AUTOMATED DESIGN AND OPTIMIZATION OF SYNTHETIC GENETIC SYSTEMS

合成遗传系统的自动化设计和优化

• 批准号: 8171931
• 负责人: Howard M Salis
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171931
• 关键词: Bacteria; Behavior; Binding Sites; Biochemical Pathway; Biological; Biological Process; Chemicals; Code; Communities; Computer Retrieval of Information on Scientific Projects Database; Computers; DNA; DNA Sequence; Engineering; Enzymes; Funding; Genetic; Genetic Models; Genetic Programming; Goals; Grant; Institution; Life; Metabolic; Methodology; Methods; Mutagenesis; Occupations; Pharmaceutical Preparations; Production; Proteins; Research; Research Personnel; Resources; Ribosomes; Source; Specific qualifier value; System; Techniques; Testing; United States National Institutes of Health; computing resources; design; improved; interest; member; promoter; sugar; synthetic biology; time use; user-friendly; web based interface

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Bacteria are living, chemical computers. By assembling together different genetic parts (refs), such as promoters, ribosome binding sites, and protein coding sequences, into a DNA molecule of a specific sequence, genetic programs are constructed that confer many useful functions to a bacterium, including the ability to manufacture biofuels and drugs from renewable sugars (refs). In the field of synthetic biology, a central goal is obtaining the ability to design genetic programs in a predictable fashion (refs). Currently, such genetic programs are constructed and tested using time-consuming trial-and-error techniques, such as random mutagenesis. We are developing the methodologies to design synthetic genetic systems in a predictive and systematic way. The methods combine biophysical models of genetic part function, DNA sequence optimization techniques, and design principles for genetic systems to convert a target biological behavior into a specific DNA sequence. We are also creating a user-friendly web-based interface where members of the genetic and metabolic engineering communities can specify a target biological function and receive the DNA sequence of a genetic program that carries out that function. We request 100 000 SUs to pursue the following research goals: (i) improve our recently developed design method for synthetic ribosome binding site by expanding it towards the optimization of entire protein coding sequences; (ii) allow users to request optimization jobs on their protein coding sequences of interest and off-load this computation onto TeraGrid resources; (iii) solve the RBS Minimax problem for a 22 enzyme metabolic network to maximize production of a chemical precursor to a biofuel. The computational resources are divided as: 40 000 SUs on Roaming TeraGrid and 60 000 SUs on Abe, Ranger, and/or Steele systems.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 细菌是活的化学计算机。通过将不同的遗传部分（参考文献），例如启动子、核糖体结合位点和蛋白质编码序列组装到特定序列的 DNA 分子中，构建遗传程序，赋予细菌许多有用的功能，包括制造细菌的能力。来自可再生糖的生物燃料和药物（参考文献）。在合成生物学领域，一个中心目标是获得以可预测的方式设计遗传程序的能力（参考文献）。目前，此类遗传程序是使用耗时的试错技术（例如随机诱变）构建和测试的。我们正在开发以预测和系统的方式设计合成遗传系统的方法。这些方法结合了遗传部分功能的生物物理模型、DNA序列优化技术和遗传系统的设计原理，将目标生物行为转化为特定的DNA序列。我们还创建了一个用户友好的基于网络的界面，遗传和代谢工程界的成员可以在其中指定目标生物功能并接收执行该功能的遗传程序的 DNA 序列。我们要求 100 000 个 SU 追求以下研究目标：（i）通过将其扩展到整个蛋白质编码序列的优化来改进我们最近开发的合成核糖体结合位点的设计方法； (ii) 允许用户请求对其感兴趣的蛋白质编码序列进行优化作业，并将计算卸载到 TeraGrid 资源上； (iii) 解决 22 种酶代谢网络的 RBS Minimax 问题，以最大限度地提高生物燃料化学前体的产量。计算资源划分为：漫游 TeraGrid 上的 40 000 个 SU 和 Abe、Ranger 和/或 Steele 系统上的 60 000 个 SU。