Fengkai Zhang, M.D., M.Math.

A range of methodologies is utilized in systems biology to simulate biological pathways, uncovering the intricate ways molecules function within living organisms and enabling hypotheses about potential interactions and reaction rate ranges without relying on traditional experimental measurements. Among these, rule-based modeling abstracts similar reactions into rules that summarize the characteristics and binding states of individual molecular components within large molecular complexes. This approach offers a distinct advantage by simplifying the representation of molecular reactions, organizing entities within hierarchical structures that align with biological formats at both the molecular and sub-molecular levels, such as domains and binding sites. Furthermore, the rule-based modeling approach can address the combinatorial complexity inherent in biological systems.

Simmune is a software suite that uses rule-based modeling to simulate biological pathways. It constructs models of biological reactions through its unique icon-based modeling language from single reactions and provides a flexible, high-level network view for model inspection. Simmune can simulate models in well-stirred environments, efficiently explore large parameter spaces, and help users identify the most relevant parameters, offering insights into the relationships between different parameters. Additionally, Simmune supports the simulation of spatially resolved models in discrete grid morphologies and 3D environments, with the ability to analyze and visualize results at fine-grained subcellular levels.

Simmune supports the Multistate, Multicomponent, and Multicompartment Species Package for SBML Level 3 (SBML-Multi) to exchange rule-based models. This standard is part of an initiative by the COmputational Modeling in BIology NEtwork (COMBINE) community standards and formats for computational models. Our group leads the effort for the SBML-Multi standard.

Simmune is built with technologies including C/C++, Qt, VTK, CMake, Python, SQL, MongoDB, Boost, version control, Sundials, and parallel computing, providing a powerful, flexible modeling tool that remains user-friendly for biological researchers with limited computing expertise. Our group collaborates with researchers in immunology, proteomics, computational modeling, and systems biology.