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A 9‐pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum
Author(s) -
Tang Wenjun,
Deshmukh Amit T.,
Haringa Cees,
Wang Guan,
van Gulik Walter,
van Winden Wouter,
Reuss Matthias,
Heijnen Joseph J.,
Xia Jianye,
Chu Ju,
Noorman Henk J.
Publication year - 2017
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.26294
Subject(s) - penicillium chrysogenum , biological system , population , computational fluid dynamics , metabolic engineering , scale (ratio) , biochemical engineering , chemistry , biology , mechanics , physics , biochemistry , engineering , enzyme , demography , quantum mechanics , sociology
A powerful approach for the optimization of industrial bioprocesses is to perform detailed simulations integrating large‐scale computational fluid dynamics (CFD) and cellular reaction dynamics (CRD). However, complex metabolic kinetic models containing a large number of equations pose formidable challenges in CFD‐CRD coupling and computation time afterward. This necessitates to formulate a relatively simple but yet representative model structure. Such a kinetic model should be able to reproduce metabolic responses for short‐term (mixing time scale of tens of seconds) and long‐term (fed‐batch cultivation of hours/days) dynamics in industrial bioprocesses. In this paper, we used Penicillium chrysogenum as a model system and developed a metabolically structured kinetic model for growth and production. By lumping the most important intracellular metabolites in 5 pools and 4 intracellular enzyme pools, linked by 10 reactions, we succeeded in maintaining the model structure relatively simple, while providing informative insight into the state of the organism. The performance of this 9‐pool model was validated with a periodic glucose feast–famine cycle experiment at the minute time scale. Comparison of this model and a reported black box model for this strain shows the necessity of employing a structured model under feast–famine conditions. This proposed model provides deeper insight into the in vivo kinetics and, most importantly, can be straightforwardly integrated into a computational fluid dynamic framework for simulating complete fermentation performance and cell population dynamics in large scale and small scale fermentors. Biotechnol. Bioeng. 2017;114: 1733–1743. © 2017 Wiley Periodicals, Inc.