Construction and Characterization of Gene Regulatory Networks in Budding Yeast

芽殖酵母基因调控网络的构建和表征

• 批准号: RGPIN-2016-05659
• 负责人: Kaern, Mads
• 金额: $ 2.77万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709033
• 关键词: Construction; Characterization; Gene; Regulatory; Networks

Humans have used budding yeast to make bread, beer and wine since ancient times. Today, we also use yeast to mass-produce beneficial chemicals that are otherwise difficult or expensive to obtain, such as drugs to combat malaria. Industrial uses of yeast involve altering or adding information encoded in its DNA in the form of genes. While scientists can change DNA through breeding, technological advances in a new scientific field are much more efficient. This field is called Synthetic Biology because it uses biology to create, or synthesize, something that is new. Synthetic Biology is about altering organisms to do what we want them to do. This can involve providing cells the genes needed to produce beneficial chemicals, or writing DNA code instructing cells to express different genes in different situations. Either way, the process of programming' cells is very complicated and we therefore use mathematics and computers to guide our work. My research program will improve our ability to write the DNA-encoded instructions that program how cells behave. Scientists are actually not very good at this, and the programs we write often don't work as expected, if they work at all. To address this problem, Synthetic Biologists have studied how natural organisms regulate gene expression. We know that molecules called transcription factors, which bind to DNA and control the expression of nearby genes, play a critical role. We also know that individual genes are regulated by many different transcription factors, and that transcription factors regulate one another through intricate networks. It is the interconnections within these networks that define which genes a cell will express in different situations. The goal of my research program is to gain knowledge on how gene regulatory networks operate inside living yeast cells. Towards this goal, I will interconnect existing DNA codes that are very predictable to form larger gene regulatory networks. Using mathematical modelling and advanced DNA manipulation techniques, I will focus on addressing two problems that make gene regulatory networks complicated: (1) when a gene is regulated by two or more transcription factors, we don't know how to predict their combined effects; and (2) when a network involves two or more interconnected transcription factors, we don't know if we can accurately predict the outcome. To further test our mathematical models, we will use the same techniques to study a natural gene regulatory network involved in drug resistance. Our models predict that cells can become resistant to drug treatment without changing their DNA if their regulatory network encodes specific timing properties. This prediction goes beyond conventional wisdom, and is an example of how a multidisciplinary research program can lead to research opportunities that will contribute new knowledge toward solving a problem with significant potential societal implications.

自古以来，人类就使用芽殖酵母来制作面包、啤酒和葡萄酒。今天，我们还使用酵母大规模生产有益的化学物质，否则很难或昂贵的获得，如抗疟疾药物。 酵母的工业用途涉及改变或添加以基因形式编码在其DNA中的信息。虽然科学家可以通过育种来改变DNA，但新科学领域的技术进步要有效得多。这个领域被称为合成生物学，因为它使用生物学来创造或合成新的东西。 合成生物学是关于改变生物体来做我们想要它们做的事情。这可能涉及为细胞提供产生有益化学物质所需的基因，或编写DNA代码，指示细胞在不同情况下表达不同的基因。无论哪种方式，编程'细胞的过程是非常复杂的，因此我们使用数学和计算机来指导我们的工作。 我的研究计划将提高我们编写DNA编码指令的能力，这些指令可以编程细胞的行为。科学家实际上并不擅长这个，我们编写的程序通常不会像预期的那样工作，如果它们工作的话。 为了解决这个问题，合成生物学家研究了自然生物如何调节基因表达。我们知道，与DNA结合并控制附近基因表达的称为转录因子的分子起着关键作用。我们还知道，单个基因受许多不同的转录因子调节，转录因子通过错综复杂的网络相互调节。正是这些网络中的相互联系决定了细胞在不同情况下会表达哪些基因。 我的研究计划的目标是获得关于基因调控网络如何在活酵母细胞内运作的知识。为了实现这一目标，我将把现有的非常可预测的DNA代码连接起来，形成更大的基因调控网络。利用数学建模和先进的DNA操作技术，我将重点解决使基因调控网络复杂化的两个问题：（1）当一个基因受到两个或更多个转录因子的调控时，我们不知道如何预测它们的组合效应;（2）当一个网络涉及两个或更多个相互关联的转录因子时，我们不知道是否可以准确预测结果。 为了进一步测试我们的数学模型，我们将使用相同的技术来研究涉及耐药性的天然基因调控网络。我们的模型预测，如果细胞的调控网络编码特定的时间特性，那么细胞可以在不改变其DNA的情况下对药物治疗产生抗性。这一预测超越了传统智慧，是一个多学科研究计划如何导致研究机会的例子，这些研究机会将为解决具有重大潜在社会影响的问题提供新的知识。