Simulation Experiment Management for Systems Biology (SEMS)

Simulation Experiment Management for Systems Biology (SEMS)

Systems biology is a scientific approach characterised by an iterative cycle of data-driven modelling and model-driven experimentation. The present project is to provide support for this iterative cycle through techniques and tools that improve and ease the working with simulation setups, using standard formats and ensuring result reproducibility.

Tools and concepts for Simulation Experiment Management in Systems Biology: Improving the production of simulation experiments through standard formats and management support.

Research goal.

In order to compare scientific results in the life sciences but also to integrate the outcomes from partners in large-scale research collaborations, standardization is necessary. The standardization and exchange of protocols by which data are generated are already widely promoted. However, for the same reasons that apply to wet-lab data generation, results from projects that involve mathematical modelling and computer simulations must be documented to improve exchange, reuse and reproducibility. For example, in order to reproduce a simulation plot in a publication it is often not sufficient to know the equations of the model. The choice of numerical algorithms and their internal settings can influence the simulation results. In parameter estimation the results are often driven by random number generation, requiring statistical information on the outcomes of parameter value optimization. Metainformation on simulation experiments improves the re-use of simulations, supports the development of new models from existing ones and helps reducing errors, thereby improving the reproducibility of scientific results in the field of systems biology.

In this project Dr. Dagmar Waltemath and her team investigate techniques for the encoding of simulation experiments. The project covers the standardized encoding of experiments in an XML format, supporting a range of types of simulation experiments, and including the versioning of both, simulation experiment descriptions and associated models. The exchange of simulation experiment descriptions, together with existing models, will help reduce the development time of models in systems biology, will help the reproducibility of publications and support training in systems biology.

Specific objectives.

  • the further development of a standard for the description of simulation experiments (SED-ML). This will improve the reproducibility of model-derived results in publications.

  • a study of model histories, tracing the development of a selection of models, describing the changes that have occurred. The analysis will help deriving criteria for the evolution of models, to enable reference to a particular model instance from a simulation description.

  • a study of simulation experiment histories, tracing the development, describing and classifying the changes that have occurred. This will help to describe differences in simulation experiments.

  • the provision of a simulation experiment management system for maintenance, public availability, retrieval, exchange, and versioning of simulation experiment descriptions in a standard format.

For further information and news please visit our project homepage.

Related publications

Evolution of computational models in BioModels Database and the Physiome Model Repository

Scharm M, Gebhardt T, Touré V, Bagnacani A, Salehzadeh-Yazdi A, Wolkenhauer O, Waltemath D

BMC Systems Biology 12:53 (2018)

SED-ML Web Tools: Generate, modify and export standard-compliant simulation studies

Bergmann FT, Nickerson D, Waltemath D, Scharm M

Oxford Bioinformatics 33:8, pp. 1253–1254 (2017)

COMODI: An ontology to characterise differences in versions of computational models in biology

Scharm M, Waltemath D, Mendes P, and Wolkenhauer O

Open Access article in the Journal of Biomedical Semantics 7:46 (2016)

Toward community standards and software for whole-cell modeling

Waltemath D, Karr JT, Bergmann FT, Chelliah V ... Scharm M et al.

Open Access article in IEEE Transactions on Biomedical Engineering 63:10, pp. 2007-14 (2016)

The Cardiac Electrophysiology Web Lab

Cooper J, Scharm M, and Mirams GR

Open Access article in the Biophysical Journal 110:2, pp 292-300 (2016)

An algorithm to detect and communicate the differences in computational models describing biological systems

Scharm M, Wolkenhauer O, and Waltemath D

Open Access article in BIOINFORMATICS 32:4, pp. 563-570 (2015)

COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project

Bergmann FT, Adams R, ... Scharm M, ... Waltemath D et al.

Open Access article in BMC Bioinformatics 15:369 (2014)

Improving the reuse of computational models through version control

Waltemath D, Henkel R, Hälke R, Scharm M, and Wolkenhauer O

Article in BIOINFORMATICS 29:6, pp. 742-7 (2014)

Model Management in Systems Biology: Challenges – Approaches – Solutions

Scharm M

A talk in the FAIRDOM webinar series 2016

Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit

Scharm M and Waltemath D

Article in Proceedings of the Workshop on Data management in Life Sciences, BTW, Hamburg, germany (2015)

Venue: Hamburg, Germany

The CombineArchiveWeb Application - A Web-based Tool to Handle Files Associated with Modelling Results.

Scharm M, Wendland F, Peters M, Wolfien M, Theile T, and Waltemath D

Open Access demo paper in Proceedings of the 2014 Workshop on Semantic Web Applications and Tools for life sciences (2014)

Identifying, interpreting, and communicating changes in XML-encoded models of biological systems

Scharm M, Wolkenhauer O, and Waltemath D

Demo paper in Proceedings of the 10th International Conference on Data Integration in the Life Sciences, Lisbon, Portugal (2014)

Venue: Lisbon, Portugal

Improving Reproducibility and Reuse of Modelling Results in the Life Sciences

Scharm M

Research results are complex and include a variety of heterogeneous data. This entails major computational challenges to (i) to manage simulation studies, (ii) to ensure model exchangeability, stability and validity, and (iii) to foster communication between partners. I describe techniques to improve the reproducibility and reuse of modelling results. First, I introduce a method to characterise differences in computational models. Second, I present approaches to obtain shareable and reproducible research results. Altogether, my methods and tools foster exchange and reuse of modelling results.

Defense: 30 August 2018