FET: Medium: Collaborative Research: Automated Analysis and Exploration of High-dimensional and Multimodal Molecular Energy Landscapes

这个项目中的活动受到阻碍我们在分子水平上理解细胞机制的核心问题的启发。几十年的科学研究表明，生物分子不断地移动，呈现不同的形式，并在它们之间切换，以与细胞中不同的分子伴侣相互作用。它们的运动可以被概念化为能量景观中的跳跃，能量景观通过将具有相似能量的形式组合到状态中来组织分子所呈现的巨大空间。分子能量景观控制着分子的潜在动力学，并揭示了形式，形式变化和功能之间的关系。该项目汇集了工程，数学和生物学的概念和技术，以推进分子能量景观自动化和有效分析和探索的算法研究。特别是，拟议的活动开发先进的数据驱动算法，用于自动检测和提取景观的全局和局部结构，并利用这些结构（及其特性）来推进分子能量景观的计算状态。该项目使计算和生物科学中不同亚社区的研究人员受益。该项目还将产生开源代码、在线教学模块和教程、公开数据、研讨会、软件演示，将提供良好的机会，训练下-代表学生在不同学科和领域的接口。该项目的活动支持在分子水平上理解细胞机制，并推进分子能量景观的计算状态，以支持这种认识该项目推进了进化计算，高维几何和空间统计以及随机优化中探索性景观分析的算法研究，以解决自动化，高效分析和探索高维和多模态景观的基本挑战。主要重点是分子能量景观，组织分子系统的微观状态，管理潜在的动力学，并揭示形式，形式变化和功能之间的关系。该项目提出了数据驱动的技术，以揭示景观的全局和局部结构的底层组织，并利用这些结构制定算法设计原则，通过随机优化有效计算分子能量景观。拟议的活动将作出一般性的贡献，进化计算，随机优化，空间统计和高维几何。特别是，这项工作将使这些社区的研究人员受益，这些社区对复杂动态系统的分子建模和建模具有应用驱动的兴趣。该研究将通过各种渠道进行传播，包括C++，Python和R的开源代码，以便接触到不同的研究人员和学生社区，在线教学模块，在线教程和公开提供的电子商务相关数据。 这个跨学科的项目创造了极好的机会，在优化和搜索，几何，统计学和计算生物学的界面上培养所有背景的学生。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The activities in this project are inspired by central standing questions hampering our ability to understand cellular mechanisms at a molecular level. Decades of scientific inquiry have demonstrated that biological molecules are constantly on the move, assuming different forms and switching between them to interface with different molecular partners in the cell. Their motions can be conceptualized as hops in an energy landscape that organizes the vast space of forms assumed by a molecule by grouping together forms with similar energies into states. Molecular energy landscapes govern the underlying dynamics of a molecule and expose the relationship between form, changes to form, and function. This project brings together concepts and techniques from engineering, mathematics, and biology to advance algorithmic research on automated and efficient analysis and exploration of molecular energy landscapes. In particular, the proposed activities develop advanced, data-driven algorithms for automated detection and extraction of global and local structures of a landscape and utilization of such structures (and their characteristics) to advance the state of computation of molecular energy landscapes. This project benefits researchers of diverse sub-communities in computational and biological sciences. The project will also result in open-source codes, online teaching modules and tutorials, publicly-available data, workshops, software demos, and will provides excellent opportunities to train under-represented students at the interface of different disciplines and domains.The activities in this project support understanding of cellular mechanism at a molecular level and advance the state of computation of molecular energy landscapes in support of such understanding. The project advances algorithmic research in exploratory landscape analysis in evolutionary computation, high-dimensional geometry and spatial statistics, and stochastic optimization to address fundamental challenges in automated, efficient analysis and exploration of high-dimensional and multimodal landscapes. The primary focus is on molecular energy landscapes that organize microstates of a molecular system, govern the underlying dynamics, and expose the relationship between form, changes to form, and function. The project puts forth data-driven techniques to uncover the underlying organization of global and local structures of a landscape and exploit such structures to formulate algorithmic design principles for effective computation of molecular energy landscapes via stochastic optimization. The proposed activities will make general contributions to evolutionary computation, stochastic optimization, spatial statistics, and high-dimensional geometry. In particular, the work will benefit researchers in these communities that have application-driven interests in molecular modeling and modeling of complex, dynamic systems. The research will be disseminated via various venues, including open-source codes in C++, Python, and R so as to reach diverse communities of researchers and students, online teaching modules, online tutorials, and publicly-available landscape-related data. This interdisciplinary project creates excellent opportunities to train under-represented students of all backgrounds at the interface of optimization and search, geometry, statistics, and computational biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.