Statistical computation for deciphering biological outcomes in proteins and cells

我们正在目睹实验技术的进步，这些技术使我们能够在蛋白质水平上快速收集生物数据 和细胞。蛋白质数据库中已知的3-D蛋白质结构已经被 生长迅速。改进的成像技术允许细胞运动被 跟踪更详细。许多动力系统的组成部分现在可以 更精确地测量，更接近实时。因此，我们有机会 数据集不仅很大，而且复杂程度很高， 结构这些科学发展反过来又为 开发新的方法， 这些数据来回答重要的生物学问题。 统计学家在量化科学过程中出现的不确定性方面发挥着关键作用，他们建立的模型和方法可以产生新的见解，并提高我们预测生物结果的能力。 该提案提出了一项研究计划，用于开发必要的分析和计算方法，以促进我们对管理蛋白质，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 目前我们可以使用的方法如下。 统计学中的抽样方法如何适应和结合 数据驱动学习，进一步提高我们预测3D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之间的联系 在细胞水平的结果，如运动和迁移的细胞机制？ 我们如何才能更好地将模型拟合到从监测生物系统中收集的噪声数据中？ 一起我们 预期从这些相互关联的生物学方面整合知识 蛋白质，基因和细胞，并提供坚实的统计基础， 他们的研究。 开发的方法将结合现代统计计算技术，特别是贝叶斯推理和蒙特卡罗方法。 我们还将充分利用现代硬件进行计算，利用大规模并行化以及CPU和GPU计算。 特别是，处理这些生物“大” 数据”需要高效和精心定制的算法，也适合 时间和计算的实际限制。 拟议研究的一个重要长期目标是其 实际的生物医学影响。在药物发现方面， 设计氨基酸序列以获得特定的三维结构， 功能 在病理学上， 迁移信号可能导致错误的细胞迁移;彻底了解 因此，细胞迁移机制的研究在医学和治疗中至关重要。 领域，包括癌症研究。

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.