Collaborative Research: Deep-sequencing analysis of edited metabolic pathways to uncover, model, and overcome the epistatic constraints upon optimization

合作研究：对编辑后的代谢途径进行深度测序分析，以发现、建模和克服优化时的上位限制

• 批准号: 1714322
• 负责人: Norma Martinez-Gomez
• 金额: $ 10.62万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714322&HistoricalAwards=false
• 关键词: Collaborative; Research; Deep; sequencing; analysis

Biological systems are inherently complex, composed of many interacting molecules. Even with knowledge of the properties of each individual component, these interactions create a challenge for predicting how changing one enzyme will affect the performance of the whole pathway and the growth of the organism. While synthetic biology has the potential to address certain critical national challenges, progress is hampered by a lack of mathematical models that can be used to guide the optimization of complex biological systems. This project works to optimize the mechanisms that incorporate carbon gas into cell material in order to develop an efficient organism for generating products such as fuels or pigments. The results of the experiments will then yield a computational model capable of predicting the effects of novel combinations of genes. This project will directly lead to specific improvements in an important biotechnological platform, while simultaneously demonstrating a generic approach to using computational biology to efficiently apply the power of genome editing to a variety of synthetic biology challenges. The project also will develop and disseminate computational tools via websites, publications, workshops, and classes that will make it easier for students and researchers to simulate and analyze metabolic networks to learn about fundamental quantitative concepts that underlie their function, and provide interdisciplinary training for undergraduates, graduate students, and postdoctoral fellows. Epistasis represents a critical challenge to optimizing biological systems. When mutational effects upon growth or product generation depend on the genetic background, assessing performance across the entire parameter space of any system of realistic size quickly becomes impossible. There is an immediate need for two linked developments: empirical techniques that can rapidly generate and assess rational, combinatorial variants, and kinetic modeling techniques to incorporate these data and to make predictions. This project will use this novel approach to optimize the function of the high-efficiency ribulose monophosphate (RuMP) pathway that the team has successfully introduced into the model methanol-consuming organism, Methylobacterium extorquens. In this project, gene editing of a plasmid-encoded suite of enzymes will be performed along with deep sequencing to rapidly assess the fitnesses of a quarter-million genotypes with combinatorial variation in nine dimensions of expression. The resulting epistasis data, combined with direct measurement of intracellular metabolite concentrations for select variant combinations, will be used to infer the numerous parameter values in the kinetic model, which then will be utilized to predict which regions of parameter space would be more or less flexible. These parameter spaces will be targeted and compared in a second round of editing, experimentation and evaluation. This project is funded by the Systems and Synthetic Biology Program in the Division of Molecular and Cellular Biosciences.

生物系统本质上是复杂的，由许多相互作用的分子组成。即使知道每个单独组分的性质，这些相互作用也给预测改变一种酶将如何影响整个途径的性能和生物体的生长带来了挑战。虽然合成生物学有潜力解决某些关键的国家挑战，但由于缺乏可用于指导复杂生物系统优化的数学模型，进展受到阻碍。该项目致力于优化将碳气体纳入细胞材料的机制，以开发一种有效的生物体，用于生产燃料或颜料等产品。实验结果将产生一个计算模型，能够预测新的基因组合的影响。该项目将直接导致一个重要的生物技术平台的具体改进，同时展示一种通用方法，利用计算生物学有效地将基因组编辑的力量应用于各种合成生物学挑战。该项目还将通过网站，出版物，研讨会和课程开发和传播计算工具，使学生和研究人员更容易模拟和分析代谢网络，以了解其功能基础的基本定量概念，并为本科生，研究生和博士后提供跨学科培训。 上位性是优化生物系统的关键挑战。当突变对生长或产物生成的影响取决于遗传背景时，评估任何实际大小的系统的整个参数空间的性能很快变得不可能。目前迫切需要两个相互关联的发展：经验技术，可以快速生成和评估合理的，组合的变量，并结合这些数据，并作出预测的动力学建模技术。该项目将使用这种新方法来优化高效核酮糖单磷酸（RuMP）途径的功能，该团队已成功将其引入到模型甲醇消耗生物-扭脱甲基杆菌中。在这个项目中，质粒编码的一套酶的基因编辑将与深度测序一起进行沿着，以快速评估25万个基因型在9个表达维度上的组合变异的适应性。 所得的上位性数据，与选择的变体组合的细胞内代谢物浓度的直接测量相结合，将用于推断动力学模型中的许多参数值，然后将其用于预测参数空间的哪些区域将更灵活或更不灵活。 这些参数空间将在第二轮编辑、试验和评估中确定目标并进行比较。该项目由分子和细胞生物科学部的系统和合成生物学计划资助。