Background The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind. Results Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models. Conclusions The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.

Key challenges in mobile ad-hoc networks are computational resource constraints, power limitations and time delays in information exchange. Therefore it is necessary to develop intelligent, efficient indexing strategies for knowledge addressing and exchange in dynamic mobile P2P architectures. The short range radio technology Bluetooth suffers from long service discovery delays and high power consumption due to required connection establishment based on its Service-Discovery-Protocol (SDP). To overcome this situation we propose a new approach. By leveraging the capability of the Bluetooth inquiry procedure, we significantly reduce the service discovery time and hence lower the power consumption. Based on a performance evaluation and a comparison with the legacy SDP, we present the benefits of our new approach.

In this paper we present BlueS, a framework for information-service discovery and data exchange in mobile adhoc scenarios. Therefore, its necessary to develop intelligent efficient indexing strategies for knowledge addressing and exchange in dynamic ensembles. Especially in scenarios like MuSAMA [8], a project dealing with Smart-Rooms, data access, query evaluation on mobile devices and retrieval techniques face problems such as resource constraints, power limitations and time delays in information exchange.

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Abstract*Aim*The increasing number of computational models of biological systems demands a better support for search facilities. One possible improvement is to enable ranked retrieval of models based on existing model annotation. Applying existing Information Retrieval (IR) techniques to computational models, we have implemented a new search strategy in BioModels Database. In this search strategy the index of search terms contains not only the model descriptions in the database, but is expanded by including ontological and textual information about a model and its constituents retrieved from external sources. To add more background knowledge, we additionally index the abstracts of the reference publications.  Results  The new search system was integrated into BioModels Database and is based on a Lucene implementation. The model index includes 454 models and 154854 terms, the semantic index 2353 constituents and 437244 terms. Different sources are incorporated in the retrieval and ranking process, the most important of them via MIRIAM annotations to models and model constituents.The search results are sorted with regard to different aspects, i.e. model authors, dates, constituents, or SBML elements. Several state-of-the-art information retrieval features are supported, such as fuzzy, proximity or range search. As a result the user is enabled to specify the criteria relevant for a search, and is provided with a ranked result set of relevant models. The search was implemented in the demo version of BioModels Database and is available at http://www.ebi.ac.uk/biomodels-demo/.A detailed description of the technical realisation is given in:[Henkel et al., 2010]: Henkel, R., Endler, L., Peters, A., Novere, N., and Waltemath, D. (2010). Ranked retrieval of computational biology models. BMC Bioinformatics, 11(1):423+..