CCF: AF: Small: Novel Stochastic Optimization Algorithms to Advance the Treatment of Dynamic Molecular Systems

CCF：AF：Small：新型随机优化算法推进动态分子系统的治疗

• 批准号: 1421001
• 负责人: Amarda Shehu
• 金额: $ 40万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421001&HistoricalAwards=false
• 关键词: CCF; AF; Small; Novel; Stochastic

This project aims to advance algorithmic research in computational biology. The focus is on problems that demand characterizations of molecules, such as proteins and peptides that are central to the inner workings of cell. These molecules often switch between different structural configurations to be compatible and interact with different molecular partners in the cell. Computing the different configurations a protein or peptide takes on for biological activity is central to understanding how these molecules operate in the healthy and diseased cell. Doing so, however, poses many computational challenges, and the research in this project aims to advance our ability to meet these challenges.The project generalizes the problem of obtaining a comprehensive view of possibly diverse functional states as that of obtaining a diverse ensemble of constraint-satisfying structures. Novel stochastic optimization techniques are put forth to deal with the high-dimensional variable spaces arising in these molecules, as well as the nonlinearity and multimodality of the energy surface associated with these spaces. The techniques include hybridization and structurization to effectively balance computational resources between exploration and exploitation. The focus is on blending the two in parallel architectures. Novel multi-objective analysis addresses the inherent noise in energy functions. Accumulated biophysical knowledge drives the design of novel variation operators for effective global and local search. The research is made available as software to facilitate advancement of stochastic optimization while allowing computational biologists to model specific systems of interest with cutting-edge techniques. The research supports a number of coordinated teaching and outreach activities across computer science, bioengineering, and neuroscience, integrating postdoctoral, college, and pre-college students.

该项目旨在推进计算生物学的算法研究。重点是需要分子表征的问题，例如对细胞内部运作至关重要的蛋白质和肽。这些分子经常在不同的结构构型之间切换，以与细胞中的不同分子伙伴兼容并相互作用。计算蛋白质或肽具有生物活性的不同构型对于了解这些分子如何在健康和患病细胞中发挥作用至关重要。然而，这样做会带来许多计算挑战，该项目的研究旨在提高我们应对这些挑战的能力。该项目将获得可能不同的功能状态的全面视图的问题概括为获得不同的约束集合的问题- 令人满意的结构。提出了新颖的随机优化技术来处理这些分子中出现的高维变量空间，以及与这些空间相关的能量表面的非线性和多模态。这些技术包括混合和结构化，以有效平衡探索和利用之间的计算资源。重点是将两者混合在并行架构中。新颖的多目标分析解决了能量函数中固有的噪声。积累的生物物理知识推动了新型变异算子的设计，以实现有效的全局和局部搜索。该研究以软件形式提供，以促进随机优化的进步，同时允许计算生物学家利用尖端技术对感兴趣的特定系统进行建模。该研究支持计算机科学、生物工程和神经科学领域的一系列协调教学和外展活动，将博士后、大学生和预科生整合在一起。