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. 2017 Jan;14(126):20160833. doi: 10.1098/rsif.2016.0833

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© 2017 The Authors.

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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Figure 10. — Efficient sampling of the full distribution P(n, t) for transcription with upstream cellular drives. We consider upstream drives governed by the Kuramoto promoter model (3.4) for C = 10 000 coupled oscillatory cells. Sample paths of N are simulated directly with the Gillespie algorithm to approximate P(n, t) at time t₁ (bottom, blue). Alternatively, sample paths of X are used to estimate , which is then mixed by performing the numerical integration (2.14) to obtain P(n, t) (top, red). The latter sampling through X is more regular and far less costly: CPU time via N is ≈36000 s, whereas CPU time via X is ≈0.1 s. (Online version in colour.)

Inline graphic — Efficient sampling of the full distribution P(n, t) for transcription with upstream cellular drives. We consider upstream drives governed by the Kuramoto promoter model (3.4) for C = 10 000 coupled oscillatory cells. Sample paths of N are simulated directly with the Gillespie algorithm to approximate P(n, t) at time t₁ (bottom, blue). Alternatively, sample paths of X are used to estimate , which is then mixed by performing the numerical integration (2.14) to obtain P(n, t) (top, red). The latter sampling through X is more regular and far less costly: CPU time via N is ≈36000 s, whereas CPU time via X is ≈0.1 s. (Online version in colour.)