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dc.contributor.authorTegner, Jesper
dc.contributor.authorZenil, Hector
dc.contributor.authorKiani, Narsis A.
dc.contributor.authorBall, Gordon
dc.contributor.authorGomez-Cabrero, David
dc.date.accessioned2017-10-17T08:48:34Z
dc.date.available2017-10-17T08:48:34Z
dc.date.issued2016-11-13
dc.identifier.citationTegnér J, Zenil H, Kiani NA, Ball G, Gomez-Cabrero D (2016) A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374: 20160144. Available: http://dx.doi.org/10.1098/rsta.2016.0144.
dc.identifier.issn1364-503X
dc.identifier.issn1471-2962
dc.identifier.doi10.1098/rsta.2016.0144
dc.identifier.urihttp://hdl.handle.net/10754/625865
dc.description.abstractSystems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.
dc.description.sponsorshipThis work was supported by the following grants to J.T.: Hjärnfonden, ERC Consolidator, Torsten Söderberg Foundation, Stockholm County Council, Swedish Excellence Center for e-Science and Swedish Research Council (3R program MH and project grant NT). H.Z. was supported by Swedish Research Council (NT). N.K. was supported by a fellowship from VINNOVA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.publisherThe Royal Society
dc.relation.urlhttp://rsta.royalsocietypublishing.org/content/374/2080/20160144
dc.rightsPublished 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.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
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.journalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionScience for Life Laboratory, Stockholm, Sweden
dc.contributor.institutionDepartment of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden
dc.contributor.institutionCenter for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden
dc.contributor.institutionDepartment of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden
dc.contributor.institutionMucosal and Salivary Biology Division, King’s College London Dental Institute, London SE1 9RT, UK
kaust.personTegner, Jesper
refterms.dateFOA2018-06-14T05:23:58Z
dc.date.published-online2016-11-13
dc.date.published-print2016-11-13


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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.
Except where otherwise noted, this item's license is described as 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.