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Related Experiment Video

Updated: Jun 17, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Metabolic flux estimation using particle swarm optimization with penalty function.

Hai-Xia Long¹, Wen-Bo Xu, Jun Sun

¹School of Information Technology, Jiangnam University, Wuxi, Jiangsu 214122, China. haixia_long@163.com

Rivista Di Biologia

|January 16, 2010

Summary

This summary is machine-generated.

This study introduces a particle swarm optimization (PSO) method for metabolic flux estimation using 13C tracing. The novel approach efficiently quantifies intracellular metabolic fluxes with superior performance and faster convergence.

Related Experiment Videos

Last Updated: Jun 17, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Area of Science:

Metabolic Engineering
Systems Biology
Computational Biology

Background:

Intracellular metabolic flux estimation using 13C tracing is vital for understanding cellular metabolism.
This estimation is typically framed as a constrained optimization problem, seeking to minimize discrepancies between experimental and simulated data.
Existing methods face challenges in efficiently handling complex stoichiometric constraints.

Purpose of the Study:

To develop and evaluate a novel particle swarm optimization (PSO) algorithm with a penalty function for metabolic flux estimation.
To address the challenges of constrained optimization in 13C-based metabolic flux analysis.
To improve the accuracy and efficiency of quantifying intracellular metabolic fluxes.

Main Methods:

A particle swarm optimization (PSO) algorithm incorporating a penalty function was developed.
Stoichiometric constraints were transformed into an unconstrained objective function through penalization.
The algorithm was applied to estimate central metabolic fluxes in Corynebacterium glutamicum.

Main Results:

The proposed PSO algorithm demonstrated superior performance in metabolic flux estimation.
The algorithm exhibited fast convergence abilities compared to existing methods.
Simulation results validated the effectiveness of the penalty function approach for handling constraints.

Conclusions:

The developed PSO with penalty function is an effective and efficient tool for 13C-based metabolic flux estimation.
This method offers a robust solution for quantifying intracellular metabolic fluxes, particularly in complex biological systems.
The findings contribute to advancing systems biology and metabolic engineering research through improved computational approaches.