关研究项目来从事这项研究。将努力招收女性和代表性不足的少数族裔本科生参与这项研究。我们在这个提案中解决了两个细胞工程问题，使细胞产量最大化。第一个问题包括识别和排序产生所需生物分子(如生物燃料或治疗药物)的非天然合成途径。在第二个问题中，我们确定了必须修改其通量以增加细胞产量的反应。虽然代谢工程界已经解决了这两个问题，但工程生物系统的整体方法需要扩展这些方法，以考虑潜在的基因相互作用和限制。合成生物界采用自下而上的方法来创建设计方法和工具，以创建基因电路，而代谢工程界则依赖基于途径的设计方法。我们建议的方法位于这两种不同方法的交叉点。在这项建议中，传统的基于途径的方法被扩展，以利用潜在遗传网络的知识和限制。利用基因相似性和转录本二级结构对合成途径进行排序。为了识别基因修饰，使用了作为设计变量的基因表达的折叠变化，同时考虑了工程干预引起的不确定性。为了验证所提出的计算工具的结果，我们建立了合成途径来产生异戊二烯类化合物，并使用大肠杆菌作为宿主生物来实施识别的基因修饰。

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.