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Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters
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  • Mohammad Karim Khaleghi,
  • Iman Shahidi Pour Savizi,
  • Nathan Lewis,
  • Seyed Abbas Shojaosadati
Mohammad Karim Khaleghi
Tarbiat Modares University

Corresponding Author:[email protected]

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Iman Shahidi Pour Savizi
Tarbiat Modares University
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Nathan Lewis
University of California, San Diego
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Seyed Abbas Shojaosadati
Tarbiat Modares University
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Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable models are produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.
23 Apr 2021Submitted to Biotechnology Journal
24 Apr 2021Submission Checks Completed
24 Apr 2021Assigned to Editor
12 May 2021Reviewer(s) Assigned
02 Jun 2021Editorial Decision: Revise Major
24 Jun 20211st Revision Received
26 Jun 2021Submission Checks Completed
26 Jun 2021Assigned to Editor
26 Jun 2021Reviewer(s) Assigned
16 Jul 2021Editorial Decision: Revise Minor
20 Jul 20212nd Revision Received
21 Jul 2021Submission Checks Completed
21 Jul 2021Assigned to Editor
21 Jul 2021Reviewer(s) Assigned
09 Aug 2021Editorial Decision: Revise Minor
10 Aug 20213rd Revision Received
11 Aug 2021Assigned to Editor
11 Aug 2021Submission Checks Completed
11 Aug 2021Reviewer(s) Assigned
11 Aug 2021Editorial Decision: Accept