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.

智力优势：遗传学是研究生物特征如何从父母传递给后代的学科。 100多年来，人们已经认识到，由于生物学的复杂性，突变效应可能会随着其发生的遗传背景而变化。例如，想象下面的生化途径。 在这里，一些化合物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）转化为第三种化合物Z。在这种情况下，使酶2失活的突变也使整个途径失活，即使在酶1起作用的生物体中也是如此。另一方面，相同的突变在酶1先前失活的生物体中没有影响。这样的相互作用使问题复杂化了，既然这些情况下突变的影响是依赖于上下文的，那么这种突变会做什么呢？但与此同时，这种相互作用为实验提供了机会，以剖析潜在的生物机制。 在我们简单的例子中，当酶1失活时，失活酶2的突变没有影响，这意味着酶2在某种共同途径中在酶1的下游起作用。 该项目使用两种理论方法将这些直观的概念形式化，一种是基于单酶如何运作的定量模型，另一种是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，将成对的突变分为由共享机制起作用的突变和由不同机制起作用的突变。 此外，它还将提供影响生物性状的不同机制的数量的估计。 这项研究是非常及时的，因为最近基因组学的高通量技术创新产生了大量关于几种微生物模型系统中突变相互作用的数据集（E。coli、S.酿酒酵母和酿酒酵母。pombe），并且对于多细胞模式生物如D.黑腹果蝇和C.优美的因此，这些实验创新为更复杂的机理分析打开了大门。 关键的是，对特定机械相互作用的直接实验攻击仍然非常昂贵，进一步激励了目前的理论方法。这项工作还有望在生物组织的几个层次上做出贡献，从酶学到整个生物体的繁殖成功，再到生态和地球化学资源的流动。 这是因为单个酶的理论模型也可以应用于整个生物体，并且因为代谢模型可以应用于任何化学通量网络。 除了允许对生物机制进行推断外，突变相互作用对多种生物过程具有理论意义，包括对适应、性别进化和物种形成的限制。 PI在其中几个领域有积极的研究计划，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的接口研究生的培训，开发在这个基因组和后基因组时代必不可少的专业知识。 副产品项目计划在每个学期和夏季在PI实验室从事研究工作的一些本科生。 PI还通过现有的NSF资助的GK-12计划，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当前理解遗传学的文化障碍以及美国进化思维的影响。