Algorithms for Molecular Kinetics

摘要 QuB是传统上用于分析单离子数据的计算工具 渠道它是一个通用程序，能够处理刺激和数据记录， 模拟数据，并解决提取动力学的逆马尔可夫问题 产生数据的模型。在过去的15年里，它一直由NIH资助， 还有NSF、Keck基金会和IBM。它被下载（免费）数千 每年都有几次，并在许多出版物中引用。我们亲自培训了150多名 来自学术界，工业界和政府的学生在年度会议上使用该计划 当然了近年来，该程序已被应用于数据分析， 分子马达、自旋电子学和小鼠的睡眠周期。它适用于任何系统 其特征在于马尔可夫动力学，即状态模型。QuB是唯一可用于 研究人员，商业或免费的，可以做到这一点，而无需编程。 QuB在过去的二十年里一直在增长，现在包含大约30万行 四种语言的代码许多自定义例程已添加到解决 直接的问题从来没有记录下来，这些问题导致了一些深奥的代码 这是不容易运输的。本申请建议重写并记录QuB 源代码使用Python处理接口，C++处理计算引擎。我们将 使新代码开放源代码，以便用户可以进行修改。降低 倾斜的学习曲线，菜单将更加分层，使最常见的 可以用尽可能少的鼠标/键盘敲击来执行功能。我们将设置 使用典型的松散耦合的网络计算机， 实验室环境，以加快大型数据集和多个模型的处理。 我们将创建向导，引导外行用户通过模拟和分析，并创建 各种主题的教学DVD。这些视频可从以下网站下载： 我们的wiki站点。

Abstract QuB is a computational tool used traditionally to analyze the data from single ion channels. It is a general program capable of handling stimulus and data recording, the simulation of data, and solving the inverse Markov Problem of extracting the kinetic model that produced the data. It has been funded for the last 15 years by the NIH and also by NSF, the Keck Foundation, and IBM. It is downloaded (for free) thousands of times a year and is cited in many publications. We have personally trained more than 150 students from academia, industry and government in use of the program at an annual course. In recent years the program has been applied to the analysis of data from molecular motors, spintronics and the sleep cycles in mice. It applies to any system characterized by Markov kinetics, i.e. state models. QuB is the only program available to researchers, commercial or free, that can do this without programming. QuB has been growing for the last twenty years and now contains about 300,000 lines of code in four languages. Many of the custom routines that have been added to solve immediate problems were never documented and these have lead to some esoteric code that is not readily transported. This application proposes to rewrite and document QuB source code using Python to handle the interfaces and C++ the compute engines. We will make the new code open source so that users can make modifications. To decrease the slope of the learning curve, the menus will be more hierarchical so that the most common functions can be executed with the fewest possible mouse/keyboard strokes. We will set up QuB to utilize the loosely coupled networked computers found in the typical laboratory environment to speed up the processing of large data sets and multiple models. We will create Wizards to lead the lay user through simulation and analysis and create instructional DVDs on various topics. These videos will be available for download from our wiki site.