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Converting differential-equation models of biological systems to membrane computing.

Ravie Chandren Muniyandi1, Abdullah Mohd Zin, J W Sanders

  • 1Research Center for Software Technology and Management, Faculty of Technology and Information Science, National University of Malaysia, 43600 Bangi, Selangor, Malaysia.

Bio Systems
|October 15, 2013
PubMed
Summary

This study introduces a novel method to model biological systems, converting ordinary differential equations into discrete membrane computations. This approach enhances accuracy for small systems and incorporates spatial information for more realistic biological modeling.

Keywords:
Ligand–receptor network of TGF-βMembrane computingModelling biological systemsOrdinary differential equationsRewrite rules

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Theoretical Computer Science

Background:

  • Ordinary differential equations (ODEs) are standard for modeling biological systems.
  • ODE models are continuous and deterministic, which can be limiting for certain biological phenomena.
  • Spatial information and non-determinism are crucial for realistic biological system modeling.

Purpose of the Study:

  • To develop a method for converting ODE-based biological system models into non-deterministic, discrete membrane computations.
  • To leverage spatial information for more accurate and realistic biological modeling.
  • To validate the proposed conversion method using a biological case study.

Main Methods:

  • Conversion of deterministic, continuous ODE representations to non-deterministic, discrete membrane computations.
  • Utilizing rewrite rules with region-specific rates to govern membrane computation dynamics.
  • Applying the method to the ligand-receptor network of protein TGF-β for validation.

Main Results:

  • The membrane computing model accurately represents small biological systems.
  • Local rates of change are effectively expressed, incorporating spatial information.
  • The conversion method preserves system behaviors and properties effectively.

Conclusions:

  • The proposed method offers a more realistic modeling approach for biological systems by incorporating non-determinism and spatial information.
  • Membrane computing provides a valuable framework for analyzing biological systems, particularly when local dynamics are important.
  • The conversion technique shows promise for advancing computational biology and systems biology research.