This study provides a mathematical model of T7 RNA polymerase (T7 RNAP) kinetics under in vitro conditions targeted at application of this model to simulation of dynamic transcription performance. A functional dependence of transcript synthesis rate is derived based on: (a) essential reactant concentrations, including T7 RNAP and its promoter, substrate nucleotides, and the inhibitory byproduct inorganic pyrophosphate; (b) a distinction among vector characteristics such as recognition sequences regulating transcription initiation and termination, respectively; and (c) specific properties of the nucleotide sequence including both transcript length and nucleotide composition. Inactivation kinetics showed a half‐life of T7 RNAP activity of 50 min under the conditions applied in vitro using the isolated enzyme. Model parameters and their precision are estimated using dynamic simulation and nonlinear regression analysis. The particular novelty of this model is its capability to incorporate linear genomic sequence information for simulation of nonlinear in vitro transcription kinetics. © 2001 John Wiley & Sons, Inc. Biotechnol Bioeng 72: 548–561, 2001.

This study provides a mathematical model of T7 RNA polymerase (T7 RNAP) kinetics under in vitro conditions targeted at application of this model to simulation of dynamic transcription performance. A functional dependence of transcript synthesis rate is derived based on: (a) essential reactant concentrations, including T7 RNAP and its promoter, substrate nucleotides, and the inhibitory byproduct inorganic pyrophosphate; (b) a distinction among vector characteristics such as recognition sequences regulating transcription initiation and termination, respectively; and (c) specific properties of the nucleotide sequence including both transcript length and nucleotide composition. Inactivation kinetics showed a half-life of T7 RNAP activity of 50 min under the conditions applied in vitro using the isolated enzyme. Model parameters and their precision are estimated using dynamic simulation and nonlinear regression analysis. The particular novelty of this model is its capability to incorporate linear genomic sequence information for simulation of nonlinear in vitro transcription kinetics.