Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Area of Science:

• Environmental microbiology
• Biogeochemical modeling
• Molecular ecology

Background:

• Environmental omics data offer potential for quantitative predictions of biogeochemical processes.
• Microbial functional gene and transcript abundances correlate with cell numbers and activity.
• A key question is whether molecular data can be quantitatively linked to reaction rates.

Purpose of the Study:

• To develop and present an enzyme-based denitrification model.
• To simulate concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes.
• To investigate the quantitative link between molecular data and reaction rates.

Main Methods:

• Developed an enzyme-based denitrification model.
• Calibrated the model using experimental data from a batch experiment with *Paracoccous denitrificans*.
• Compared model predictions to measured transcript dynamics and denitrification rates.

Main Results:

• The model accurately predicted denitrification rates and transcript dynamics.
• Strong non-linearity and hysteresis were observed between transcript concentrations and reaction rates.
• The enzyme-based model performed similarly to a classical Monod-type model for nitrogen species.

Conclusions:

• Assuming a unique transcript-to-gene ratio to reaction rate relationship is an oversimplification.
• Integrating molecular data showed limited benefit for estimating denitrification rates compared to traditional models.
• The enzyme-based model enhances mechanistic understanding of biomolecular quantity-reaction rate relationships and highlights the importance of enzyme kinetics and gene expression dynamics.