VIRTUAL FCS

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 荧光相关光谱(FCS)基于对荧光粒子在飞升观测体积中移动时发出的光子的统计分析，提供了关于分子迁移率、浓度、分子络合物组成、解离常数和反应动力学的定量信息。已建立的FCS理论基于粒子的自由扩散以及参与某些类型的反应的粒子来预测光子到达时间的统计。我们正在开发一种使用虚拟FCS和虚拟单元进行参数优化的新方法。根据每个物种的分子质量，可以预测细胞质中的扩散系数。然后可以使用这些值来执行虚拟FCS仿真。通过综合来自多个物种的结果，可以计算出该模型的虚拟自相关函数。通过比较活细胞中的虚拟自相关函数和实验FCS自相关函数，可以优化模型中的参数。这代表了一种在虚拟单元中进行参数优化的强大的全局方法。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Fluorescence correlation spectroscopy (FCS) based on statistical analysis of photons emitted by fluorescent particles as they move through a femtoliter observation volume provides quantitative information about mobility of molecules, concentration, composition of molecular complexes, dissociation constants, and reaction kinetics. The established theory of FCS predicts the statistics of photon arrival times based on free diffusion of particles, and particles involved in certain kinds of reactions. We are developing a new method for parameter optimization using virtual FCS in conjunction with Virtual Cell. Based on the molecular mass of each species the diffusion coefficient in the cytoplasm can be predicted. These values can then be used perform a virtual FCS simulation. By integrating the results from multiple species it is possible to calculate a virtual autocorrelation function for the model. Comparison of the virtual autocorrelation function to the experimental FCS autocorrelation function in live cells can be used to optimize parameters in the model. This represents a powerful global approach to parameter optimization in the Virtual Cell.