Show simple item record

dc.contributor.authorZenil, Hector
dc.contributor.authorKiani, Narsis A.
dc.contributor.authorMarabita, Francesco
dc.contributor.authorDeng, Yue
dc.contributor.authorElias, Szabolcs
dc.contributor.authorSchmidt, Angelika
dc.contributor.authorBall, Gordon
dc.contributor.authorTegner, Jesper
dc.date.accessioned2019-10-13T11:33:15Z
dc.date.available2017-09-14T06:03:51Z
dc.date.available2019-10-13T11:33:15Z
dc.date.issued2019-09-27
dc.identifier.citationZenil H, Kiani NA, Marabita F, Deng Y, Elias S, et al. (2017) An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems. Available: http://dx.doi.org/10.1101/185637.
dc.identifier.doi10.1016/j.isci.2019.07.043
dc.identifier.doi10.1101/185637
dc.identifier.urihttp://hdl.handle.net/10754/625448
dc.description.abstractWe introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information. By applying sequences of controlled interventions to systems and networks, we estimate how changes in their algorithmic information content are reflected in positive/negative shifts towards and away from randomness. The strong connection between approximations to algorithmic complexity (the size of the shortest generating mechanism) and causality induces a sequence of perturbations ranking the network elements by the steering capabilities that each of them is capable of. This new dimension unmasks a separation between causal and non-causal components providing a suite of powerful parameter-free algorithms of wide applicability ranging from optimal dimension reduction, maximal randomness analysis and system control. We introduce methods for reprogramming systems that do not require the full knowledge or access to the system's actual kinetic equations or any probability distributions. A causal interventional analysis of synthetic and regulatory biological networks reveals how the algorithmic reprogramming qualitatively reshapes the system's dynamic landscape. For example, during cellular differentiation we find a decrease in the number of elements corresponding to a transition away from randomness and a combination of the system's intrinsic properties and its intrinsic capabilities to be algorithmically reprogrammed can reconstruct an epigenetic landscape. The interventional calculus is broadly applicable to predictive causal inference of systems such as networks and of relevance to a variety of machine and causal learning techniques driving model-based approaches to better understanding and manipulate complex systems.
dc.description.sponsorshipWe want to thank the reviewers for their valuable input. Author Contributions H.Z., N.A.K., and J.T. conceived and designed the methods. H.Z. and N.A.K. are responsible for data acquisition. H.Z., N.A.K., Y.D., F.M. contributed to data analysis. H.Z. and N.A.K. developed the methodology, with key contributions from J.T. H.Z. performed most of the numerical experiments with Y.D. and F.M. also contributing. H.Z., A.S., G.B., and S.E. contributed the literature-based Th17 enrichment analysis. H.Z. and J.T. wrote the article with key contributions from N.A.K. H.Z. was supported by the Swedish Research Council grant no. 2015-05299. Declaration of Interests The authors declare no competing interests.
dc.publisherElsevier BV
dc.publisherElsevier
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S2589004219302706?via%3Dihub#ack0010
dc.rightsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleAn Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journaliScience
dc.identifier.wosutWOS:000488278300096
dc.eprint.versionPublisher's version/PDF
dc.contributor.institutionAlgorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, 75006, France.
dc.contributor.institutionScience for Life Laboratory, Solna,171 65, Sweden
dc.contributor.institutionDepartment of Computer Science, University of Oxford, Oxford, OX1 3QD, UK.
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
pubs.publication-statusPublished
kaust.personTegner, Jesper
dc.relation.issupplementedbyDOI:10.5281/zenodo.2629312
refterms.dateFOA2018-06-14T05:27:06Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Zenil, H., Zea, A. A., &amp; Rueda-Toicen, A. (2019). <i>allgebrist/algodyn: First release!</i> (Version v1.0.3) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.2629312. DOI: <a href="https://doi.org/10.5281/zenodo.2629312" >10.5281/zenodo.2629312</a> Handle: <a href="http://hdl.handle.net/10754/667039" >10754/667039</a></a></li></ul>
dc.date.published-online2019-08-08
dc.date.posted2017-09-07


Files in this item

Thumbnail
Name:
1-s2.0-S2589004219302706-main.pdf
Size:
6.995Mb
Format:
PDF
Description:
publisher's version

This item appears in the following Collection(s)

Show simple item record

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
VersionItemEditorDateSummary

*Selected version