Show simple item record

dc.contributor.authorHiscock, Thomasen
dc.date.accessioned2019-05-22T23:30:26Z
dc.date.available2019-05-22T23:30:26Z
dc.identifier.issn1471-2105
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/293061
dc.description.abstractBACKGROUND: Gene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turing's model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform. Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery. RESULTS: We use gradient-descent optimization algorithms from machine learning to rapidly screen and design gene circuits. With this approach, we found that we could rapidly design circuits capable of executing a range of different functions, including those that: (1) recapitulate important in vivo phenomena, such as oscillators, and (2) perform complex tasks for synthetic biology, such as counting noisy biological events. CONCLUSIONS: Our computational pipeline will facilitate the systematic study of natural circuits in a range of contexts, and allow the automatic design of circuits for synthetic biology. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.
dc.description.sponsorshipThis work was supported by an EMBO Long-term fellowship, ALTF 606–2018.
dc.languageengen
dc.publisherBioMed Cantral
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGene circuitsen
dc.subjectMachine learningen
dc.subjectNumerical screensen
dc.titleAdapting machine-learning algorithms to design gene circuits.en
dc.typeArticle
prism.number214en
prism.publicationNameBMC Bioinformaticsen
prism.volume20en
dc.identifier.doi10.17863/CAM.40210
dcterms.dateAccepted2019-04-02en
rioxxterms.versionofrecord10.1186/s12859-019-2788-3en
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-04-02en
dc.identifier.eissn1471-2105
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2019-04-27en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

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