The successful scale-up of biotechnological processes from laboratory to industrial scale is crucial for translating innovation to practice. Scale-down simulators have emerged as indispensable tools in this endeavor, enabling the evaluation of potential hosts' adaptability to the dynamic conditions encountered in large-scale fermenters. By simulating these real-world scenarios, scale-down simulators facilitate more accurate estimations of host productivity, thereby improving the process of selecting optimal strains for industrial production. Conventional scale-down systems for detailed intracellular analysis necessitate an elaborate setup comprising interconnected lab-scale reactors such as stirred tank reactors (STRs) and plug-flow reactors (PFRs), often proving time-consuming and resource-intensive. This work introduces a miniaturized bubble column reactor setup (60 mL working volume), enabling individual and parallel carbon-limited chemostat fermentations, offering a more efficient and streamlined approach. The industrially relevant organism , chosen as a model organism, is continuously grown and subjected to carbon starvation for 150 s, followed by a return to carbon excess for another 150 s. The cellular response is characterized by the accumulation of the alarmone guanosine pentaphosphate (ppGpp) accompanied by a significant reduction in energy charge, from 0.8 to 0.7, which is rapidly replenished upon reintroduction of carbon availability. Transcriptomic analysis reveals a two-phase response pattern, with over 200 genes upregulated and downregulated. The initial phase is dominated by the CRP-cAMP- and ppGpp-mediated response to carbon limitation, followed by a shift to stationary phase-inducing gene expression under the control of stress sigma factors. The system's validity is confirmed through a thorough comparison with a conventional STR/PFR setup. The analysis reveals the potential of the system to effectively reproduce data gathered from conventional STR/PFR setups, showcasing its potential use as a scale-down simulator integrated in the process of strain development.
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http://dx.doi.org/10.1002/elsc.202400051 | DOI Listing |
The successful scale-up of biotechnological processes from laboratory to industrial scale is crucial for translating innovation to practice. Scale-down simulators have emerged as indispensable tools in this endeavor, enabling the evaluation of potential hosts' adaptability to the dynamic conditions encountered in large-scale fermenters. By simulating these real-world scenarios, scale-down simulators facilitate more accurate estimations of host productivity, thereby improving the process of selecting optimal strains for industrial production.
View Article and Find Full Text PDFBiotechnol Prog
February 2025
Expression Systems and Novel Biopharmaceutical Materials, MilliporeSigma, Saint Louis, Missouri, USA.
As the industry continues to explore the benefits of continuous and intensified manufacturing, it is important to assure that the cell line development (CLD) workflows in practice today are well suited to generate clones that meet the unique challenges associated with these processes. Most cell lines used in intensified processes are currently developed using traditional fed-batch CLD workflows followed by adaptation of these cell lines to perfusion processes. This method maybe suboptimal as fed-batch CLD workflows select clones which produce high volumetric titers irrespective of cell growth rate and specific productivity (qP).
View Article and Find Full Text PDFPLoS One
December 2024
Department of Electrical Engineering, Stanford University, Stanford, California, United States of America.
BMC Biol
November 2024
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Genetics
January 2025
Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Simulations are an essential tool in all areas of population genetic research, used in tasks such as the validation of theoretical analysis and the study of complex evolutionary models. Forward-in-time simulations are especially flexible, allowing for various types of natural selection, complex genetic architectures, and non-Wright-Fisher dynamics. However, their intense computational requirements can be prohibitive to simulating large populations and genomes.
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