Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for 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 survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users’ intuition about model similarity, and to support complex model searches in databases.
|Original language||English (US)|
|Journal||Briefings in Bioinformatics|
|State||Published - Oct 14 2016|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This article was drafted during a meeting that was organized by D.W. and funded through the BMBF e:Bio program (grant no. FKZ0316194). R.H. is funded by the German Federal Ministry of Education and Research (BMBF; grant number FKZ 031 A540A [de.NBI]). The Junior Research Group SEMS, BMBF e:Bio program (grant no. FKZ0316194 to D.W.). T.K. is funded by the German Federal Ministry of Education and Research (BMBF) via the Greifswald Approach to Individualized Medicine (GANI_MED; grant 03IS2061A) and by the Unternehmen Region as part of the ZIK-FunGene (grant 03Z1CN22). German Research Foundation (grant no. Ll 1676/2-1 to W.L.).