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Surrogate Model Development for Digital Experiments in Welding

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A reliable simulator for dynamic flux balance analysis.

K Höffner¹, S M Harwood, P I Barton

¹Process System Engineering Laboratory, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Biotechnology and Bioengineering

|October 12, 2012

Summary

Dynamic flux balance analysis (DFBA) enables precise control of biochemical processes by integrating metabolic networks with dynamic simulations. This method accurately models substrate consumption and product secretion in bioreactors.

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Area of Science:

Biochemical Engineering
Systems Biology
Metabolic Engineering

Background:

Dynamic flux balance analysis (DFBA) is a powerful framework for modeling biochemical processes.
It integrates genome-scale metabolic network analysis with dynamic extracellular environment simulations.
Existing DFBA models often face challenges with computational efficiency and simulation accuracy, particularly near model boundaries.

Purpose of the Study:

To present a novel numerical tool for accurate and efficient simulation of large-scale DFBA models.
To improve upon existing DFBA implementations by addressing limitations in integration schemes and linear programming.

Main Methods:

Coupling of metabolic network models with dynamic mass balance equations for the extracellular environment.
Embedding linear programming (LP) within a dynamic bioreactor model, incorporating substrate uptake and product excretion rates as constraints.
Development of a numerical integration scheme with variable step size, solving the LP only when necessary to capture changes in optimal flux distribution.

Main Results:

The presented numerical tool accurately and efficiently simulates large-scale DFBA models.
Variable step size integration enhances computational efficiency.
Selective LP solving and reliable simulation near domain boundaries represent significant advantages over existing methods.

Conclusions:

The developed numerical tool offers a robust and efficient platform for DFBA simulations.
This advancement facilitates detailed design, control, and optimization of biochemical process technologies.
The tool's capabilities are demonstrated through large-scale literature examples.