MAMMOTh database

MAMMOTh database is designed for storage and accumulation of elementary subsystem models of biomolecular systems. Elementary subsystem model is the “functional network” model where the entities of the network and the mathematical model parameters are specified. The dynamics of each elementary subsystem model is related to published experimental data, which are referenced in the corresponding “model description” field. Each mathematical model in the database has the description field where one can find references on articles which were the basis for the model development or which contain a quantitative experimental data used for model parameters fitting. The mathematical models were manually curated and verified by expert authors. Each model in the database has been developed on basis of the general entities vocabulary. It makes possible to compare entities in the different models by identifiers directly. Using this property, MAMMOTh provides some functionality for manipulation with models. To follow the “building blocks” strategy for complex models reconstruction we have implemented a method of template models generation reconstructed by the automatic composition from a subset of elementary models which are presented in the database. These assembled models can be exported in such well known formats as SBML, MATLAB, MATHEMATICA, and Pajek for further analysis in other tools.

MAMMOTh web service available by reference:

MAMMOTh web service help available by reference:

MAMMOTh has the REST API with JSON response that available by reference:


This work has been supported by the budget project No.0324-2016-0008 and the RFBR grant Nos. 15-07-03879 and 15-07-05889.

You can help support MAMMOTh by citing our publication when you use MAMMOTh or data from this source: "MAMMOTh: a new database for curated MAthematical Models of bioMOlecular sysTems" Kazantsev F.V., Akberdin I.R., Lashin S.A., Ree N.A., Timonov V.V., Ratushny A.V., Khlebodarova T.M., Likhoshvai V.A. Journal of Bioinformatics and Computational Biology. Vol. 16, No. 1 (2018) 1740010 (16 pages). DOI: 10.1142/S0219720017400108.