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Related Experiment Video

Updated: Dec 4, 2025

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

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Using optimal control to understand complex metabolic pathways.

Nikolaos Tsiantis1,2, Julio R Banga3

  • 1Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208, Vigo, Spain.

BMC Bioinformatics
|October 22, 2020
PubMed
Summary
This summary is machine-generated.

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This study applies multi-objective optimal control to complex metabolic pathways, enabling predictions of enzyme and metabolite dynamics. The developed computational framework handles intricate biological systems and cost-benefit trade-offs effectively.

Area of Science:

  • Systems Biology
  • Biochemistry
  • Computational Biology

Background:

  • Optimality principles explain biological organization from molecular to population levels.
  • Previous models used simplified, single-objective approaches for metabolic pathways.
  • Evolutionary justification supports optimality in biological systems.

Purpose of the Study:

  • Extend optimality principles to complex, realistic biochemical pathways.
  • Address challenges in optimal control for intricate biological networks.
  • Develop a scalable computational framework for analyzing metabolic dynamics.

Main Methods:

  • Applied multi-objective optimal control to biochemical networks.
  • Developed a scalable and efficient computational framework.
Keywords:
Dynamic modelingMulti-criteria optimizationOptimal controlPareto optimality

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  • Utilized case studies of central carbon metabolism in S. cerevisiae and B. subtilis.

    • Main Results:

    • Successfully predicted temporal profiles of enzyme activation and metabolite concentrations.
    • Demonstrated the framework's ability to handle complex metabolic pathways.
    • Illustrated metabolic dynamics during nutrient shift experiments.

    Conclusions:

    • Multi-objective optimal control accurately predicts dynamics in complex metabolic pathways.
    • The framework accommodates general cost-benefit trade-offs.
    • Applicable to other complex biological networks like signal transduction and gene regulation.