VIRTUAL FCS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 荧光相关光谱（FCS）基于荧光颗粒在它们移动通过飞升观测体积时发射的光子的统计分析，提供关于分子的迁移率、浓度、分子复合物的组成、解离常数和反应动力学的定量信息。已建立的FCS理论预测光子到达时间的统计基于粒子的自由扩散，和粒子参与某些种类的反应。我们正在开发一种新的方法，参数优化使用虚拟FCS结合虚拟小区。基于每个物种的分子量，可以预测细胞质中的扩散系数。这些值可用于执行虚拟FCS仿真。通过整合来自多个物种的结果，可以计算模型的虚拟自相关函数。活细胞中虚拟自相关函数与实验FCS自相关函数的比较可用于优化模型中的参数。这代表了一个强大的全局方法，参数优化的虚拟小区。

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.