This document provides an overview of the Computational Modeling in Biology Network (COMBINE) which coordinates the standardization of data and models in computational biology. It describes COMBINE's role in developing standards for encoding models (SBML), visualizing models (SBGN), and simulating models (SED-ML). The document also discusses COMBINE's guidance on publishing models according to FAIR principles, developing software tools and libraries to support the standards, and establishing best practices through documentation and training resources.
Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This document provides an overview of standards and best practices for making computational models reusable through the use of model repositories and standard formats. It discusses the COMBINE initiative for standardizing the encoding of models and simulations. The document encourages authors to make their models and data FAIR (Findable, Accessible, Interoperable, Reusable) by using community standards for publishing, exchanging, and archiving models. Examples of open model repositories and standards-compliant tools and libraries are provided to demonstrate how authors can improve sharing and reuse of their models.
Presentation on how to enable model reuse in systems biology. Presented as part of the series "Führende Köpfe in der IT - Wissenschaftlerinnen im Dialog" (ZB Med, Bonn, Germany)
This document provides an overview of the Computational Modeling in Biology Network (COMBINE) which coordinates the standardization of data and models in computational biology. It describes COMBINE's role in developing standards for encoding models (SBML), visualizing models (SBGN), and simulating models (SED-ML). The document also discusses COMBINE's guidance on publishing models according to FAIR principles, developing software tools and libraries to support the standards, and establishing best practices through documentation and training resources.
Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This document provides an overview of standards and best practices for making computational models reusable through the use of model repositories and standard formats. It discusses the COMBINE initiative for standardizing the encoding of models and simulations. The document encourages authors to make their models and data FAIR (Findable, Accessible, Interoperable, Reusable) by using community standards for publishing, exchanging, and archiving models. Examples of open model repositories and standards-compliant tools and libraries are provided to demonstrate how authors can improve sharing and reuse of their models.
Presentation on how to enable model reuse in systems biology. Presented as part of the series "Führende Köpfe in der IT - Wissenschaftlerinnen im Dialog" (ZB Med, Bonn, Germany)
This document summarizes work using Neo4j graph databases for computational systems biology models. It discusses:
1) Projects using Neo4j to integrate storage of models and simulation studies, enable ranked retrieval, and identify frequent patterns in models.
2) Tools developed including MASYMOS for linking models, simulations, annotations via graph structures, and STON for converting SBGN maps to Neo4j.
3) Applications including model repositories, analysis tools, and identifying common reaction motifs in models.
This document summarizes Dagmar Waltemath's presentation on model management for systems biology projects. It discusses the need for effective data management strategies due to the large, complex, and heterogeneous nature of systems biology data. It recommends using a data management plan, dedicated model management systems like FAIRDOMHub, standards for sharing data, publishing models in repositories, ensuring model quality, and tracking provenance. The goal is to make studies reproducible, valuable, and sustainable.
This document discusses challenges to reproducibility in systems biology and potential solutions. It notes a lack of data standards, quality, availability, and transparency make it difficult for researchers to reproduce results. Tools and initiatives discussed that aim to improve reproducibility include the COMBINE archive to bundle necessary files, graph databases to integrate model-related data, and version control systems to track model evolution over time. The overall goal is to better support scientists in sharing reproducible model-based studies.
This document discusses SED-ML (Simulation Experiment Description Markup Language), a standard for describing computational simulations. SED-ML files contain information like the models, data, simulation settings and algorithms used in an experiment. Using SED-ML allows experiments to be reproduced and shared. The document encourages adopting SED-ML to make research more reproducible and help curation of models in repositories. It also provides an overview of tools that support SED-ML and ways to get involved in its development.
This document discusses data and model management in systems biology. It covers topics such as data ownership, metadata, ontologies, standards for encoding models and analyses, and tools for working with systems biology models and data. Standards like SBML, SBGN, SED-ML and COMBINE Archive allow for structured representation, visualization, simulation, and sharing of models and data. Resources like SEEK enable curation, simulation and publication of models in a findable, accessible, interoperable and reusable (FAIR) manner.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
Introduction to the hands on session on "Standards and tools for model management" at the ICSB 2015.
Focus on COMBINE standards, tools for search, version control and archiving. Used management platform is SEEK.
These are the slides from COMBINE 2015. In this talk, I presented the different approaches we take to determine the similarity between simulation models encoded in SBML or CeLLML -- namely: Information Retrieval based ranked model retrieval; annotation-based feature extraction for sets of models; and structure-based similarity search and clustering of model sets.
This document discusses improving reproducibility of simulation studies in computational biology through better management of simulation models and data. The SEMS project aims to develop standards and tools to link related data such as publications, models, simulations, results and more. This will be achieved by using graph databases and COMBINE standards to integrate data from various repositories. Tools will be created to search, compare, cluster and visualize models and their evolution over time to enable more reproducible and reusable simulation studies.
The document summarizes the work of the SBGN-ED+ project, which aims to further develop and integrate the Systems Biology Graphical Notation (SBGN) for modeling biological networks. Some key goals of the project include contributing to the SBGN specification and library, implementing SBGN support for model version control and merging in software tools like SBGN-ED, and using SBGN maps to display differences between model versions. The project also seeks to incorporate SBGN maps into model search, comparison and integration of model-related data. This would help address the need for standardized visual representations of biological networks to reduce ambiguity and enable sharing of computational models.
Some slides put together on analogies between biosamples and model samples. Prepared for the Biosamples workshop at The University of Manchester, 17th June 2015.
Talk in the research seminar of the Systems Biology group at the University of Rostock. The goal was to introduce the two new projects running in SEMS from summer 2015: The de.NBI-SYSBIO German Network for Bioinformatics infrastructure (focus: systems biology data management) and SBGN-ED (support and further development of SBGN-ED and libSBGN).
MaSyMoS is a tool for finding hidden treasures in model repositories by enabling semantic searches across models, annotations, and associated data. It addresses a common problem researchers face in difficulty managing and accessing their data. MaSyMoS allows users to query model repositories to find models associated with certain publications, genes, or behaviors. It also provides files needed to run simulations from retrieved models. The tool aims to help researchers better discover, organize, and leverage existing computational models.
This document discusses challenges in modeling reproducibility, dissemination, and management. It notes that researchers struggle with data management. Standards are needed for reproducible modeling results, including models, annotations, and protocols. Models should be disseminated through public repositories for higher visibility, long-term availability, and quality checks. Management of models and related data can be improved through integration into graph databases linked to ontologies, as well as version control systems. The SEMS projects aim to address these issues to foster dissemination, ensure reproducibility, and improve management of computational models.
The document presents work from the Department of Systems Biology and Bioinformatics at the University of Rostock on improving reproducibility in systems biology simulations. It discusses developing standards for representing simulations (SED-ML) and modeling provenance to better reproduce published results and enable model reuse. The goals are to specify simulation experiments, develop simulation management methods focusing on model provenance, establish links between model data, and promote reproducible science.
This document discusses three approaches to integrating model-related data in computational biology:
1) The COMBINE archive which bundles all model data into a single zip file for easy distribution.
2) Using a graph database (MORRE) to manage existing model data by representing it as a network of interrelated nodes that can be queried using information retrieval techniques.
3) Integrating model data into the semantic web and linked open data through BIO2RDF to enable automated reasoning and linking to other biological knowledge bases.
Ron Henkel's presentation of our Ranked Retrieval approach; 2012 PALs meeting of the Sysmo-SEEK project in Heidelberg, Germany. 28th-30th of November 2012.
A presentation on annotations for computational biological models. Second part is on SED-ML, a format for the storage of simulation experiment descriptions.
This document summarizes work using Neo4j graph databases for computational systems biology models. It discusses:
1) Projects using Neo4j to integrate storage of models and simulation studies, enable ranked retrieval, and identify frequent patterns in models.
2) Tools developed including MASYMOS for linking models, simulations, annotations via graph structures, and STON for converting SBGN maps to Neo4j.
3) Applications including model repositories, analysis tools, and identifying common reaction motifs in models.
This document summarizes Dagmar Waltemath's presentation on model management for systems biology projects. It discusses the need for effective data management strategies due to the large, complex, and heterogeneous nature of systems biology data. It recommends using a data management plan, dedicated model management systems like FAIRDOMHub, standards for sharing data, publishing models in repositories, ensuring model quality, and tracking provenance. The goal is to make studies reproducible, valuable, and sustainable.
This document discusses challenges to reproducibility in systems biology and potential solutions. It notes a lack of data standards, quality, availability, and transparency make it difficult for researchers to reproduce results. Tools and initiatives discussed that aim to improve reproducibility include the COMBINE archive to bundle necessary files, graph databases to integrate model-related data, and version control systems to track model evolution over time. The overall goal is to better support scientists in sharing reproducible model-based studies.
This document discusses SED-ML (Simulation Experiment Description Markup Language), a standard for describing computational simulations. SED-ML files contain information like the models, data, simulation settings and algorithms used in an experiment. Using SED-ML allows experiments to be reproduced and shared. The document encourages adopting SED-ML to make research more reproducible and help curation of models in repositories. It also provides an overview of tools that support SED-ML and ways to get involved in its development.
This document discusses data and model management in systems biology. It covers topics such as data ownership, metadata, ontologies, standards for encoding models and analyses, and tools for working with systems biology models and data. Standards like SBML, SBGN, SED-ML and COMBINE Archive allow for structured representation, visualization, simulation, and sharing of models and data. Resources like SEEK enable curation, simulation and publication of models in a findable, accessible, interoperable and reusable (FAIR) manner.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
Introduction to the hands on session on "Standards and tools for model management" at the ICSB 2015.
Focus on COMBINE standards, tools for search, version control and archiving. Used management platform is SEEK.
These are the slides from COMBINE 2015. In this talk, I presented the different approaches we take to determine the similarity between simulation models encoded in SBML or CeLLML -- namely: Information Retrieval based ranked model retrieval; annotation-based feature extraction for sets of models; and structure-based similarity search and clustering of model sets.
This document discusses improving reproducibility of simulation studies in computational biology through better management of simulation models and data. The SEMS project aims to develop standards and tools to link related data such as publications, models, simulations, results and more. This will be achieved by using graph databases and COMBINE standards to integrate data from various repositories. Tools will be created to search, compare, cluster and visualize models and their evolution over time to enable more reproducible and reusable simulation studies.
The document summarizes the work of the SBGN-ED+ project, which aims to further develop and integrate the Systems Biology Graphical Notation (SBGN) for modeling biological networks. Some key goals of the project include contributing to the SBGN specification and library, implementing SBGN support for model version control and merging in software tools like SBGN-ED, and using SBGN maps to display differences between model versions. The project also seeks to incorporate SBGN maps into model search, comparison and integration of model-related data. This would help address the need for standardized visual representations of biological networks to reduce ambiguity and enable sharing of computational models.
Some slides put together on analogies between biosamples and model samples. Prepared for the Biosamples workshop at The University of Manchester, 17th June 2015.
Talk in the research seminar of the Systems Biology group at the University of Rostock. The goal was to introduce the two new projects running in SEMS from summer 2015: The de.NBI-SYSBIO German Network for Bioinformatics infrastructure (focus: systems biology data management) and SBGN-ED (support and further development of SBGN-ED and libSBGN).
MaSyMoS is a tool for finding hidden treasures in model repositories by enabling semantic searches across models, annotations, and associated data. It addresses a common problem researchers face in difficulty managing and accessing their data. MaSyMoS allows users to query model repositories to find models associated with certain publications, genes, or behaviors. It also provides files needed to run simulations from retrieved models. The tool aims to help researchers better discover, organize, and leverage existing computational models.
This document discusses challenges in modeling reproducibility, dissemination, and management. It notes that researchers struggle with data management. Standards are needed for reproducible modeling results, including models, annotations, and protocols. Models should be disseminated through public repositories for higher visibility, long-term availability, and quality checks. Management of models and related data can be improved through integration into graph databases linked to ontologies, as well as version control systems. The SEMS projects aim to address these issues to foster dissemination, ensure reproducibility, and improve management of computational models.
The document presents work from the Department of Systems Biology and Bioinformatics at the University of Rostock on improving reproducibility in systems biology simulations. It discusses developing standards for representing simulations (SED-ML) and modeling provenance to better reproduce published results and enable model reuse. The goals are to specify simulation experiments, develop simulation management methods focusing on model provenance, establish links between model data, and promote reproducible science.
This document discusses three approaches to integrating model-related data in computational biology:
1) The COMBINE archive which bundles all model data into a single zip file for easy distribution.
2) Using a graph database (MORRE) to manage existing model data by representing it as a network of interrelated nodes that can be queried using information retrieval techniques.
3) Integrating model data into the semantic web and linked open data through BIO2RDF to enable automated reasoning and linking to other biological knowledge bases.
Ron Henkel's presentation of our Ranked Retrieval approach; 2012 PALs meeting of the Sysmo-SEEK project in Heidelberg, Germany. 28th-30th of November 2012.
A presentation on annotations for computational biological models. Second part is on SED-ML, a format for the storage of simulation experiment descriptions.
22. Systembiologisches Datenmanagement:
Retrieval und Bewertung (MORRE)
Which are the most frequently used
GO annotations in my model set?
Which models contain reactions with 'ATP'
as reactant and 'ADP' as product?
Find good candidates for features
describing my model set.
How similar are these two models
with respect to structure?
Which models are
annotated with ‘Ubiquitin'’?
Publication
Annotation
Person
Simulation
Model
Show me models of the Cell
Cycle by Tyson that simulate
cdk1 concentration!
23. Systembiologisches Datenmanagement:
Retrieval und Bewertung (MORRE)
Lucene: species:cdk1, compartment:cell, …
model
species
URI
qualifier
Model Index
species:
cdk1
Semantic Index author:
NOT
John
Doe
compart-
ment:
cell
species:
cdk1
relevant models
author:
NOT
John
Doe
species:
cdk1
species
URI:
P04551
Models by non-bogus authors describing
the Cell Cycle?
speciesURI:
P04551
retrieve
models
...BM-ID species speciesURI
BIOMD
00000005
cdc2k,
cyclin
UniProt:P04551
IPR:006670
...
URI qualifier content
UniProt:P04551
IPR:006670
Cdk1, cdk1,
swo2. pi002,
SPBC11B10.09
Tyson1991
ID: BIOMD000000005
Authors: Tyson JJ.
Date: 13 Sep 2005 12:31:08
Publication: pubmed:1831270
Species: cdc2k, cyclin …
Reaction: cyclin_cdc2k_dissociation, …
compart-
ment:
cell
Show me models of the Cell Cycle,
not by John Doe that contain cdk1
and a compartment cell!
(Abb. angelehnt an Henkel et al (2010))
36. MIRACUM ‐ Ausbildung
Whole Cell summer school, Rostock
Whole Cell 2, Salt Lake City, US
Data citation & integration, Rostock
Drawing SBGN Maps, Newcastle, UK
Kinetics on the move,
Heidelberg