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Catabolite Regulation of the Main Metabolism
Published in Kazuyuki Shimizu, Metabolic Regulation and Metabolic Engineering for Biofuel and Biochemical Production, 2017
The overflow metabolism occurs due to the repression of the TCA cycle in response to the increased glycolytic flux, where the TCA cycle or the respiration is constrained by some threshold value. This may be reasoned by the protection against oxidative stress caused by the reactive oxygen species (ROSs) generated in the respiratory chain reaction. Most oxygen- dependent organisms furnish deliberate defense mechanisms against oxidative stress caused by ROSs (Fig. 10). The TCA cycle generates NADH, and this may generate ROSs during respiration, but also scavenges them by the production of NADPH at ICDH in some organisms such as E. coli and mammals, while some bacteria such as Pseudomonas fluorescens, etc. have both NADH- and NADPH-producing ICDH (Mailloux et al. 2007). The typical enzymes for detoxification of ROS are catalase, superoxide dismutase, and glutathione peroxidase, where the glutathione plays also an important role for this (Urso and Claekson 2003). The oxidative stress is transcriptionally regulated by such transcription factors as SoxR/S, OxyR, etc., and depending on NADPH availability (Nordberg and Arner 2001). RpoS plays important roles for general stress response including oxidative stress, where the expression of rpoS which encodes the alternative sigma factor cS is repressed by Crp (Barth et al. 2009, Basak and Jiang 2012). As the cell growth rate increases, cAMP-Crp level decreases, and thus RpoS is activated to cope with the oxidative stress.
Experimental validation of a full-horizon interval observer applied to hybridoma cell cultures
Published in International Journal of Control, 2020
Laurent Dewasme, Alain Vande Wouwer
Following the ever increasing demand for therapeutic products and the constraints imposed by the Food and Drug Administration, monitoring of bioprocesses has received dedicated attention of the pharmaceutical sector. Process Analytical Technologies (PAT) are nowadays an integral part of process development, which has boosted hardware and software instrumentation. Bioprocess monitoring is however still a delicate task because on-line hardware sensors are expensive, have specific operational constraints, and do not necessarily exist for the component of interest. Developing software sensors is therefore an appealing alternative to reconstruct on-line, at-line or off-line the missing information. Software sensors require some process a priori knowledge under the form of dynamic models. Previous optimisation studies of hybridoma cell cultures for MAb production were usually conducted using simple mathematical models based on macroscopic reaction schemes such as in de Tremblay, Perrier, Chavarie, and Archambault (1992) and Dhir, John Morrow, Rhinehart, and Wiesne (2000). A more detailed model is developed in Amribt, Niu, and Bogaerts (2013), which is based on the bottleneck assumption of Sonnleitner and Käppeli (1986) for the description of the overflow metabolism or short-term Crabtree effect (De Deken, 1966). In a recent work (Dewasme, Cote, Filee, Hantson, & Vande Wouwer, 2017), the authors derived a data-driven model based on a PCA of the available data to deduce the minimal set of macroscopic reactions, and nonlinear parameter estimation. The current work aims at exploiting these latter results to develop a robust state estimation strategy.