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

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：遗传学是研究生物特征如何从父母传递给后代的学科。 一百多年来，人们已经认识到，由于生物的复杂性，突变效应可能会随其发生的遗传背景而变化。例如，想象以下生化途径。 这里，一些化合物 X 通过酶 1（由一个基因编码）转化为化合物 Y，然后 Y 通过酶 2（由另一个基因编码）转化为第三种化合物 Z。在这种情况下，使酶 2 失活的突变也会使酶 2 失活整个途径，甚至在酶 1 起作用的生物体中也是如此。另一方面，相同的突变对于酶 1 先前失活的生物体不会产生任何影响。这种相互作用使问题变得复杂，因为这些情况下突变的影响是依赖于环境的，所以这种突变会做什么？但与此同时，这种相互作用为剖析潜在生物机制的实验提供了机会。 在我们的简单示例中，观察到当酶 1 失活时，使酶 2 失活的突变没有影响，这意味着酶 2 在某些常见途径中机械地作用于酶 1 的下游。 该项目使用两种理论方法将这些直观的概念形式化，一种基于单个酶如何运作的定量模型，另一种基于整个生物体代谢的定量模型。 这项工作将产生一个分析框架，将突变对分类为通过共享机制起作用的突变和通过不同机制起作用的突变。 此外，它将提供对影响生物特征的不同机制数量的估计。 这项研究非常及时，因为最近基因组学的高通量技术创新正在产生有关几种微生物模型系统（大肠杆菌、酿酒酵母和粟酒裂殖酵母）中突变相互作用的大量数据集，并且多细胞模型中的类似数据集前景良好生物体，例如黑腹果蝇和线虫。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，这进一步推动了目前的理论方法。这项工作还有望在生物组织的多个层面做出贡献，从酶学到整个生物体的繁殖成功，再到生态和生物地球化学资源通量。 这是因为单一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。更广泛的影响。 除了可以对生物机制进行推断之外，突变相互作用还对多种生物过程具有理论意义，包括对适应、性别和物种形成的进化的限制。 PI 在其中几个领域开展了积极的研究项目，因此这项研究直接补充了他正在进行的工作。 此外，该项目直接支持研究生在数学和生物学交叉领域的培训，以培养基因组和后基因组时代必需的专业知识。 计划在每个学期和夏季让一些本科生在 PI 实验室进行研究工作。 PI 还通过现有的 NSF 资助的 GK-12 项目持续致力于普罗维登斯公立学校学生和教师的智力参与。 这项外展工作解决了美国当前理解遗传学的文化障碍和进化思想的影响。