From plasmid sequence to process design: A computational analysis of metabolism in the context of plasmid DNA manufacturing.

Authors

Nikolaos Stratis, Massimo Morbidelli, Alexandros Kiparissides

Published in

Journal of biotechnology. Jun 27, 2026. Epub Jun 27, 2026.

Abstract

Detailed understanding of plasmid-host physiological interactions and recombinant protein expression is crucial for the design and optimization of plasmid DNA (pDNA) manufacturing processes. Successfully transformed host cells carrying one or more copies of a particular plasmid exhibit modified metabolic behavior which may include reduced specific growth rate, increased metabolic resource cycling (ATP/ADP, NAD/NADH, NADP/NADPH) and, in some cases, even modified nutrient uptake and metabolite secretion rates. Mapping plasmid design features to critical process parameters (CPPs), such as specific growth rate and productivity, remains an active area of metabolic resource allocation research, highlighting the need to systematically evaluate the metabolic burden incurred by plasmid-associated expression and/or replication. Herein we present, a comprehensive computational analysis based on carbon constrained Flux Balance Analysis (ccFBA) to precisely calculate the metabolic burden imposed by plasmid replication and recombinant protein expression. Explicit stoichiometric coefficients for nucleotides and amino acids were derived directly from the plasmid's sequence and were used to formulate biosynthetic reactions tailored to the components of individual plasmids. Three tunable parameters were introduced to map the impact of promoter strength, copy number and choice of selection marker on the host cell's metabolism. Literature derived experimental data from E. coli cultures producing three different plasmids were used to constrain the iJR904 Genome Scale Model (GeM). Our analysis revealed correlations between cellular growth rate, promoter strength and pDNA productivity with significant implications for the design of recombinant technology based manufacturing processes.

PMID:
42364778
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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