Making sense of parameter estimation and model simulation in bioprocesses

M. Constanza Sadino-Riquelme, José Rivas, DAVID ALEJANDRO JEISON NUÑEZ, Robert E. Hayes, Andrés Donoso-Bravo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Most articles that report fitted parameters for kinetic models do not include meaningful statistical information. This study demonstrates the importance of reporting a complete statistical analysis and shows a methodology to perform it, using functionalities implemented in computational tools. As an example, alginate production is studied in a batch stirred-tank fermenter and modeled using the kinetic model proposed by Klimek and Ollis (1980). The model parameters and their 95% confidence intervals are estimated by nonlinear regression. The significance of the parameters value is checked using a hypothesis test. The uncertainty of the parameters is propagated to the output model variables through prediction intervals, showing that the kinetic model of Klimek and Ollis (1980) can simulate with high certainty the dynamic of the alginate production process. Finally, the results obtained in other studies are compared to show how the lack of statistical analysis can hold back a deeper understanding about bioprocesses.

Original languageEnglish
Pages (from-to)1357-1366
Number of pages10
JournalBiotechnology and Bioengineering
Volume117
Issue number5
DOIs
StatePublished - 1 May 2020
Externally publishedYes

Keywords

  • confidence and prediction intervals
  • hypothesis test
  • kinetic model
  • parameter estimation

Fingerprint Dive into the research topics of 'Making sense of parameter estimation and model simulation in bioprocesses'. Together they form a unique fingerprint.

Cite this