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的运动。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，我们可以访问不仅很大，而且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利用这些数据回答重要生物学问题的新方法的发展提供了肥沃的土壤。统计学家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，以开发必要的分析和计算方法，以促进我们对蛋白质、基因和细胞的复杂生物机制以及它们之间相互作用的科学理解。根据我们目前获得的数据，需要解决的一些具体问题如下。如何调整统计学中的抽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蛋白质三维结构的能力？我们怎样才能更好地理解细胞中的分子机制与细胞水平的结果之间的联系，比如运动和迁移？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？我们期待着整合来自这些相互关联的生物学方面的知识，包括蛋白质、基因和细胞，并为它们的研究提供坚实的统计学基础。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。特别是，处理这些生物“大数据”需要高效和精心定制的算法，这些算法也符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生物医学影响。在药物开发中，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。在病理学上，异常的迁移信号可能导致错误的细胞迁移；因此，彻底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至关重要的。

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.