Statistical computation for deciphering biological outcomes in proteins and cells

我们正在见证实验技术的进步，这些技术使我们能够在蛋白质和细胞水平上快速收集生物数据。已知的三维蛋白质结构的蛋白质数据库一直在快速增长。改进的成像技术可以更详细地跟踪细胞运动。许多动力系统的组件现在可以更精确、更接近实时地测量。因此，我们不仅可以访问大型数据集，而且可以访问具有高度复杂性和结构的数据集。这些科学发展反过来又为开发利用这些数据来回答重要生物学问题的新方法提供了肥沃的土壤。统计人员在量化科学过程中出现的不确定性方面发挥了关键作用，他们建立了产生新见解的模型和方法，并提高了我们预测生物结果的能力。这项建议提出了一个研究计划，用于开发必要的分析和计算方法，以促进我们对控制蛋白质、基因和细胞的复杂生物机制以及它们之间的相互作用的科学理解。根据我们目前掌握的数据，一些需要解决的具体问题如下：如何调整统计学中的采样方法并将其与数据驱动学习相结合，以进一步提高我们从氨基酸序列预测三维蛋白质结构的能力？我们如何更好地理解细胞中分子机制与细胞水平的结果之间的联系，如运动性和迁移性？我们如何更好地将模型与从监测生物系统收集的噪声数据相匹配？我们期待着整合来自这些相互关联的生物方面-蛋白质、基因和细胞-的知识，并为他们的研究提供坚实的统计学基础。即将开发的方法将融合现代统计计算技术，特别是来自贝叶斯推理和蒙特卡罗方法领域的技术。我们还将充分利用现代硬件进行计算，利用大规模并行以及CPU和GPU计算。尤其是，处理这些生物的“大数据”需要高效和精心定制的算法，这些算法也符合实际的时间和计算限制。这项拟议研究的一个重要长期目标是其实际的生物医学影响。在药物发现中，人们对设计氨基酸序列以实现特定的三维结构和功能越来越感兴趣。在病理学中，异常的迁移信号可能导致错误的细胞迁移；因此，深入了解细胞迁移机制在包括癌症研究在内的医学和治疗领域是必不可少的。

We are witnessing advances in experimental techniques that have enabled the rapid collection of biological data at the levels of proteins and cells. The Protein Data Bank of known 3-D protein structures has been growing quickly. Improved imaging techniques permit cellular motions to be tracked with greater detail. The components of many dynamical systems can now be measured more precisely and closer to real-time. As a result, we have access to datasets that are not only large, but with a high level of complexity and structure. These scientific developments, in turn, provide fertile ground for the development of novel approaches that leverage these data to answer important biological questions.  Statisticians have a key role in quantifying uncertainty that arises in the scientific process, by building models and methods that yield new insights and improve our capacity to make predictions of biological outcomes. This proposal advances a research program for developing the necessary analytical and computational methods to advance our scientific understanding of the complex biological mechanisms governing proteins, genes, and cells, and the interplay between them. Some specific questions to be addressed, in light of data currently available to us, are as follows.  How can sampling methods in statistics be adapted and combined with data-driven learning to further improve our ability to predict 3-D protein structure from amino acid sequence?  How can we better understand the links between molecular mechanisms in cells with cellular-level outcomes such as motility and migration?  How can we better fit models to noisy data collected from monitoring biological systems?  Together, we anticipate integrating knowledge from these interconnected biological facets - proteins, genes, and cells - and providing solid statistical foundations for their study. The methods to be developed will incorporate techniques of modern statistical computation, especially from the realms of Bayesian inference and Monte Carlo methods.  We will also fully utilize modern hardware for computation, leveraging mass parallelization as well as both CPU and GPU computing.  In particular, handling these biological "big data" requires efficient and carefully-tailored algorithms that also fit within practical constraints of time and computing. An important long-term goal of the proposed research is its practical biomedical impacts. In drug discovery, there is increasing interest in designing amino acid sequences to achieve specific 3-D structure and function.  In pathology, abnormal migratory signals may cause erroneous cell migrations; a thorough understanding of cell migration mechanisms is therefore essential in medical and therapeutic domains, including cancer research.