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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach.

David Henriques1, Miguel Rocha2, Julio Saez-Rodriguez2

  • 1Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal and European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.

Bioinformatics (Oxford, England)
|May 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational framework combining logic-based models and mixed-integer dynamic optimization (MIDO) to identify regulatory structures and parameters in biological pathways. This approach efficiently handles complex systems biology models and uncertainty in kinetic parameters.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Systems biology models aid hypothesis testing against new data or existing models.
  • Hypotheses often involve multiple regulatory mechanisms with uncertain parameters.
  • Current methods struggle with large-scale pathway models and parameter uncertainty.

Purpose of the Study:

  • To develop a computational framework for simultaneous identification of regulatory structures and kinetic parameters.
  • To address the challenge of uncertainty in biological pathway models.
  • To create a logic-based differential equation model for dynamic pathway analysis.

Main Methods:

  • Integration of a logic-based formalism for regulatory structure description.
  • Application of mixed-integer dynamic optimization (MIDO) for parameter identification.
  • Development of a framework to handle binary and real-valued parameters simultaneously.

Main Results:

  • A novel framework combining logic-based formalism and MIDO was successfully developed.
  • The method efficiently identifies both regulatory structure and kinetic parameters.
  • Demonstrated effectiveness on synthetic, bacterial homeostasis, and liver cancer signaling networks.

Conclusions:

  • The proposed method offers a tractable approach for analyzing complex biological pathways.
  • This framework enhances the ability to model and understand biological systems with uncertain parameters.
  • The approach is applicable to various biological systems, including disease-related signaling networks.