Ranked retrieval of Computational Biology models

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Management and provision of computational models

Quantitative models of biological systems provide an understanding of chemical and biological phenomena based on their underlying mechanisms. Moreover, they can be used for example, to predict the behaviour of a system under given conditions or direct future experiments. This has made quantitative models the perfect tools to answer a variety of questions in the biological sciences and has lead to a steady growth of the number of published models.&#xd;&#xa;&#xd;&#xa;To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. BioModels Database(http://www.ebi.ac.uk/biomodels/) has been developed to exactly fulfil those needs. In order to ensure the correctness of the models distributed, their structure and behaviour are thoroughly checked. To ease their understanding, the model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Finally, to allow their reuse, the models are provided encoded in community supported and standardised formats.&#xd;&#xa;&#xd;&#xa;However, the modelling field is constantly evolving and data providers, like BioModels Database, are faced with new challenges. For example, models are getting more and more complex (with for instance the availability of whole organism metabolic network reconstructions) and this has a direct impact on the performance of hosting infrastructures and annotation procedures. Also, models are now being developed collaboratively: this requires new methodologies and systems, akin to the ones used in software development (with for example versioned repositories of models). Moreover, very different kinds of models are being developed by diverse communities, but ultimately their data management needs are very similar.&#xd;&#xa;&#xd;&#xa;This talk will introduce the needs which lead to the development of BioModels Database, present the resource and its current infrastructure and finally discuss the challenges that we are facing today and the plans to overcome them

Annotation-based feature extraction from sets of SBML models

Background: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. Results: In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Conclusions: Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison

Retrieval, alignment, and clustering of computational models based on semantic annotations

As the number of computational systems biology models increases, new methods are needed to explore their content and build connections with experimental data. In this Perspective article, the authors propose a flexible semantic framework that can help achieve these aims

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation

Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE)

The Computational Modeling in Biology Network (COMBINE, http://co.mbine.org/), an initiative whose goal is to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarises the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathons, held in New-York on April 18-22 2011. The first of those meetings hosted 81 attendees, and discussions covered not only the standards part of COMBINE such as BioPAX, SBGN and SBML, but emerging efforts and interoperability between the different formats. The second meeting, oriented towards developers, welcomed 59 participants and witnessed many technical discussions and development enhancing software support of the standards, and conversion between them. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner

Annotation-based storage and retrieval of models and simulation descriptions in computational biology

This work aimed at enhancing reuse of computational biology models by identifying and formalizing relevant meta-information. One type of meta-information investigated in this thesis is experiment-related meta-information attached to a model, which is necessary to accurately recreate simulations. The main results are: a detailed concept for model annotation, a proposed format for the encoding of simulation experiment setups, a storage solution for standardized model representations and the development of a retrieval concept.Die vorliegende Arbeit widmete sich der besseren Wiederverwendung biologischer Simulationsmodelle. Ziele waren die Identifikation und Formalisierung relevanter Modell-Meta-Informationen, sowie die Entwicklung geeigneter Modellspeicherungs- und Modellretrieval-Konzepte. Wichtigste Ergebnisse der Arbeit sind ein detailliertes Modellannotationskonzept, ein Formatvorschlag für standardisierte Kodierung von Simulationsexperimenten in XML, eine Speicherlösung für Modellrepräsentationen sowie ein Retrieval-Konzept

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation

Towards a biological modelling tool recommending proper subnetworks

The aim of this thesis is to develop methods that suggest the users suitable subnetworks for integration during modelling. To this end, techniques from the field of recommender systems are used, which aim to predict the users’ interest in certain objects in order to filter and recommend the most suitable ones. Especially association rule mining is of particular relevance in this thesis. Its algorithms offer the opportunity to find patterns of joint appearance in a large set of items. For this purpose, biological networks are considered, which are represented as graphs and annotated with standardised ontology terms. Association rule mining then is applied with respect to structural and also to semantic similarity. For a partly modelled biological network the elements are found that may extend it. The obtained results form a solid basis for the development of a recommender system that facilitates the efficient reuse of networks and decreases the manual effort to find and integrate relevant structures

A Knowledge Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing

Due to the needs in supporting lifecycle activities of computational models in Smart Manufacturing (SM), a Knowledge Enriched Computational Model (KECM) is proposed in this dissertation to capture and integrate domain knowledge with standardized computational models. The KECM captures domain knowledge into information model(s), physics-based model(s), and rationales. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model. To support the deployment of computational models in SM systems, the KECM can be used for data integration between computational models and SM systems. A case study has been developed to show the deployment of a Constraint Programming optimization model into a Business To Manufacturing Markup Language (B2MML) -based system. In another situation where multiple computational models need to be deployed, the KECM can be used to support the combination of computational models. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. To support model retrieval, a semantics-based method is suggested in this dissertation. As an example, a dispatching rule model retrieval problem has been addressed with a semantics-based approach. The semantics-based approach has been verified and it demonstrates good capability in using the KECM to retrieve computational models