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Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

Endre Somogyi1, James P Sluka2, James A Glazier2

  • 1Department of Computer Science, Biocomplexity Institute Indiana University Bloomington, IN 47405.

Model Driven Engineering Languages and Systems : ... International Conference, Models ... : Proceedings. MODELS (Conference)
|January 17, 2018
PubMed
Summary
This summary is machine-generated.

We developed a new hybrid programming language to unify complex biological simulations. This approach captures and reuses domain-specific knowledge for multi-scale modeling in cellular and tissue biology.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Multi-scale agent-based simulations are crucial for understanding cellular and tissue biology.
  • These simulations integrate diverse components and processes, leading to dynamic interactions.
  • Existing domain-specific languages struggle to represent these complex, interconnected elements and their interactions cohesively.

Purpose of the Study:

  • To introduce a novel hybrid programming language paradigm.
  • To enable a unified representation of multi-scale objects and their dynamic interactions.
  • To facilitate the capture, search, formalization, extraction, and reuse of domain-specific knowledge in biological simulations.

Main Methods:

  • Development of a hybrid programming language.
  • Integration of multi-scale objects (molecular, cellular, tissue) and processes (biochemical, biomechanical, spatial, behavioral).
  • Focus on representing dynamic interactions and domain knowledge in a human-readable and reusable format.

Main Results:

  • A programming paradigm that naturally expresses complex multi-scale objects and dynamic interactions.
  • Unified representation of components and their interrelations within simulations.
  • Enhanced capabilities for domain knowledge management within computational biology.

Conclusions:

  • The proposed hybrid programming language offers a unified approach for complex biological simulations.
  • It addresses the limitations of current languages in representing interconnected biological systems.
  • This paradigm enhances the formalization and reuse of domain knowledge, advancing multi-scale modeling in biology.