Ron Henkel

Improving the reuse of computational models through version control

MOTIVATION Only models that are accessible to researchers can be reused. As computational models evolve over time, a number of different but related versions of a model exist. Consequently, tools are required to manage not only well-curated models but also their associated versions. RESULTS In this work, we discuss conceptual requirements for model version control. Focusing on XML formats such as Systems Biology Markup Language and CellML, we present methods for the identification and explanation of differences and for the justification of changes between model versions. In consequence, researchers can reflect on these changes, which in turn have considerable value for the development of new models. The implementation of model version control will therefore foster the exploration of published models and increase their reusability.

Combining computational models, semantic annotations and simulation experiments in a graph database

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/

Annotation-based feature extraction from sets of SBML models

BackgroundModel 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.ResultsIn 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.ConclusionsAnnotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

Information Retrieval in Life Sciences: A Programmatic Survey

Biomedical databases are a major resource of knowledge for research in the life sciences. The biomedical knowledge is stored in a network of thousands of databases, repositories and ontologies. These data repositories differ substantially in granularity of data, storage formats, database systems, supported data models and interfaces. In order to make full use of available data resources, the high number of heterogeneous query methods and frontends requires high bioinformatic skills. Consequently, the manual inspection of database entries and citations is a time-consuming task for which methods from computer science should be applied.Concepts and algorithms from information retrieval (IR) play a central role in facing those challenges. While originally developed to manage and query less structured data, information retrieval techniques become increasingly important for the integration of life science data repositories and associated information. This chapter provides an overview of IR concepts and their current applications in life sciences. Enriched by a high number of selected references to pursuing literature, the following sections will successively build a practical guide for biologists and bioinformaticians.

Reproducibility of Model-Based Results in Systems Biology

Science requires that results are reproducible. This is naturally expected for wet-lab experiments and it is equally important for model-based results published in the literature. Reproducibility, in general, requires standards that provide the information necessary and tools that enable others to re-use this information. In computational biology, reproducibility requires not only a coded form of the model but also a coded form of the experimental setup to reproduce the analysis of the model. Well-established databases and repositories store and provide mathematical models. Recently, these databases started to distribute simulation setups together with the model code. These developments facilitate the reproduction of results. In this chapter, we outline the necessary steps towards reproducing model-based results in computational biology. We exemplify the workflow using a prominent example model of the Cell Cycle and state-of-the-art tools and standards.

Notions of similarity for systems biology models

Abstract 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.

STON: exploring biological pathways using the SBGN standard and graph databases

BackgroundWhen modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.ResultsWe present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.ConclusionSTON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.

Annotation-Based Feature Extraction from Sets of SBML Models

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 can then help to classify models, to identify additional features for model retrieval tasks, or to enable the comparison of sets of models. In this paper, we present four methods for annotation-based feature extraction from model sets. All methods have been used with four different model sets in SBML format and taken from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies for SBML models, namely Gene Ontology, ChEBI and SBO. We find that three of the four tested methods are suitable to determine characteristic features for model sets. 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.

Das Sombi-Framework zum Ermitteln geeigneter Suchfunktionen für biologische Modelldatenbasen

ZusammenfassungDie Wiederverwendung von Simulationsmodellen biologischer Systeme ist mit der ansteigenden Zahl der in Modelldatenbanken gespeicherten Modelle zu einem wichtigen Forschungsproblem geworden. Ein Teilproblem ist die effiziente Suche nach relevanten Modellen in einer Datenbasis. Als Lösungsansatz wurde kürzlich die Nutzung von Information-Retrieval-Techniken für das bewertete Finden von Modellen vorgestellt.Die im Folgenden beschriebene Software stellt Anwendungsentwicklern ein Framework zur Evaluation verschiedener Retrieval- und Rankingfunktionen unter Nutzung unterschiedlicher Datenbasen zur Verfügung. Der modulare Aufbau des Frameworks ermöglicht die Unterstützung weiterer XML-basierter Beschreibungsformate sowie das Einbinden zusätzlicher Funktionen. Voraussetzungen für die Verwendung des Frameworks sind die Kodierung der Simulationsmodelle in einem XML-basierten Standard-Repräsentationsformat sowie die Verfügbarkeit von semantischen Modellinformationen, z.B. in Form von in Ontologien kodierten Meta-Informationen. Sombi wurde als Evaluationswerkzeug für Datenbankentwickler im Bereich der Modellspeicherung in der Systembiologie entwickelt. Eine Verwendung des Frameworks auf anderen Anwendungsgebieten ist jedoch vorstellbar.

Situation Model for Situation-Aware Assistance of Dementia Patients in Outdoor Mobility

Background: Dementia impairs spatial orientation and route planning, thus often affecting the patient’s ability to move outdoors and maintain social activities. Situation-aware deliberative assistive technology devices (ATD) can substitute impaired cognitive function in order to maintain one’s level of social activity. To build such a system, one needs domain knowledge about the patient’s situation and needs. We call this collection of knowledge situation model. Objective: To construct a situation model for the outdoor mobility of people with dementia (PwD). The model serves two purposes: 1) as a knowledge base from which to build an ATD describing the mobility of PwD; and 2) as a codebook for the annotation of the recorded behavior. Methods: We perform systematic knowledge elicitation to obtain the relevant knowledge. The OBO Edit tool is used for implementing and validating the situation model. The model is evaluated by using it as a codebook for annotating the behavior of PwD during a mobility study and interrater agreement is computed. In addition, clinical experts perform manual evaluation and curation of the model. Results: The situation model consists of 101 concepts with 11 relation types between them. The results from the annotation showed substantial overlapping between two annotators (Cohen’s kappa of 0.61). Conclusion: The situation model is a first attempt to systematically collect and organize information related to the outdoor mobility of PwD for the purposes of situation-aware assistance. The model is the base for building an ATD able to provide situation-aware assistance and to potentially improve the quality of life of PwD.