Algorithms for Molecular Kinetics

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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