Mechanistic modelling of recombinant L-Asparaginase production by Escherichia coli - Simas, Rodrigo Gabriel (2024)
L-asparaginase type II from Escherichia coli is a biopharmaceutical used in the treatment of acute lymphoblastic leukaemia (ALL), the most common paediatric cancer. The high cost of ALL treatment is primarily driven by the cost of asparaginase, making the reduction of manufacturing costs crucial to lowering overall treatment expenses. Although attempts have been made to optimize cultivation conditions to increase titre and productivity, these efforts have been largely empirical and have met with varying degrees of success. This is partly due to the lack of a comprehensive mathematical model for ASNase expression - a challenge that also extends to recombinant protein expression in E. coli, for which comprehensive models remain surprisingly scarce. Mathematical models provide a more systematic approach to process optimisation, enabling in-silico studies that reduce both the cost and time associated with extensive experimentation. In this study, a robust kinetic model for recombinant protein expression in E. coliwas proposed, integrating current mechanistic knowledge of key physiological processes - cell growth, inducer transport, transcription, and translation - into a concise framework with independently verifiable parameters. The model was applied to study of ASNase expression by E. coli BL21(DE3). For ASNase quantification, the colorimetric Nessler method for ammonia measurement was adapted by incorporating polyvinyl alcohol and sodium tartrate to reduce interference from salts commonly found in cultivation media and purification buffers. The model was then used to investigate the impact of IPTG concentration (0 to 1000 µM) and temperature (23°C to 37°C) on recombinant asparaginase production. It accurately described conditions involving low transcription levels and validated common empirical observations, such as tunable expression at low IPTG concentrations (0 - 100 µM) and temperatures (≤30ºC). Additionally, the model predicted na inverse correlation between temperature and recombinant protein expression, as well as evidence for coupled transcriptiontranslation processes. In conclusion, the proposed model represents a significant advancement in understanding the dynamics of recombinant protein expression in E. coli. Its modular design allows for easy adaptation to various system designs and cultivation modes, enabling a more efficient and tailored approach to bioprocess development.