Quantitative regulatory genomics: networks, cis-regulatory codes, and phenotypic variation

DNA序列的变化如何影响生物特性？这是现代社会的一个中心问题 生物学，以及对它的洞察可以帮助我们理解，尤其是，为什么病人会有反应 不同于相同的处理，或者为什么一些物种表现出在 其他物种。解决这个“从基因型到表型”的问题的一个主要障碍是我们缺乏知识 表型和细胞过程的基因调控机制，以及这些机制是如何 机制是在DNA中编码的。这也导致了确定优先顺序的严重困难 表型连锁的非编码变异(多态)以供进一步研究。由这些驱动 挑战，我的实验室试图开发量化框架来描述和 发现转录调控机制。我们在这方面取得了重大进展 目标有两个主要方向：(1)我们开发了详细的生物物理模型 基因表达的顺式调节编码。使用这些模型，我们已经展示了监管是如何 转录因子结合位点的功能取决于它们的序列和DNA形状，因为 以及它们的‘跨境’，例如，调节剂的细胞浓度，以及 ‘顺式上下文’，例如接近其他TF结合位点和染色质状态。(2)我们有 设计了统计模型来从转录数据中发现TF基因的相互作用，以及 其他类型的“组学”数据(如果有)。与生物学家密切合作，我们已经应用了这些 了解表型的模型，如细胞系中的细胞毒性药物反应，行为反应 社交相遇和胚胎发育。建立在我们坚实的基础上 在过去的工作中，我建议建立一个研究转录调控的研究项目 从整体上看，是顺式和跨式的。我们的新目标将包括：(1)利用我们的 描述两个数据丰富的哺乳动物调控程序的计算、序列水平模型，以及 实验合作剖析了一个关键炎症基因的顺式调控逻辑 大规模平行记者分析，以及我们建模技术的重大进步；(2)新的 用于重建TF-基因相互作用网络的机器学习方法 来自多组学的表型差异、顺式和跨式调控证据的整合 数据，以及将这些方法应用于癌症药物基因组学和行为学的合作 神经基因组学；(3)一种新的概率框架，以结合传统的统计分数 非编码变异体，基于 上述技术。探索这些相关目标之间的新形式的协同 将编织网络重建、顺式调控序列建模和变体解释 在我们的整个研究项目中。

How do changes in DNA sequence impact organismal properties? This is a central question of modern biology, and insights into it can help us understand, among other things, why patients respond differently to the same treatment, or why some species exhibit behavioral properties not seen in other species. A major hurdle in solving this ‘genotype-to-phenotype’ problem is our poor knowledge of gene regulatory mechanisms underlying phenotypes and cellular processes, and how those mechanisms are encoded in DNA. It also leads to severe difficulties in prioritizing phenotype-linked non-coding variants (polymorphisms) for further investigation. Driven by these challenges, my lab seeks to develop quantitative frameworks for describing and discovering transcriptional regulatory mechanisms. We have made significant progress towards this goal in two main directions: (1) We have developed detailed biophysical models of the cis-regulatory encoding of gene expression. Using these models we have shown how the regulatory function of transcription factor (TF) binding sites depends on their sequence and DNA shape, as well as their ‘trans-context’, e.g., cellular concentrations of regulators, and ‘cis-context’, e.g., proximity to other TF binding sites and chromatin states. (2) We have devised statistical models to discover TF-gene interactions from transcriptomic data, as well as other types of ‘omics’ data if available. Working closely with biologists, we have applied these models to understand phenotypes such as cytotoxic drug response in cell lines, behavioral response to social encounters, and embryonic development. Building on the strong foundations of our past work, I propose to establish a research program that studies transcriptional regulation holistically at the cis- and trans- levels. Our new pursuits will include: (1) use of our computational, sequence-level models to describe two data-rich mammalian regulatory programs, an experimental collaboration to dissect the cis-regulatory logic of a key inflammation gene using massively parallel reporter assays, and major advances in our modeling techniques; (2) new machine learning methods for reconstructing networks of TF-gene interactions that explain phenotypic differences, integration of cis- and trans-regulatory evidence from multi-omics data, and collaborations to apply these methods in cancer pharmacogenomics and behavioral neurogenomics; (3) a new probabilistic framework to combine traditional statistical scores of a non-coding variant with quantitative predictions of its regulatory impact based on the above-mentioned techniques. Explorations of new forms of synergy among these related goals of network reconstruction, cis-regulatory sequence modeling and variant interpretation will be woven throughout our research program.