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Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism
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Global optimization of the Saccharomyces cerevisiae: fermentation process.

Lakshmi N Sridhar1

  • 1Chemical Engineering Dept., University of Puerto Rico, Mayaguez, PR, 00681-9046. lakshmin.sridhar@upr.edu

Biotechnology Progress
|June 20, 2013
PubMed
Summary

This study optimized Saccharomyces cerevisiae fermentation, finding multiple solutions. The LINDO solver identified the global optimum, even on the feasible boundary, highlighting challenges in yeast fermentation optimization.

Keywords:
fermentationoptimization

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Area of Science:

  • Biochemical Engineering
  • Optimization Theory

Background:

  • The Saccharomyces cerevisiae fermentation process is complex, exhibiting nonlinearities and bifurcations.
  • Previous research has explored the model's dynamics but not its optimization challenges.

Purpose of the Study:

  • To perform steady-state optimization of Saccharomyces cerevisiae fermentation.
  • To investigate the challenges posed by the Jones-Kompala model to optimization.
  • To identify globally optimum solutions using various optimization strategies.

Main Methods:

  • Utilized the Jones-Kompala model for steady-state fermentation.
  • Employed NEOS global optimization solver LINDO.
  • Applied branch and bound (bnb20.m), global search, and multistart algorithms from MATLAB.
  • Varied dilution rate and oxygen mass transfer coefficient as decision variables.

Main Results:

  • Identified multiple optimum solutions in the Saccharomyces cerevisiae fermentation process.
  • LINDO successfully found the global optimum, including a non-stationary point on the feasible boundary.
  • MATLAB optimization toolbox algorithms found locally optimum solutions, validated by objective function plots.

Conclusions:

  • The choice of optimization solver is critical for identifying the true global optimum in complex fermentation processes.
  • Non-stationary points on the feasible boundary can represent the most beneficial outcomes.
  • This study addresses the previously uninvestigated optimization challenges of the Jones-Kompala model.