Statistical computation for deciphering biological outcomes in proteins and cells

我们正在见证实验技术的进步，这些技术使得在蛋白质水平上快速收集生物数据成为可能 和细胞。已知的三维蛋白质结构的蛋白质数据库已经 生长迅速。改进的成像技术使细胞运动能够 更详细地追踪到。许多动力系统的组件现在可以 测量更精确，更接近实时。因此，我们可以访问 数据集不仅很大，而且具有高度的复杂性和 结构。这些科学发展反过来又为 新方法的开发利用 这些数据可以回答重要的生物学问题。统计学家在量化科学过程中产生的不确定性方面发挥着关键作用，他们通过建立模型和方法来产生新的见解，并提高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。 根据数据，需要解决的一些具体问题 目前提供给我们的，如下所示。如何调整和结合统计学中的抽样方法 数据驱动学习进一步提高我们预测三维蛋白质的能力 氨基酸序列的结构？我们如何才能更好地理解分子间的联系 细胞的机制与细胞水平的结果，如运动和迁移？我们如何才能更好地将模型与从监测生物系统中收集的噪声数据相匹配？在一起，我们 期望整合来自这些相互关联的生物学方面的知识 蛋白质、基因和细胞，并为 他们的书房。 将要开发的方法将结合现代统计计算技术，特别是来自贝叶斯推理和蒙特卡洛方法领域的技术。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上的“大” 数据“需要高效和精心定制的算法，这些算法也适合 时间和计算的实际限制。 拟议研究的一个重要的长期目标是其 实际的生物医学影响。在药物发现方面，人们越来越感兴趣 在设计氨基酸序列以实现特定的三维结构和 功能。在病理学上，不正常 迁移信号可能导致错误的细胞迁移；彻底理解 因此，细胞迁移机制的研究在医学和治疗中是必不可少的 包括癌症研究在内的各个领域。

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.