A 9‐pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum

Abstract

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.

Comparative performance of different scale‐down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

“…We assume that the specific glucose uptake rate (q s ) during a complete cycle is a hyperbolic function of C s , according to q s = . With the estimated parameters = 0.0417 mol CmolX −1 h −1 and K s = 7.8 μM (Tang et al ., ), the q s profiles in the IFRs of 3 min and 6 min can be reproduced very well (Fig. S2), but not in the 30 s IFR.…”

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

“…Haringa et al () integrated the 9‐pool penicillin model of Tang et al () into their full‐scale CFD simulation, observing a 33% loss in production rate and yield under chemostat conditions and good agreement with industrial production rate dynamics under fed‐batch conditions (Figure a). In the 9‐pool metabolic model, the glucose uptake rate () is subject to transporter control, where the availability of transporter () is controlled by growth rate µ (h −1 ) and the penicillin producing enzyme is inhibited by the lumped metabolic pool of glycolytic intermediates (; Tang et al, ). However, during the feast‐famine cycles, the simulation results showed that the growth rate oscillated between 0 and 0.15 hr −1 with the average value of 0.03 hr −1 , and the pool size of the glycolytic intermediate oscillates between 0 and 50 µmolC/gDW with the average value of 25 µmolC/gDW (Haringa et al, ).…”

“…As an example, as shown in Figure , our research group has developed and validated standard operating procedures (SOPs) for fast sampling, quenching, and efficient extraction of metabolites as well as preparation of U‐ 13 C metabolites as internal standards from P. chrysogenum to allow for global analysis of the metabolome under both steady‐state and nonstationary conditions (Wang, Chu, et al, ). This protocol has been exactly followed in our previous studies, which delivered invaluable information for in silico metabolic modeling and revealed regulatory mechanisms in response to genetic or environmental perturbations (Tang et al, ; Wang, Chu, et al, ; Wang, Chu, et al, ; Wang, Wu, et al, ; Wang, Zhao, et al, ; Wang, Zhao, et al, ).…”

“…A recent pioneering work by Hackett et al (), based on the measurements of metabolite and enzyme concentrations as well as metabolic fluxes across 25 different yeast chemostat cultures by simultaneously modulating both limiting nutrient and dilution rate, has demonstrated that metabolic reaction rates are collectively driven by both substrate concentrations (the strongest driver) and metabolite concentrations, with the physiological impact double than that of enzymes in terms of manipulating metabolic fluxes. In industrial bioprocesses, metabolic responses induced by both short‐term (at the timescales of tens of seconds to minutes) and long‐term (at the timescales of hours to days) dynamics provide informative insights into the metabolic switch of the microbes and require the establishment of metabolic models allowing a rational bioprocess optimization and scale‐up (Haringa et al, ; Tang et al, ). Further, metabolic behavior/response can be understood on a system level by integrating experimental evidence with mathematical models.…”

“…of microbes in industrial bioreactors. Thereinto, integrated metabolic models describing the biophase have been developing from simple saturation‐type kinetics of sugar uptake model (Larsson et al, ) to recently 9‐pool metabolic structured model (Haringa et al, ; Tang et al, ). As metabolic modeling progressed, more mechanistic insight into the in vivo kinetics can be obtained and more important, cell population dynamics and complete bioprocess performance in different scales of fermentors can be in silico simulated before bioprocess scaling‐up (Haringa et al, ).…”