Scaling strategy for cell and gene therapy bioreactors based on turbulent parameters

So far, power input has been used as the main parameter for bioreactor scale-up/-down in upstream process development and manufacturing. The rationale is that maintaining a consistent power input per unit volume should result in comparable mixing times at different scales. However, shear generated from turbulent flow may compromise the integrity of non-robust cells such as those used during the production of cell and gene therapies, which may lead to low product quality and yield. Of particular interest is the Kolmogorov length parameter that characterizes the smallest turbulent eddies in a mixture. To understand its impact on scale-up/-down decisions, the distribution of Kolmogorov length along the trajectory flow of individual particles in bioreactors was estimated in silico with the help of computational fluid dynamics simulations. Specifically, in this study the scalability of iPSC-derived lymphocyte production and the impact of shear stress across various differentiation stages were investigated. The study used bioreactors of volumes from 0.1 to 10 L, which correspond to the scales most used for parameter optimization. Our findings, which align with in vitro runs, help determine optimal agitation speed and shear stress adjustments for process transfer between scales and bioreactor types, using vertically-oriented wheel and pitched-blade impellers. In addition, empirical models specific to the bioreactors used in this study were developed. The provided computational analysis in combination with experimental data supports selection of appropriate bioreactors and operating conditions for various cell and gene therapy process steps.

Advances in promoter engineering: novel applications and predefined transcriptional control

Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.

Identifying Regulatory Changes to Facilitate Nitrogen Fixation in the Nondiazotroph Synechocystis sp. PCC 6803

The incorporation of biological nitrogen fixation into a nondiazotrophic photosynthetic organism provides a promising solution to the increasing fixed nitrogen demand, but is accompanied by a number of challenges for accommodating two incompatible processes within the same organism. Here we present regulatory influence networks for two cyanobacteria, Synechocystis PCC 6803 and Cyanothece ATCC 51142, and evaluate them to co-opt native transcription factors that may be used to control the nif gene cluster once it is transferred to Synechocystis. These networks were further examined to identify candidate transcription factors for other metabolic processes necessary for temporal separation of photosynthesis and nitrogen fixation, glycogen catabolism and cyanophycin synthesis. Two transcription factors native to Synechocystis, LexA and Rcp1, were identified as promising candidates for the control of the nif gene cluster and other pertinent metabolic processes, respectively. Lessons learned in the incorporation of nitrogen fixation into a nondiazotrophic prokaryote may be leveraged to further progress the incorporation of nitrogen fixation in plants.