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Updated: Jul 3, 2026
10:36
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
Classification, error detection, and reconciliation of process information in complex biochemical systems
H J Noorman1, B Romein, K C Luyben
1Gist-brocades by, PO Box 1, 2600 MA Delft, The Netherlands.
Biotechnology and Bioengineering
|February 20, 1996
Summary
This study presents a systematic method for integrating diverse bioprocess information, including metabolic data, to improve process understanding and identify bottlenecks. The approach accurately predicts yields for Acinetobacter calcoaceticus, guiding process improvements.
Area of Science:
- Biotechnology
- Biochemical Engineering
- Metabolic Engineering
Background:
- Bioprocess identification relies on integrating diverse information, often facing challenges with data quality and consistency.
- Existing methods for bioprocess modeling are limited, especially when dealing with complex metabolic networks and dispersed data sources.
Purpose of the Study:
- To generalize a systematic method for bioprocess identification by incorporating metabolic network data and varied information types.
- To demonstrate the application of this method using Acinetobacter calcoaceticus growing on acetate and xylose.
- To illustrate how optimal information selection and bottleneck analysis drive process improvements.
Main Methods:
- Utilizing conservation constraints to systematically integrate diverse data, including metabolic network information, kinetic expressions, and empirical correlations.
- Applying the method to the alkalophilic bacterium Acinetobacter calcoaceticus, utilizing acetate and xylose as substrates.
- Treating all available information as constraints within a unified framework.
Main Results:
- The generalized method effectively handles missing, inconsistent, or inaccurate data.
- Metabolic network data and dispersed process information were systematically integrated for Acinetobacter calcoaceticus.
- Accurate prediction of A. calcoaceticus yield on acetate at varying xylose/acetate feed ratios was achieved.
Conclusions:
- The developed method provides a straightforward and systematic approach to bioprocess identification and data integration.
- Optimal information selection and bottleneck analysis are crucial for directed bioprocess improvements.
- This approach enables accurate prediction of microbial yields, facilitating enhanced bioprocess design and optimization.
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