Algorithms for Molecular Kinetics

科学通过它的工具进步，这个提议是一个更新的应用程序，以继续开发QuB， 分子动力学分析软件包。集成的QuB软件包可以分析任何 用马尔可夫模型描述的系统。它运行在Windows下的Intel PC上，并提供各种辅助功能。 信号处理和报告生成工具。QuB还处理数据采集和信号调理。核心 从数据中求解速率常数的逆马尔可夫算法可以理想化数据或对原始数据进行操作。 数据拟合提供了参数的误差限制以及用于模型比较的对数似然。变量 度量优化器使用似然函数的分析导数，并接受许多先验约束， 详细平衡程序可以应用于不同的刺激，如不同的电压，膜张力，或 配体浓度来求解对速率常数的刺激依赖性贡献。理想化的数据是 可用于静态或动态不均匀性等的各种排序。数据库操作允许绘图 任何参数（振幅、概率、持续时间、标准偏差、爆发长度、局部似然性等）与任何其他参数的比较 或时间或位置记录。提供了任意函数对所有数据的曲线拟合。自动化 在Word和Excel中生成输出报告。QuB模型库包括分子马达的阶梯模型， 合并和优化多个通道同时活动的模型，解决宏观动力学 （多通道）的马尔可夫模型，而不是时间常数，并完成模拟在单一的 渠道和宏观层面。Python脚本编写工具允许用户自动化， 提供在线命令和教程。我们建议继续开发和维护QuB。我们将 开发非理想马尔可夫数据的算法，指导模型识别，提高计算效率， 非平稳刺激设计与分析。我们将创建交互式向导，用于简化 初学者，继续教授QuB软件课程，并写一本关于用QuB做动力学的书。

Science advances through its tools, and this proposal is a renewal application to continue development of QuB, a software toolkit for molecular kinetic analysis. The integrated QuB software package can analyze the kinetics of any system that is described by a Markov model. It runs on Intel PCs under Windows, and provides a variety of auxiliary signal processing and report generating tools. QuB also handles data acquisition and signal conditioning. The core inverse Markov algorithms ¿ solving for rate constants from the data ¿ can idealize the data or operate on the raw data. The fitting provides error limits on the parameters as well as the logliklihood for model comparison. The varible metric optimizers use analytical derivatives of the likelihood function and accept many a priori constraints including detailed balance. The routines can be applied across varying stimuli such as different voltages, membrane tensions, or ligand concentrations to solve for the stimulus dependent contributions to the rate constants. The idealized data is available for a variety of sorting for stationarity or kinetic inhomogenity, etc. The database operations permit plotting any parameter (amplitude, probability, duration, standard deviations, burst length, local likihood etc) against any other or time or position in the record. Curve fitting of arbitrary functions to all data is provided. The programautomatically generates output reports in Word and Excel. The QuB model library includes staircase models for molecular motors, the merging and optimization of models for multiple simultaneous channel activity, solving macroscopic kinetics (multiple channels) in terms of Markov models instead of time constants, and complete simulation at the single channel and the macroscopic level. Python scripting tools allow for user automation, and extensive user help is provided with on-line commands and tutorials. We propose to continue QuB development and maintenance. We will develop algorithms for nonideal Markov data, guided model identification, improvement of computational efficiency, and non-stationary stimulus design and analysis. We will create interactive wizards for simplifying typical tasks for beginners, continue to teach the QuB software course, and write a book on doing kinetics with QuB.