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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-Q (01))
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Lyster, Peter
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State University of New York at Buffalo
Schools of Medicine
United States
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Yao, Jing; Liu, Beiying; Qin, Feng (2011) Modular thermal sensors in temperature-gated transient receptor potential (TRP) channels. Proc Natl Acad Sci U S A 108:11109-14
Liu, Beiying; Yao, Jing; Zhu, Michael X et al. (2011) Hysteresis of gating underlines sensitization of TRPV3 channels. J Gen Physiol 138:509-20
Qin, Feng (2010) Hill coefficients of a polymodal Monod-Wyman-Changeux model for ion channel gating. Biophys J 99:L29-31