Minimum Information About a Simulation Experiment (MIASE)

Reproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment (MIASE, Glossary in Box 1) describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes

Ontologies for use in Systems Biology: SBO, KiSAO and TEDDY

The use of computational modelling in the description and analysis of biological systems is at the heart of Systems Biology. Besides the information stored in a core model, there is increasingly a need to provide additional semantic information: to identify model components, to assist in biological interpretation of models, to define simulation conditions and to describe simulation results. This information deficit can be addressed through the use of ontologies. We describe here three ontologies created specifically to address the needs of the Systems Biology community in each sub-division, and illustrate their practical use with the 'Repressilator' model (Elowitz and Leibler, 2000)

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

Altproduktrückführung als logistische Dienstleistung - Entwicklung eines kundenorientierten Rückführkonzeptes

Das Kreislaufwirtschafts- und Abfallgesetz sowie die bevorstehende Elektro-Schrott-Verordnung sind Ergebnis der zunehmenden Forderung einer Kreislaufführung auf hohem Niveau. Insbesondere bei Konsumgütern wird ein effizientes, ökonomisch tragfähiges Recycling für Hersteller, Handel und Dienstleister erforderlich. Der Rückführlogistik kommt eine besondere Bedeutung zu, da sie einerseits den Großteil der gesamten Entsorgungskosten determiniert, andererseits die Erfassungsquoten über den Logistikservice damit die Effektivität - beeinflussen kann. Ziel der Arbeit ist die Entwicklung eine Konzeptes zur Altproduktrückführung, dass an den Kundenanforderungen ausgerichtet ist. Zu berücksichtigen sind damit Dienstleister, die in bipolaren Kundenbeziehungen zu den Quellen, Entsorgungskunden, und zu den Senken, Demontage-fabriken, mit ihren divergierenden Leistungsanforderungen stehen. Darüber hinaus sind beste-hende Systeme der Altproduktrückführung so zu integrieren, dass ein zu entwickelnder Entsorgungsverbund die zu ermittelnden Anforderungen kosten- und leistungsoptimal erfüllt. Nach Ausarbeitung der Dienstleistungsspezifika werden die Quellen und Senken der Altproduktrückführung analysiert. Hierauf aufbauend werden die Anforderungen der Entsorgungskunden, Quellen und Senken, untersucht. Die Anforderungen der Kundengruppe Privathaushalte werden durch eine bundesweite Befragung und Auswertung mittels Conjoint-Analyse untersucht. Hier können drei Kundengruppen, ökologieorientierte, preisbewusste sowie ökologie- und serviceorientierte Kunden nachgewiesen und ihre Anforderungen analysiert werden. Für die Senken, den Demontagefabriken, wurden hohe Informationsbereitschaft, Sorten- und Mengenflexibilität und Massenleistungsfähigkeit als Hauptanforderungen ermittelt. Hierauf aufbauend wird das Rückführkonzept entwickelt: Durch die zentrale Erfassung der Ent- und Versorgungsaufträge sowie das zentrale Informations-Management durch einen Call-Center-Dienstleister kann der Servicegrad gesteigert und eine Leerkostenreduktion durch Senkung von Auslastungsschwankungen nachgewiesen werden. Die physischen Entsorgungsleistungen werden durch einen Entsorgungsverbund, bestehen aus einem strategischen Netzwerk und unterlagerten regionalen Netzwerken gesteuert und erbracht: Das Strategische Netzwerk erbringt Aufgaben der strategischen Leistungsausrichtung, der Schnittstellen-Koordination, der Qualitätssicherung, der Integration des Informations-Managements sowie des Controllings. Die regionalen Netzwerke übernehmen Aufgaben der operativen Steuerung und Leistungserstellung: Hier werden die Leistungen zwischen fest dimensionierbaren Primär-Dienstleistern und bedarfsweise zu integrierenden Sekundär-Diensleistern aufgeteilt. Neben der Senkung der Leerkosten ist damit eine den Anforderungen entsprechende, differenzierte Leistungserstellung von preis-, ökologie- oder serviceorientierten Rückführleistungen innerhalb des Entsorgungsverbundes möglich. Die Dissertation bietet damit die Basis für eine vertiefende Analyse der Kundengruppen und ihrer Anforderungen sowie vor allem für die Weiterentwicklung des bundesweiten Entsorgungsnetzwerkes unter Einbindung existierender Entsorgungs- und Logistikdienstleister

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

Kinetic Simulation Algorithm Ontology

To enable the accurate and repeatable execution of a computational simulation task, it is important to identify both the algorithm used and the initial setup. These minimum information requirements are described by the MIASE guidelines. Since the details of some algorithms are not always publicly available, and many are implemented only in a limited number of simulation tools, it is crucial to identify alternative algorithms with similar characteristics that may be used to provide comparable results in an equivalent simulation experiment. The Kinetic Simulation Algorithm Ontology (KiSAO) was developed to address this issue by describing existing algorithms and their inter-relationships through their characteristics and parameters. The use of KiSAO in conjunction with simulation descriptions, such as SED-ML, will allow simulation software to automatically choose the best algorithm available to perform a simulation. The availability of algorithm parameters, together with their type may permit the automatic generation of user-interfaces to configure simulators. To enable making queries to KiSAO programmaticaly, from simulation experiment description editors and simulation tools, a java library libKiSAO was implemented

Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined

Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3)

The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment

The JWS online simulation database

Summary: JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments. Availability and Implementation: The Simulation Database is available on line at https://jjj.bio.vu. nl/models/experiments/