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Biosystem models, generated from a complex rule/reaction/influence network and from two functionality prototypes.

M Varga1, A Prokop2, B Csukas1

  • 1Research Group on Process Network Engineering, Kaposvar University, 40 Guba S, 7400, Kaposvar, Hungary.

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|January 8, 2017
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Summary
This summary is machine-generated.

This study enhances Direct Computer Mapping (DCM) for biosystem modeling, introducing a unified network representation and automated GraphML generation for multiscale simulations. This improves the integration of quantitative and qualitative biological models.

Keywords:
Editable graphical modelInfluence networkModel generationMultiscale modelQualitative modelQuantitative modelReaction networkRule networkState prototypeTransition prototypep53 signalling at cancer

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Direct Computer Mapping (DCM) is a methodology for biosystem modeling and simulation.
  • Existing DCM methods require further development for unified representation of complex biological networks.

Purpose of the Study:

  • To develop an enhanced Direct Computer Mapping (DCM) methodology.
  • To introduce a unified, transition-based representation for complex biological networks.
  • To enable automatic generation of editable and extensible GraphML models for biosystems.

Main Methods:

  • Developed a unified, transition-based representation for rule, reaction, and influence networks.
  • Created general state- and transition-prototype functional models.
  • Elaborated an automatic generation method for GraphML descriptions from network and functional prototypes.
  • Implemented developments in an improved DCM kernel.

Main Results:

  • Unified functional modeling of state and transition nodes.
  • Automatic generation of graphically editable and extensible GraphML biosystem models.
  • Enabled unified generation and execution of quantitative, qualitative, and multiscale biosystem models.
  • Successfully applied the methodology to integrate a detailed p53/miR34a signaling system with a pathological model.

Conclusions:

  • The enhanced DCM methodology provides a unified approach for modeling diverse biosystem complexities.
  • The developed tools facilitate the creation and simulation of integrated, multiscale biological models.
  • This work advances computational approaches for systems biology research and disease modeling.