Cell Growth and Division Shape mRNA-Protein Correlations

Area of Science:

• Molecular and Cellular Biology
• Systems Biology
• Biophysics

Background:

• Correlations between cellular variables, like gene expression, offer insights into regulatory mechanisms.
• Previous analytical predictions for mRNA-protein correlations assumed constant cell volume, leading to discrepancies with experimental data.

Purpose of the Study:

• To re-examine and test analytical predictions for mRNA-protein correlations.
• To develop a theoretical framework incorporating cell growth and division to explain observed discrepancies.
• To derive a new analytical expression for mRNA-protein correlations in dynamic cellular environments.

Main Methods:

• Testing existing predictions on single-cell *E. coli* data.
• Developing a theoretical framework for mRNA-protein correlations in growing and dividing cells.
• Deriving an analytical expression for these correlations and validating it using stochastic simulations.

Main Results:

• Substantial disagreement was observed when testing previous predictions on *E. coli* data.
• The new theoretical framework, incorporating cell growth and division, significantly alters mRNA-protein correlations.
• The derived relation is invariant to upstream transcriptional dynamics and validated through simulations.

Conclusions:

• The assumption of constant cell volume is a key limitation in previous models of mRNA-protein correlations.
• Explicitly including cell growth and division provides a more accurate theoretical framework.
• The new predictions are consistent with experimental *E. coli* data, improving our understanding of gene expression regulation.