Stochastic Networks in Nanoscale Biophysics Modeling Enzymatic Reaction of a Single Protein

• Published in: Journal of the American Statistical Association Vol. 103; no. 483; pp. 961 - 975
• Main Author: Kou, S. C
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.09.2008; American Statistical Association; Taylor & Francis Ltd
• Subjects: Applications; Applications and Case Studies; Autocorrelation function; Biology; Biophysics; Continuous-time Markov chain; Distribution theory; Enzymatic reactions; Enzymes; Exact sciences and technology; Experimental data; First passage time; Fluorescence; General topics; Goodness of fit; Markov chains; Markovian processes; Mathematics; Maximum likelihood estimates; Michaelis-Menten model; Modeling; Molecules; Nanotechnology; Physics; Physiology; Probability and statistics; Probability theory and stochastic processes; Reaction kinetics; Reaction rate; Sciences and techniques of general use; Scientific research; Statistical analysis; Statistical methods; Statistics; Stochastic models; Stochastic processes; Autocorrelation function; Stochastic model; Michaelis-Menten model; Experimental data; Continuous-time Markov chain; Goodness of fit test; Biological model; Reaction rate; Goodness of fit; Statistical method; Time distribution; Distribution function; First passage time; Maximum likelihood estimates; Application

Advances in nanotechnology enable scientists for the first time to study biological processes on a nanoscale molecule-by-molecule basis. A surprising discovery from recent nanoscale single-molecule biophysics experiments is that biological reactions involving enzymes behave fundamentally differently...