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. 2013 Nov 18;8(11):e79345. doi: 10.1371/journal.pone.0079345

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© 2013 Maarleveld et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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StochPy plots for single-molecule enzyme activity simulations with StochPy simulations (step, markers, blue) and analytical solutions (solid, black) (A–B) time-series data of and , . (C) three time trajectories that fluctuate around the analytical solution. This analytical solution corresponds to the mean rate of formation, which for stochastic simulations can be obtained by taking the average of many generated time trajectories. (D) event waiting times of product formation peak.

Inline graphic — StochPy plots for single-molecule enzyme activity simulations with StochPy simulations (step, markers, blue) and analytical solutions (solid, black) (A–B) time-series data of and , . (C) three time trajectories that fluctuate around the analytical solution. This analytical solution corresponds to the mean rate of formation, which for stochastic simulations can be obtained by taking the average of many generated time trajectories. (D) event waiting times of product formation peak.