Modeling Biomolecular Transport Processes

生物分子运输过程建模

• 批准号: 0242543
• 负责人: Timothy Elston
• 金额: $ 4.6万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2004-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0242543&HistoricalAwards=false
• 关键词: Modeling; Biomolecular; Transport; Processes

Elston0075821 The broad goal of this project is to gain a mechanisticunderstanding of energy transduction in biomolecular transportprocesses. The project focuses on three specific systems: thebacterial flagellar motor, the motor protein dynein, and proteintranslocation systems found in membranes of the endoplasmicreticulum and mitochondria. While the biology involved in thesesystems is very different, the same mathematical techniques areapplicable for analyzing theoretical models of all three. Tomodel these systems requires the use of Langevin or stochasticdifferential equations. The randomness in these equations comesfrom two sources, thermal diffusion and chemical kinetics.Thermal fluctuations are characterized by the diffusioncoefficient, which can be measured experimentally, and many ofthe important reaction rates are known from biochemical studies.In addition to molecular collisions, electrostatic interactionsare the other dominant forces involved in these transportsystems. If available, structural data are used to determine therelevant electrostatic potentials. Once model equations for thesystems have been developed, numerical and analytical techniquesare used to compare their behavior with experimental data. Thefinal phase of the analysis is to use the mathematical models toproduce experimentally testable predictions. Biological molecular motors are nanometer-sized engines thatuse chemical energy to generate force. A well known molecularmotor is myosin, which is the protein responsible for musclecontraction. Other examples include the flagellar motor, which isused by bacteria for swimming, and dynein, which produces theforce necessary for cilia motion. The broad goal of this projectis to gain a mechanistic understanding of force generation inboth these systems. Current experimental techniques are allowingbiophysicists to study molecular motors at the single moleculelevel, thereby allowing the mechanical properties of motorproteins to be measured. These new physical data in conjunctionwith structural data provide the impetus for renewed theoreticalinvestigations into molecular motor function. An important reasonfor performing a mathematical analysis of force generation isthat it allows a quantitative comparison between experimentaldata and model behavior to be made. The results of such acomparison not only are important for model validation, but alsocan be used to uncover errors in the assumptions underlying themodel. However, the significance of mathematical modeling goesbeyond model validation and lies in its predictive power. Once atheoretical model has been developed that is consistent withcurrent experimental data, it is straightforward to extend theanalysis to include situations that have not yet beeninvestigated in the laboratory. If model predictions are borneout by experiment, further confidence in the reality of the modelis gained. For the systems under consideration in this project,this means a mechanistic understanding of force generation hasbeen achieved. From a technological standpoint, the results ofthese investigations should be relevant for designing andfabricating manmade nanomachines.

Elston0075821这个项目的广泛目标是从机制上了解生物分子运输过程中的能量传递。该项目的重点是三个特定的系统：细菌鞭毛马达，马达蛋白动力蛋白，以及内质网和线粒体膜中的蛋白转位系统。虽然这些系统涉及的生物学非常不同，但相同的数学技术适用于分析这三个系统的理论模型。要建立这些系统的模型，需要使用朗之万方程或随机微分方程。这些方程中的随机性来自两个来源，热扩散和化学动力学。热波动由扩散系数来表征，扩散系数可以通过实验测量，许多重要的反应速率是从生化研究中获知的。除了分子碰撞，静电相互作用是这些运输系统中涉及的另一种主要作用力。如果可能，结构数据被用来确定相关的静电势。一旦建立了系统的模型方程，就可以使用数值和分析技术将它们的行为与实验数据进行比较。分析的最后阶段是使用数学模型来产生可实验检验的预测。生物分子马达是一种纳米大小的发动机，利用化学能产生力量。一种众所周知的分子马达是肌球蛋白，它是负责肌肉收缩的蛋白质。其他例子包括鞭毛马达和动力蛋白，鞭毛马达被细菌用于游泳，动力蛋白产生纤毛运动所需的力量。这个项目的广泛目标是从机械上理解这两个系统中的力的产生。目前的实验技术允许生物物理学家在单分子水平上研究分子马达，从而允许测量马达蛋白质的机械性能。这些新的物理数据与结构数据结合在一起，为分子马达功能的新的理论研究提供了动力。对力的产生进行数学分析的一个重要原因是，它允许在实验数据和模型行为之间进行定量比较。这种比较的结果不仅对模型验证很重要，而且还可以用来揭示模型基础假设中的错误。然而，数学建模的意义不仅仅在于模型的有效性，还在于它的预测能力。一旦建立了与当前实验数据一致的理论模型，就很容易将分析扩展到包括尚未在实验室进行调查的情况。如果模型的预测是通过实验得出的，则对模型的真实性获得了进一步的信心。对于这个项目中考虑的系统，这意味着已经实现了对力产生的机械理解。从技术的角度来看，这些研究的结果应该与人造纳米机器的设计和制造有关。