KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Permanent link to this recordhttp://hdl.handle.net/10754/618024
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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.
SponsorsThe 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).
PublisherCold Spring Harbor Laboratory Press