Show simple item record

dc.contributor.authorKorsbo, Niklas
dc.contributor.authorJönsson, Henrik
dc.date.accessioned2020-07-10T22:08:38Z
dc.date.available2020-07-10T22:08:38Z
dc.date.issued2020-06-29
dc.date.submitted2019-09-26
dc.identifier.issn1553-734X
dc.identifier.otherpcompbiol-d-19-01662
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/307847
dc.description.abstractThoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions or it can prevent an otherwise accurate model from describing experimentally observed dynamics. Here, we perform a computational investigation of sequential multi-step pathway models that contain fewer pathway steps than the system they are designed to emulate. We demonstrate when such models will fail to reproduce data and how detrimental truncation of a pathway leads to detectable signatures in model dynamics and its optimised parameters. An alternative assumption is suggested for simplifying such pathways. Rather than assuming a truncated number of pathway steps, we propose to use the assumption that the rates of information propagation along the pathway is homogeneous and, instead, letting the length of the pathway be a free parameter. We first focus on linear pathways that are sequential and have first-order kinetics, and we show how this assumption results in a three-parameter model that consistently outperforms its truncated rival and a delay differential equation alternative in recapitulating observed dynamics. We then show how the proposed assumption allows for similarly terse and effective models of non-linear pathways. Our results provide a foundation for well-informed decision making during model simplifications.
dc.languageen
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectResearch Article
dc.subjectPhysical sciences
dc.subjectResearch and analysis methods
dc.subjectComputer and information sciences
dc.subjectBiology and life sciences
dc.titleIt’s about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology
dc.typeArticle
dc.date.updated2020-07-10T22:08:38Z
prism.issueIdentifier6
prism.publicationNamePLOS Computational Biology
prism.volume16
dc.identifier.doi10.17863/CAM.54941
dcterms.dateAccepted2020-05-27
rioxxterms.versionofrecord10.1371/journal.pcbi.1007982
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Rao, Christopher V.
dc.contributor.orcidKorsbo, Niklas [0000-0001-9811-3190]
dc.contributor.orcidJönsson, Henrik [0000-0003-2340-588X]
dc.identifier.eissn1553-7358
pubs.funder-project-idGatsby Charitable Foundation (GAT3395-PR4)


Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)