RT Journal Article
SR Electronic
T1 Notions of similarity for computational biology models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 044818
DO 10.1101/044818
A1 Ron Henkel
A1 Robert Hoehndorf
A1 Tim Kacprowski
A1 Christian Knüpfer
A1 Wolfram Liebermeister
A1 Dagmar Waltemath
YR 2016
UL http://biorxiv.org/content/early/2016/03/21/044818.abstract
AB 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.