A Web-based Repository of Reproducible Simulation Experiments for Systems Biology.

Systems Biology requires increasingly complex simulation models. Effectively interpreting and building upon previous simulation results is both difficult and time consuming. Thus, simulation results often cannot be reproduced exactly; making it difficult for other modellers to validate results and take the next step in a simulation study. The Simulation Experiment Description Mark-up Language~(SED-ML), a subset of the Minimum Information About a Simulation Experiment~(MIASE) guidelines, promises to solve this problem by prescribing the form and content of the information required to reproduce simulation experiments. SED-ML is detailed enough to enable automatic rerunning of simulation experiments. Here, we present a web-based simulation-experiment repository that lets modellers develop SED-ML compliant simulation-experiment descriptions The system encourages modellers to annotate their experiments with text and images, experimental data and domain meta-information. These informal annotations aid organisation and classification of the simulations and provide rich search criteria. They complement SED-ML's formal precision to produce simulation-experiment descriptions that can be understood by both men and machines. The system combines both human-readable and formal machine-readable content, thus ensuring exact reproducibility of the simulation results of a modelling study. </p

A WEB-BASED REPOSITORY OF REPRODUCIBLE SIMULATION EXPERIMENTS FOR SYSTEMS BIOLOGY

Abstract: Systems Biology requires increasingly complex simulation models. Effectively interpreting and building upon previous simulation results is both difficult and time consuming. Thus, simulation results often cannot be reproduced exactly; making it difficult for other modellers to validate results and take the next step in a simulation study. The Simulation Experiment Description Mark-up Language (SED-ML), a subset of the Minimum Information About a Simulation Experiment (MIASE) guidelines, promises to solve this problem by prescribing the form and content of the information required to reproduce simulation experiments. SED-ML is detailed enough to enable automatic rerunning of simulation experiments. Here, we present a web-based simulation-experiment repository that lets modellers develop SED-ML compliant simulation-experiment descriptions The system encourages modellers to annotate their experiments with text and images, experimental data and domain meta-information. These informal annotations aid organisation and classification of the simulations and provide rich search criteria. They complement SED-ML&apos;s formal precision to produce simulation-experiment descriptions that can be understood by both men and machines. The system combines both human-readable and formal machine-readable content, thus ensuring exact reproducibility of the simulation results of a modelling study

A Web-based Repository of Reproducible Simulation Experiments for Systems Biology.

Systems Biology requires increasingly complex simulation models. Effectively interpreting and building upon previous simulation results is both difficult and time consuming. Thus, simulation results often cannot be reproduced exactly; making it difficult for other modellers to validate results and take the next step in a simulation study. The Simulation Experiment Description Mark-up Language~(SED-ML), a subset of the Minimum Information About a Simulation Experiment~(MIASE) guidelines, promises to solve this problem by prescribing the form and content of the information required to reproduce simulation experiments. SED-ML is detailed enough to enable automatic rerunning of simulation experiments. Here, we present a web-based simulation-experiment repository that lets modellers develop SED-ML compliant simulation-experiment descriptions The system encourages modellers to annotate their experiments with text and images, experimental data and domain meta-information. These informal annotations aid organisation and classification of the simulations and provide rich search criteria. They complement SED-ML's formal precision to produce simulation-experiment descriptions that can be understood by both men and machines. The system combines both human-readable and formal machine-readable content, thus ensuring exact reproducibility of the simulation results of a modelling study. </p