dc.contributor.author |
Douglas, Jordan |
en |
dc.contributor.author |
Kingston, Richard |
en |
dc.contributor.author |
Drummond, Alexei |
en |
dc.date.accessioned |
2020-04-09T01:29:31Z |
en |
dc.date.issued |
2020-02-14 |
en |
dc.identifier.issn |
1553-734X |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/50349 |
en |
dc.description.abstract |
Transcription elongation can be modelled as a three step process, involving polymerase translocation, NTP binding, and nucleotide incorporation into the nascent mRNA. This cycle of events can be simulated at the single-molecule level as a continuous-time Markov process using parameters derived from single-molecule experiments. Previously developed models differ in the way they are parameterised, and in their incorporation of partial equilibrium approximations. We have formulated a hierarchical network comprised of 12 sequence-dependent transcription elongation models. The simplest model has two parameters and assumes that both translocation and NTP binding can be modelled as equilibrium processes. The most complex model has six parameters makes no partial equilibrium assumptions. We systematically compared the ability of these models to explain published force-velocity data, using approximate Bayesian computation. This analysis was performed using data for the RNA polymerase complexes of E. coli, S. cerevisiae and Bacteriophage T7. Our analysis indicates that the polymerases differ significantly in their translocation rates, with the rates in T7 pol being fast compared to E. coli RNAP and S. cerevisiae pol II. Different models are applicable in different cases. We also show that all three RNA polymerases have an energetic preference for the posttranslocated state over the pretranslocated state. A Bayesian inference and model selection framework, like the one presented in this publication, should be routinely applicable to the interrogation of single-molecule datasets. |
en |
dc.format.medium |
Electronic-eCollection |
en |
dc.language |
eng |
en |
dc.relation.ispartofseries |
PLoS computational biology |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
Escherichia coli |
en |
dc.subject |
Bacteriophage T7 |
en |
dc.subject |
Saccharomyces cerevisiae |
en |
dc.subject |
DNA-Directed RNA Polymerases |
en |
dc.subject |
Bayes Theorem |
en |
dc.subject |
Markov Chains |
en |
dc.subject |
Stochastic Processes |
en |
dc.subject |
Transcription, Genetic |
en |
dc.subject |
Kinetics |
en |
dc.subject |
Models, Genetic |
en |
dc.title |
Bayesian inference and comparison of stochastic transcription elongation models. |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1371/journal.pcbi.1006717 |
en |
pubs.issue |
2 |
en |
pubs.begin-page |
e1006717 |
en |
pubs.volume |
16 |
en |
dc.rights.holder |
Copyright: The author |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Comparative Study |
en |
pubs.subtype |
Research Support, Non-U.S. Gov't |
en |
pubs.subtype |
research-article |
en |
pubs.subtype |
Journal Article |
en |
pubs.elements-id |
796811 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Biological Sciences |
en |
pubs.org-id |
Science Research |
en |
pubs.org-id |
Maurice Wilkins Centre (2010-2014) |
en |
dc.identifier.eissn |
1553-7358 |
en |
pubs.record-created-at-source-date |
2020-02-15 |
en |
pubs.dimensions-id |
32059006 |
en |