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Collaborative Proposal: CDI-Type I: A multidisciplinary, multiscale approach to discover organizing principles in macromolecular dynamics and functions

Collaborative Proposal: CDI-Type I: A multidisciplinary, multiscale approach to discover organizing principles in macromolecular dynamics and functions
合作提案:CDI-I 型:发现大分子动力学和功能中组织原理的多学科、多尺度方法
批准号:
0835712
负责人:
Mauro Maggioni
金额:
$24.02万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-10-01 至 2013-09-30

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中文摘要
翻译
摘要理解生物分子系统中的合作过程(如蛋白质动力学、折叠和自组装)对理论和实验都提出了突出的挑战。所涉及的大量自由度,以及目前存在的异质性程度,乍一看可能表明缺乏一般原则。正如二十年前弗朗西斯·克里克在评论大分子动力学时所写的那样,“在物理学家看来,这是一个极其复杂的过程,可能是自然发现的最简单的过程。”大分子系统的行为似乎极其复杂。生物相关的大分子系统是数十亿年进化的结果,在此期间,出于功能原因,细节和例外被挑选出来。尽管复杂,但在大分子系统中出现了集体现象,例如在蛋白质折叠和自组装过程中,这表明了组织原则的存在,这些原则实际上可以利用复杂性来获得简单性。我们有可能理解(即,复制、量化和预测)生物分子系统中单个自由度的相互作用如何在广泛的长度和时间尺度上出现组织吗?经验和理论证据支持这样的观点,即对于大多数大分子过程,只访问了构象空间的一小部分,而对于中/长时间尺度,非常少量的参数就足以描述大分子体系的粗略动力学。以前在这个方向上的工作并不是自动的或系统的,主要是由物理直觉驱动的,很少或没有成功的保证。这项工作的目标是开发和应用一种截然不同的方法,将生物和生化方法与物理和数学观点相协调。制定进化用来调节生物分子过程行为的一般“规则”的关键一步在于对忠实地再现感兴趣的大分子过程所需的最小有效参数集进行严格的数学识别和物理上的合理解释。为此,将开发的方法是基于多尺度几何测量理论、调和分析和降维。这些想法的核心将广泛适用于跨越生物物理学和超越生物物理学的大型高维数据集的几何分析,从而产生关于此类数据集的降维和回归的新的通用范例。
英文摘要
AbstractThe understanding of cooperative processes in biomolecular systems (such as protein dynamics, folding, and self-assembly) poses outstanding challenges both for theory and experiment. The large number of degrees of freedom involved, and the degree of heterogeneity present, may at a first glance suggest the lack of general principles. As Francis Crick wrote twenty years ago, commenting on macromolecular dynamics, "what seems to physicists a hopelessly complicated process may have been what Nature found simplest". The behavior of a macro-molecular system appears overwhelmingly complicated. Biologically relevant macro-molecular systems are the result of billions of years of evolution, during which details and exceptions have been selected for functional reasons. In spite of the complexity, collective phenomena emerge in macromolecular systems, as for instance in protein folding and self-assembly processes, suggesting the existence of organizing principles that may actually exploit the complexity to obtain simplicity.Is it possible to understand (that is, reproduce, quantify, and predict) how organization emerges from the interactions of the single degrees of freedom in a biomolecular system, over a broad spectrum of length and timescales? Empirical and theoretical evidence supports the idea that for most macromolecular processes only a small portion of the conformational space is visited, and that for medium/long time scales a very small number of parameters are enough to describe the coarse dynamics of a large macromolecular system. Previous work in this direction has not been automatic or systematic, and has been driven mostly by physical intuition, with little or no guarantee of success.It is the goal of this work to develop and apply a radically different approach, that reconciles biological and biochemical approaches with a physical and mathematical perspective. A key step towards the formulation of the general "rules" that evolution has employed for regulating the behavior of biomolecular processes resides in the mathematically rigorous identification and the physically sound interpretation of the minimal set of effective parameters needed to faithful reproduce the macromolecular process of interest. The methods that will be developed to this end are based on multiscale geometric measure theory, harmonic analysis, and dimensionality reduction. The core of these ideas will be widely applicable to the analysis of the geometry of large high dimensional data sets, across and beyond biophysics, leading to novel general paradigms for dimensionality reduction and regression on such data sets.
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BIGDATA: F: Compositional Learning, Maps and Transfer: Statistical and Machine Learning on Collections of Data Sets
  • 批准号:
    1837991
  • 项目类别:
    Standard Grant
  • 资助金额:
    $70.0万
  • 财政年份:
    2019
  • 负责人:
    Mauro Maggioni
  • 依托单位:
ATD: Estimation and Anomaly Detection for high-dimensional Data, Maps and Dynamic Processes
  • 批准号:
    1737984
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.0万
  • 财政年份:
    2017
  • 负责人:
    Mauro Maggioni
  • 依托单位:
ATD: Online Multiscale Algorithms for Geometric Density Estimation in High-Dimensions and Persistent Homology of Data for Improved Threat Detection
  • 批准号:
    1756892
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.99万
  • 财政年份:
    2016
  • 负责人:
    Mauro Maggioni
  • 依托单位:
Collaborative Proposal: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis
  • 批准号:
    1708353
  • 项目类别:
    Standard Grant
  • 资助金额:
    $14.56万
  • 财政年份:
    2016
  • 负责人:
    Mauro Maggioni
  • 依托单位:
海外基金