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Computational Modeling of Mitochondrial Function from a Systems Biology Perspective.

Sonia Cortassa1, Steven J Sollott2, Miguel A Aon2

  • 1National Institute on Aging, National Institutes of Health, Baltimore, MD, USA. sonia.cortassa@nih.gov.

Methods in Molecular Biology (Clifton, N.J.)
|June 1, 2018
PubMed
Summary

Computational models integrating physicochemical principles are essential for analyzing complex biological big data. This study surveys methods for building reliable mitochondrial energetics models using analytical tools for improved biological interpretation and prediction.

Keywords:
Elementary flux modesLinear optimization of metabolic modelsMetabolic control and dynamic analysesMitochondrial energeticsOrdinary differential equations (ODEs)

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioenergetics

Background:

  • The rise of biological "big data" (genomics, proteomics, metabolomics) necessitates advanced computational methods.
  • Understanding complex cellular networks requires models grounded in physicochemical principles.
  • Thermo-kinetic models offer superior insights into mitochondrial energy metabolism regulation.

Purpose of the Study:

  • To survey methods for constructing computational models of mitochondrial energetics.
  • To explore integrating these models within broader cellular process networks.
  • To enhance the interpretation and reliability of high-throughput biological data.

Main Methods:

  • Utilizing physicochemical mechanistic principles for model building.
  • Employing analytical tools like elementary flux modes and control analysis.
  • Focusing on steady-state behavior analysis and model validation.

Main Results:

  • Provides a survey of methods for computational model development in mitochondrial energetics.
  • Demonstrates the utility of analytical tools for refining model behavior and reliability.
  • Highlights approaches for integrating mitochondrial models into cellular networks.

Conclusions:

  • Computational modeling based on mechanistic principles is crucial for big data in biology.
  • Analytical tools improve the design, building, and interpretation of metabolic models.
  • This work facilitates more reliable prediction and understanding of cellular energy metabolism.