Notions of similarity for computational biology models

Handle URI:
http://hdl.handle.net/10754/618024
Title:
Notions of similarity for computational biology models
Authors:
Waltemath, Dagmar ( 0000-0002-5886-5563 ) ; Henkel, Ron ( 0000-0001-6211-2719 ) ; Hoehndorf, Robert ( 0000-0001-8149-5890 ) ; Kacprowski, Tim ( 0000-0002-5393-2413 ) ; Knuepfer, Christian ( 0000-0002-1323-5445 ) ; Liebermeister, Wolfram ( 0000-0002-2568-2381 )
Abstract:
Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users' intuition about model similarity, and to support complex model searches in databases.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Publisher:
Cold Spring Harbor Laboratory Press
Issue Date:
21-Mar-2016
DOI:
10.1101/044818
Type:
Article
Sponsors:
The authors would like to thank the participants of the 2013 Model Meeting in Rostock (Germany) for valuable discussions on similarity notions of models. The meeting was funded through the BMBF e:Bio program, grant no. FKZ0316194. DW is funded through the Junior Research Group SEMS, BMBF e:Bio program, grant no. FKZ0316194. TK is funded through the BMBF via the Greifswald Approach to Individualized Medicine (GANI_MED) (grant 03IS2061A) and “Unternehmen Region” as part of the ZIK-FunGene (grant 03Z1CN22).
Additional Links:
http://biorxiv.org/content/early/2016/03/21/044818
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorWaltemath, Dagmaren
dc.contributor.authorHenkel, Ronen
dc.contributor.authorHoehndorf, Roberten
dc.contributor.authorKacprowski, Timen
dc.contributor.authorKnuepfer, Christianen
dc.contributor.authorLiebermeister, Wolframen
dc.date.accessioned2016-08-08T10:47:52Z-
dc.date.available2016-08-08T10:47:52Z-
dc.date.issued2016-03-21-
dc.identifier.doi10.1101/044818-
dc.identifier.urihttp://hdl.handle.net/10754/618024-
dc.description.abstractComputational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users' intuition about model similarity, and to support complex model searches in databases.en
dc.description.sponsorshipThe authors would like to thank the participants of the 2013 Model Meeting in Rostock (Germany) for valuable discussions on similarity notions of models. The meeting was funded through the BMBF e:Bio program, grant no. FKZ0316194. DW is funded through the Junior Research Group SEMS, BMBF e:Bio program, grant no. FKZ0316194. TK is funded through the BMBF via the Greifswald Approach to Individualized Medicine (GANI_MED) (grant 03IS2061A) and “Unternehmen Region” as part of the ZIK-FunGene (grant 03Z1CN22).en
dc.language.isoenen
dc.publisherCold Spring Harbor Laboratory Pressen
dc.relation.urlhttp://biorxiv.org/content/early/2016/03/21/044818en
dc.rightsThe copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license. http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titleNotions of similarity for computational biology modelsen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.contributor.institutionHeidelberg Institute for Theoretical Studies, Heidelberg, Germanyen
dc.contributor.institutionDepartment of Functional Genomics Interfaculty Institute for Genetics and Functional Genomics University Medicine and Ernst-Moritz-Arndt University Greifswald Greifswald, Germanyen
dc.contributor.institutionInstitute for Computer Science, University Jena, Jena, Germanyen
dc.contributor.institutionInstitute of Biochemistry, Charité – Universitätsmedizin Berlin, Berlin, Germanyen
dc.contributor.institutionDepartment of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germanyen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHoehndorf, Roberten
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