Statistical Models of Biopolymer Sequence and Folding

题目：生物聚合物序列和折叠的统计模型摘要：本研究涉及到用于生物聚合物序列分析的新的概率和贝叶斯统计模型的发展。重点是蛋白质和RNA的预测建模。研究了具有复杂长短期相互作用结构的随机变量序列的模型。特别的重点放在统一统计模型估计从数据和统计力学模型估计通过实验参数测量聚合物折叠。目标应用包括蛋白质结构预测，蛋白质折叠动力学和蛋白质- rna结合。统计方法的发展侧重于将序列分析和变化点问题的统计模型（包括图形马尔可夫模型和随机场）与聚合物折叠的统计力学模型（特别是生物聚合物（蛋白质和RNA））联系起来，以发展预测理论。该研究计划的另一个核心组成部分涉及这些模型中概率推断的计算方法的开发，包括用于多模态分布和粗略能源景观的新型马尔可夫链蒙特卡罗（MCMC）算法。分子生物科学和生物医学的现代研究越来越依赖于对大量实验数据的计算建模和分析。本研究的重点是开发新的统一的方法来结合这些领域。这项工作利用物理模型来开发改进的统计数据分析方法，并使用统计方法来提高物理模型的预测准确性。重点是高通量全基因组分析产生的蛋白质和核酸序列和结构的分析。这些进展将为计算生物学提供重要的新统计方法，并为领域科学家提供改进的数据分析和预测建模工具。

Proposal ID: 0204690PI: Scott SchmidlerTitle: Statistical models of biopolymer sequence and foldingAbstract:This research involves development of new probabilistic and Bayesian statistical models for the analysis of biopolymer sequences. Emphasis is on predictive modeling of proteins and RNA. Models for sequences of random variables with complex short- and long-range interaction structure are developed and explored. Particular focus is placed unifying statistical models estimated from data with statistical mechanical models of polymer folding estimated via experimental parameter measurement. Targeted applications include protein structure prediction, protein folding kinetics, and protein-RNA binding. Statistical methodology development focuses on connecting statistical models for sequence analysis and change-point problems, including graphical Markov models and random fields, to statistical mechanical models of polymer folding, especially on biopolymers (proteins and RNA), to develop predictive theories. An additional core component of this research program concerns development of computational methodology for probabilistic inference in these models, including novel Markov chain Monte Carlo (MCMC) algorithms for multi-modal distributions and rough energy landscapes.Modern research in the molecular biosciences and biomedicine relies increasingly on both computational modeling and analysis of large collections of experimental data. This research concerns development of novel and unified methods for combining these areas. This work leverages physical models to develop improved methods for statistical data analysis, and uses statistical methodology for improving predictive accuracy of physical models. The focus is on analysis of protein and nucleic acid sequences and structures being generated by high-throughput whole-genome analyses. These advances will provide important new statistical methodology for computational biology, as well as provide domain scientists with improved tools for data analysis and predictive modeling.