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Cheng M, Xie X, Schmitz P, Fillaudeau L. Extensive review about industrial and laboratory dynamic filtration modules: Scientific production, configurations and performances. Sep Purif Technol 2021. [DOI: 10.1016/j.seppur.2020.118293] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Morchain J, Quedeville V, Fox RO, Villedieu P. The closure issue related to liquid-cell mass transfer and substrate uptake dynamics in biological systems. Biotechnol Bioeng 2021; 118:2435-2447. [PMID: 33713345 DOI: 10.1002/bit.27752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/12/2021] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
An original dynamic model for substrate uptake under transient conditions is established and used to simulate a variety of biological responses to external perturbations. The actual uptake and growth rates, treated as cell properties, are part of the model variables as well as the substrate concentration at the cell-liquid interface. Several regulatory loops inspired by the structure of the glycolytic chain are considered to establish a set of ordinary differential equations. The uptake rate evolves so as to reach an equilibrium between the cell demand and the environmental supply. This model does not contain any of the usual algebraic closure laws relating to the instantaneous uptake, growth rates, and the substrate concentration, nor does it enforce the continuity of mass fluxes at the liquid-cell interface. However, these relationships are found in the steady-state solution. Previously unexplained experimental observations are well reproduced by this model. Also, the model structure is suitable for further coupling with flux-based metabolic models and fluid-flow equations.
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Affiliation(s)
- Jérôme Morchain
- TBI, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France.,FERMaT, CNRS, INPT, INSA, UPS, Université de Toulouse, Toulouse, France
| | - Vincent Quedeville
- TBI, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France.,FERMaT, CNRS, INPT, INSA, UPS, Université de Toulouse, Toulouse, France
| | - Rodney O Fox
- FERMaT, CNRS, INPT, INSA, UPS, Université de Toulouse, Toulouse, France.,Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA
| | - Philippe Villedieu
- Institut de Mathématiques de Toulouse, Université de Toulouse, Toulouse, France.,ONERA/DMPE, Université de Toulouse, Toulouse, France
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Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 32978650 DOI: 10.1007/10_2020_133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.
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Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach. Processes (Basel) 2020. [DOI: 10.3390/pr8121554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.
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Wang G, Haringa C, Noorman H, Chu J, Zhuang Y. Developing a Computational Framework To Advance Bioprocess Scale-Up. Trends Biotechnol 2020; 38:846-856. [DOI: 10.1016/j.tibtech.2020.01.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/27/2020] [Accepted: 01/29/2020] [Indexed: 01/10/2023]
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Haringa C, Mudde RF, Noorman HJ. From industrial fermentor to CFD-guided downscaling: what have we learned? Biochem Eng J 2018. [DOI: 10.1016/j.bej.2018.09.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Pigou M, Morchain J, Fede P, Penet MI, Laronze G. An assessment of methods of moments for the simulation of population dynamics in large-scale bioreactors. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.05.026] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A population balance model for bioreactors combining interdivision time distributions and micromixing concepts. Biochem Eng J 2017. [DOI: 10.1016/j.bej.2016.09.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Haringa C, Deshmukh AT, Mudde RF, Noorman HJ. Euler-Lagrange analysis towards representative down-scaling of a 22 m 3 aerobic S. cerevisiae fermentation. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.01.014] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Haringa C, Noorman HJ, Mudde RF. Lagrangian modeling of hydrodynamic–kinetic interactions in (bio)chemical reactors: Practical implementation and setup guidelines. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.07.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Investigating the interactions between physical and biological heterogeneities in bioreactors using compartment, population balance and metabolic models. Chem Eng Sci 2015. [DOI: 10.1016/j.ces.2014.11.035] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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