Modeling phenotypic transitions in gene expression state space

描述（由申请人提供）：现代生物医学研究中最大的挑战之一是了解基因型和表型之间的联系，更重要的是，创建模型使我们能够预测生物系统对可能影响它们的各种扰动的反应。尽管基因组技术产生了大量数据，并且不断努力开发捕捉生物系统复杂性的模型，但我们在实现这些目标方面尚未取得重大进展。 在这里，我们建议开发新的现象学方法，利用经验数据以及生物医学文献等其他来源的信息，创建可以进一步测试和验证的预测模型。为了了解转录网络的全局特性并预测最有可能影响表型变化的元素，我们将把我们的模型映射到热力学自旋系统，这使我们能够识别强大的基因模块和相关的顺序参数。我们假设这些有序参数以类似于水在固体、液体和气体之间的压力和温度依赖性相变的方式驱动表型转变。 为了开发和测试这种方法，我们将使用目前通过 NHGRI 资助的基因组科学卓越中心 (CEGS) 项目生成的数据，在该项目中，我们正在研究肿瘤病毒衍生基因扰乱细胞网络以驱动细胞转化的机制。我们确定的模块将用于对转化所必需的关键参数进行具体预测，并且将通过使用 RNAi 扰乱细胞网络并通过基因表达分析和表型测定来评估响应来测试这些预测。 通过与我的导师 John Quackenbush 博士以及共同导师 Karl Munger 博士和 Giovanni Parmigiani 博士的合作，这个项目将使我能够制定一个研究计划，促进我从物理学向生物学的过渡，并帮助我获得独立的研究职位。为了进一步支持我的职业发展，我和我的导师制定了严格的培训计划，为我提供统计学和计算生物学的正式培训，以及现代实验室分子生物学的实践培训。 公共卫生相关性：了解基因如何在细胞网络中相互作用以影响细胞从一种状态转变为另一种状态的方式对于理解从发育到人类疾病的各种过程至关重要。我们的项目将使用人类细胞病毒转化的数据以及其他公开信息，开发基因间相互作用及其在此过程中的作用的计算模型；然后，这些模型将被映射到从理论物理学借用的等效系统，以确定控制表型转变的关键参数。这个总体框架将深入了解为什么特定的细胞状态是稳健的，以及干扰它们改变表型的最佳方法。

DESCRIPTION (provided by applicant): One of the greatest challenges in modern biomedical research is to understand the link between genotype and phenotype and, more importantly, to create models that allow us to predict the response of biological systems to various perturbations that can affect them. Despite an explosion of data that has emerged from genomic technologies, and continued efforts to develop models that capture the complexity of biological systems, we have not yet made significant progress in achieving these goals. Here we propose to develop new, phenomenological approaches that leverage empirical data together with information from other sources such as the biomedical literature, to create predictive models that can be further tested and validated. To understand the global properties of transcriptional networks and to predict the elements with the greatest potential to effect phenotypic changes, we will map our models to thermodynamic spin systems which allow us to identify robust gene modules and associated order parameters. We hypothesize that these order parameters drive phenotypic transitions in a manner analogous to the pressure and temperature dependent phase transitions of water between solid, liquid and gas. To develop and test this approach, we will use data currently being generated through the NHGRI-funded Center for Excellence in Genome Science (CEGS) program in which we are investigating the mechanisms by which oncovirus-derived genes perturb cellular networks to drive cellular transformation. The modules we identify will be used to make concrete predictions about key parameters essential for transformation and these predictions will be tested by using RNAi to perturb the cellular networks and evaluating the response by gene expression analysis and phenotypic assays. Working with my mentor, Dr. John Quackenbush, and co-mentors, Dr. Karl Munger and Dr. Giovanni Parmigiani, this project will allow me to develop a research program that will facilitate my transition from physics to biology and help me to secure an independent research position. To further support my career development, my mentors and I have developed a rigorous training program that will provide me with formal training in statistics and computational biology, and hands-on training in modern laboratory molecular biology. PUBLIC HEALTH RELEVANCE: Knowing how genes interact in cellular networks to influence the way cells change from one state to another is essential for understanding a wide variety of processes ranging from development to human disease. Our project will use data on viral transformation of human cells, together with other publicly-available information, to develop computational models of gene-gene interactions and their role in this process; these models will then be mapped to equivalent systems borrowed from theoretical physics to identify crucial parameters governing phenotypic transitions. This general framework will provide insight into why particular cell states are robust and the best ways to perturb them to alter their phenotype.