Does it pay off to link functional gene expression to denitrification rates via modelling?

The abundances of functional genes and transcripts have provided new insights into microbially mediated biogeochemical processes and might improve quantitative predictions of turnover rates.
However, the relationship between reaction rates and the gene and transcript abundances may not be a simple correlation.
Most mechanistic reaction models cannot predict molecular-biological data, and it is unclear how they can be informed by such data.

We developed a mechanistic model that considers transcript abundances of denitrification genes, enzyme concentrations, biomass, and solute concentrations as state variables that are interrelated by ordinary differential equations, and thus mechanistically links molecular-biological data to reaction rates.
Important features of transcript dynamics can be reproduced with the transcript-based model.

We calibrated the model using data from a batch experiment with a denitrifying organism at the onset of anoxia.
We explored the relationship between transcript abundances and reaction rates by analyzing the model results.
The transcript abundances reacted very quickly to substrate concentrations so that we could simplify the model by assuming a quasi steady state of the transcripts.

We compared our model to a classical Monod-type formulation, which was as good at simulating the concentrations of nitrogen species as the transcript-based model, but it cannot make use of any molecular-biological data.
Our results, thus, suggest that enzyme kinetics (substrate limitation, inhibition) control denitrification rates more strongly than the dynamics of gene expression.