Mathematics of Synthetic Gene Networks

合成基因网络的数学

• 批准号: 1100309
• 负责人: Xiao Wang
• 金额: $ 68.46万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1100309&HistoricalAwards=false
• 关键词: Mathematics; Synthetic; Gene; Networks

This project uses the combination of mathematical, engineering, and biological techniques to construct, monitor, and analyze novel engineered gene networks. The objective of the research is to expand the mathematical framework used in the modeling and analysis of synthetic gene networks. First, the investigators systematically study stochasticity at separatrices of bistable synthetic gene networks. Recently developed yeast bistable gene networks are used for the first time to initialize gene networks on their separatrices. In particular, the difference between Gillespie algorithm and Chemical Langevin equation (CLE) based algorithms in simulating stochastic processes near bifurcation points is investigated, both analytically and experimentally. With the experience in studying bistable systems, the next step is to mathematically and experimentally analyze nonlinear stochastic dynamics in three potential well systems. Nonlinear stochastic dynamics in multi potential well systems have not been well studied. This network is constructed using available, well-characterized components and techniques and by combining microfluidics devices, single cell live imaging and stochastic modeling to observe and study noise induced random state switching. Finally, methods of high dimensional analysis of gene networks are developed. Tools for dynamical analysis of high dimensional gene networks have been lacking. This project develops mathematical methods to expand the analysis of gene networks from two dimensions into higher dimensionalities. High throughput network identification methods that utilize parallel computing are developed. Bifurcation analysis of high dimensional dynamical systems with applications in gene networks is tested. Novel systematic understanding of cell differentiation and reprogramming derives from the study of synthetic multistable gene networks. Synthetic multistable systems provide unique opportunities to study the core mechanisms of cell pluripotency and differentiation because highly connected small transcription networks regulating cell differentiation have topological similarities with the synthetic gene networks under consideration in this project. Constructing and analyzing small multistable gene network deepens our understanding of multistability, which can arise from similar topologies in stem cell gene regulations. Additionally, mathematical theories and tools to study high dimensional nonlinear dynamics and stochasticity in the context of gene networks are developed. Currently, theoretical efforts to study cellular multistable systems are lacking. This research fills the gap between technological progress and available analytical tools to facilitate future biotechnological development. In addition, both undergraduate and graduate students carry out synthetic biology experiments and analysis. By participating in the international Genetically Engineered Machine (iGEM) competition, these students promote developments of modern biological technologies at Arizona State University. K-12 students and teachers in the Phoenix metropolitan area will also have opportunities to participate in cutting-edge research activities with scientific and infrastructure support from the principal investigators and the university.

这个项目结合了数学、工程和生物技术来构建、监测和分析新的工程基因网络。该研究的目的是扩展用于合成基因网络建模和分析的数学框架。首先，研究者系统地研究了双稳态合成基因网络在分离点上的随机性。利用近年来发展起来的酵母双稳态基因网络，首次在酵母双稳态基因网络的分离层上进行基因网络的初始化。特别研究了Gillespie算法与基于化学朗格万方程（Chemical Langevin equation， CLE）的算法在模拟分岔点附近随机过程方面的差异。有了研究双稳系统的经验，下一步是对三个势井系统的非线性随机动力学进行数学和实验分析。多势井系统的非线性随机动力学还没有得到很好的研究。该网络是利用现有的、特性良好的组件和技术，结合微流体设备、单细胞实时成像和随机建模来观察和研究噪声引起的随机状态切换。最后，提出了基因网络的高维分析方法。对高维基因网络进行动态分析的工具一直缺乏。该项目发展数学方法，将基因网络的分析从二维扩展到更高的维度。提出了基于并行计算的高吞吐量网络识别方法。测试了高维动力系统的分岔分析在基因网络中的应用。对细胞分化和重编程的新系统理解源于对合成多稳定基因网络的研究。合成多稳定系统为研究细胞多能性和分化的核心机制提供了独特的机会，因为调节细胞分化的高度连接的小转录网络与本项目中考虑的合成基因网络具有拓扑相似性。构建和分析小的多稳定基因网络加深了我们对干细胞基因调控中类似拓扑结构产生的多稳定性的理解。此外，在基因网络的背景下，研究高维非线性动力学和随机性的数学理论和工具得到了发展。目前，对细胞多稳定系统的理论研究还很缺乏。这项研究填补了技术进步和现有分析工具之间的空白，促进了未来生物技术的发展。此外，本科生和研究生都进行合成生物学实验和分析。通过参加国际基因工程机器（iGEM）竞赛，这些学生促进了亚利桑那州立大学现代生物技术的发展。凤凰城市区的K-12学生和教师也将有机会在主要研究人员和大学的科学和基础设施支持下参与尖端研究活动。