Algorithms for Molecular Kinetics

描述(由申请人提供)：科学通过其工具进步，本提案是继续开发用于分子动力学分析的软件工具包QUB的续展申请。集成的QUB软件包可以分析任何由马尔可夫模型描述的系统的动力学。它运行在Windows下的Intel PC上，并提供了各种辅助信号处理和报告生成工具。QUB还负责数据采集和信号调理。核心逆马尔可夫算法--根据数据求解速率常数--可以理想化数据或对原始数据进行操作。该拟合提供了参数的误差限制以及模型比较的可能性。变尺度优化器使用似然函数的解析导数，并接受许多先验约束，包括详细的平衡。这些例程可以应用于不同的刺激，如不同的电压、膜张力或配基浓度，以求出刺激对速率常数的贡献。理想化的数据可用于针对平稳性或动态不均匀性等的各种分类。数据库操作允许根据记录中的任何其他或时间或位置绘制任何参数(幅度、概率、持续时间、标准差、突发长度、局部可能性等)。提供了任意函数对所有数据的曲线拟合。该程序自动生成Word和Excel格式的输出报告。QUB模型库包括分子马达的阶梯模型，多个同时通道活动的模型的合并和优化，用马尔可夫模型而不是时间常数来求解宏观动力学(多个通道)，以及在单通道和宏观水平上完成模拟。Python脚本工具允许用户自动化，并通过在线命令和教程提供广泛的用户帮助。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，引导模型识别，提高计算效率，以及非平稳刺激设计和分析。我们将为初学者创建用于简化典型任务的交互式向导，继续教授QUB软件课程，并撰写一本关于使用QUB进行动力学的书

DESCRIPTION (provided by applicant): 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 likelihood for model comparison. The variable 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 likelihood etc) against any other or time or position in the record. Curve fitting of arbitrary functions to all data is provided. The program automatically 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 non-ideal 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