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
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/016214507000001021

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 from what classical theory predicts. In this article we introduce a stochastic network model to explain the experimental puzzles (by modeling enzymatic reactions as a stochastic network connected by different enzyme conformations). Detailed analyses of the model, including analyses of the first-passage-time distributions and goodness of fit, show that the stochastic network model is capable of explaining the experimental surprises. The model is analytically tractable and closely fits experimental data. The biological/chemical meaning of the model is discussed.