Reproducible and flexible simulation experiments with ML-Rules and SESSL.

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Developing complex systems biology models requires diverse simulation experiments.
• Ensuring replicability of simulation results necessitates robust documentation of experimental procedures.

Purpose of the Study:

• To introduce a new SESSL binding for the ML-Rules modeling language.
• To enhance the execution and documentation of simulation experiments for ML-Rules models.

Main Methods:

• Utilizing embedded domain-specific languages (DSLs) like SESSL for simulation experiment specification.
• Developing a SESSL binding to integrate ML-Rules models with SESSL's features.
• Leveraging SESSL for integrating diverse simulation methods and third-party software components.

Main Results:

• The SESSL binding enables executable and readable simulation experiment specifications for ML-Rules.
• It facilitates the integration of various simulation experimentation methods.
• Improved possibilities for documenting simulation experiments and ensuring result replicability.

Conclusions:

• The SESSL binding for ML-Rules offers a powerful approach to managing and documenting complex systems biology simulations.
• This integration enhances the usability and reproducibility of computational modeling in systems biology.