Inferring Biological Mechanism from Mutational Interactions

从突变相互作用推断生物机制

• 批准号: 1038657
• 负责人: Daniel Weinreich
• 金额: $ 25.91万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1038657&HistoricalAwards=false
• 关键词: Inferring; Biological; Mechanism; Mutational; Interactions; Inferring; Biological; Mechanism; Mutational; Interactions

Intellectual Merit: Genetics is the study of how biological traits are transmitted from parents to offspring. For over 100 years it has been appreciated that owing to biological complexities, a mutations effect may vary with the genetic background in which it occurs. For example, imagine the following biochemical pathway. Here some compound X is converted by Enzyme 1 (coded by one gene) into compound Y and then Y is converted by Enzyme 2 (coded by another gene) into a third compound Z. In this case, a mutation that inactivates Enzyme 2 also inactivates the entire pathway, even in an organism in which Enzyme 1 is functional. On the other hand, the same mutation would have no effect in an organism in which Enzyme 1 was previously inactivated. Such interactions complicate the question, what does this mutation do since the effects of mutations in these cases are context-dependent? But at the same time, such interactions provide opportunities for experimentation to dissect underlying biological mechanisms. In our simple example, the observation that mutations inactivating Enzyme 2 have no effect when Enzyme 1 has been inactivated implies that Enzyme 2 mechanistically acts downstream of Enzyme 1 in some common pathway. This project formalizes these intuitive notions using two theoretical approaches, one based on a quantitative model of how single enzymes operate and the other based on a quantitative model of whole-organism metabolism. This work will yield an analytic framework to sort pairs of mutations into those that act by a shared mechanism and those that act by distinct mechanisms. In addition it will provide an estimate of the number of distinct mechanisms influencing a biological trait. This research is extremely timely because recent high-throughput technical innovations in genomics are yielding vast datasets on mutational interactions in several microbial model systems (E. coli, S. cerevisiae and S. pombe), and prospects are good for similar datasets in multicellular model organisms such as D. melanogaster and C. elegans. These experimental innovations thus open the door to far more sophisticated mechanistic analyses. Critically, direct experimental attack on specific mechanistic interactions remains prohibitively expensive, further motivating the present theoretical approach. This work also promises to make contributions at several levels of biological organization, from enzymatics to whole organism reproductive success to ecological and biogeochemical resource fluxes. This follows because the theoretical model of single enzymes can also be applied to whole organisms, and because the model of metabolism can be applied to any network of chemical fluxes.Broader Impacts. Beyond allowing inferences to be made regarding biological mechanisms, mutational interactions have theoretical implications for a diversity of biological processes, including constraints on adaptation, the evolution of sex and speciation. The PI has active research programs in several of these areas and so this research directly complements his ongoing work. Moreover this project directly supports the training of a graduate student at the interface of mathematics and biology, to develop expertise essential in this genomic and post-genomic era. Spin-off projects are planned to engage a number of undergraduate students in research working in the PIs laboratory each term and during the summer. The PI also has an ongoing commitment to the intellectual engagement of Providence public school students and teachers through an existing NSF-funded GK-12 program. This outreach work addresses current cultural barriers to understanding genetics and the implications of evolutionary thinking in the United States.

智力优点：遗传学是对生物学特征如何从父母传播到后代的研究。 100多年来，人们一直认为，由于生物复杂性，突变效应可能随发生的遗传背景而变化。例如，想象以下生化途径。 在这里，某些化合物X被酶1（由一个基因编码）转化为化合物Y，然后Y被酶2（由另一个基因编码）转换为第三种化合物Z。在这种情况下，即使在整个途径中灭活酶2的突变，即使在enzyme 1中也使整个途径失活。另一方面，在先前酶1被灭活的生物体中，相同的突变无效。这种相互作用使问题复杂化，由于在这些情况下突变的影响与上下文有关，因此该突变有什么作用？但与此同时，这种相互作用为实验提供了剖析基本生物学机制的机会。 在我们的简单示例中，当酶1被灭活时，突变灭活酶2的观察没有影响，这意味着酶2在某些公共途径中机械地在酶1的下游作用。 该项目使用两种理论方法对这些直观的概念进行形式化，一种基于一个定量模型，该模型是关于单个酶如何运作的定量模型，而另一种基于全生物代谢的定量模型。 这项工作将产生一个分析框架，将一对突变对通过共享机制和通过不同机制起作用的突变进行分类。 此外，它将提供影响生物特征的不同机制的数量。 这项研究非常及时，因为最近的高通量技术创新在几种微生物模型系统（E. Coli，S。cerevisiae and S. pombe）中产生了广泛的数据集，并且前景对多细胞模型有机体（如D. Melanogasters和C. equans）中的类似数据集有益。因此，这些实验创新为更复杂的机械分析打开了大门。 至关重要的是，对特定机械相互作用的直接实验攻击仍然非常昂贵，进一步激发了当前的理论方法。这项工作还有望在生物组织的多个级别上做出贡献，从酶促到整个生物体生殖成功再到生态和生物地球化学资源通量。 因此，这是因为单个酶的理论模型也可以应用于整个生物体，并且代谢模型可以应用于任何化学通量网络。 除了对生物学机制做出推论之外，突变相互作用对生物过程的多样性具有理论意义，包括对适应性的限制，性别和物种的演变。 PI在其中几个领域具有积极的研究计划，因此这项研究直接补充了他正在进行的工作。 此外，该项目直接支持在数学和生物学界面上对研究生的培训，以在这个基因组和基因组后时代发展专业知识。 计划分拆项目让许多本科生参与每个学期和夏季在PIS实验室工作的研究。 PI还通过现有的NSF资助的GK-12计划对Providence Public Sc​​hool学生和教师的智力参与持续承诺。 这项外展工作旨在解决了解遗传学及其进化思想的影响的当前文化障碍。