Literature DB >> 31814101

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses.

Guan Wang1, Cees Haringa2,3, Wenjun Tang3, Henk Noorman3,4, Ju Chu1, Yingping Zhuang1, Siliang Zhang1.   

Abstract

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.
© 2019 Wiley Periodicals, Inc.

Keywords:  CFD; Euler-Langrange; metabolic model; metabolomics; population heterogeneity; scale down

Mesh:

Year:  2019        PMID: 31814101     DOI: 10.1002/bit.27243

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  5 in total

1.  Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down.

Authors:  Peter Neubauer; Emmanuel Anane; Stefan Junne; Mariano Nicolas Cruz Bournazou
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

Review 2.  Cephalosporin C biosynthesis and fermentation in Acremonium chrysogenum.

Authors:  Ling Liu; Zhen Chen; Wuyi Liu; Xiang Ke; Xiwei Tian; Ju Chu
Journal:  Appl Microbiol Biotechnol       Date:  2022-09-17       Impact factor: 5.560

3.  Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors.

Authors:  Christopher Sarkizi Shams Hajian; Julia Zieringer; Ralf Takors
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

Review 4.  Optimization and Scale-Up of Fermentation Processes Driven by Models.

Authors:  Yuan-Hang Du; Min-Yu Wang; Lin-Hui Yang; Ling-Ling Tong; Dong-Sheng Guo; Xiao-Jun Ji
Journal:  Bioengineering (Basel)       Date:  2022-09-14

5.  Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data.

Authors:  Alexander Reiter; Jian Asgari; Wolfgang Wiechert; Marco Oldiges
Journal:  Metabolites       Date:  2022-03-17
  5 in total

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