Gene expression in growing cells: A biophysical primer

Biophysical studies have traditionally examined cell growth and gene expression as separate phenomena. Recent advances in single-cell techniques have sparked renewed interest in understanding how these processes interact. Prior research has shown that gene regulation was often modeled as a static process, ignoring the dynamic nature of the cell cycle. However, this gap motivated new investigations into how gene activity might be influenced by the progression of the cell cycle. The replication and dilution of cellular components during growth were already known to impact gene expression. Yet, the extent to which these factors shape global gene expression patterns remained unclear. Experimental findings have revealed that mRNA and protein levels fluctuate with cell volume and division. This uncertainty drove the need to explore how such fluctuations arise and whether they are consistent across genes and species. The challenge lies in reconciling these observations with theoretical models that can generalize across biological systems.

This review aims to synthesize current understanding of how gene expression is modulated by cell-cycle dynamics. The study addresses the specific problem of how gene activity is affected by the changing cellular environment during growth. The motivation stems from the observation that many theoretical models have overlooked the influence of cell-cycle progression. The authors seek to highlight experimental findings that demonstrate the coupling between cell growth and gene expression. They also aim to identify the molecular mechanisms behind observed scaling laws and their exceptions. The goal is to evaluate whether these patterns are consistent across different genes and organisms. The study seeks to bridge the gap between theoretical models and experimental data. By doing so, it provides a framework for understanding gene expression in growing cells.

The authors conducted a comprehensive review of recent experimental and theoretical studies. They focused on reports where gene expression and cell growth were measured simultaneously in individual cells. The review approach included analyzing data on mRNA and protein levels in relation to cell volume and cell-cycle progression. The authors examined how transcription and translation factors change during the cell cycle. They also considered the effects of genome replication and dilution on gene expression. The study incorporated stochastic models to explain fluctuations in gene expression. The authors evaluated how these factors contribute to heterogeneity in gene expression. The synthesis of findings aimed to identify common patterns and exceptions across different systems.

The strongest finding is that gene expression levels scale with cell volume and cell-cycle progression. Experimental reports show that mRNA and protein levels increase during the cell cycle but are diluted at division. The replication of the genome affects the dosage of transcription and translation factors. These changes modulate gene expression in a time-dependent manner. The authors observed that scaling laws break down under certain conditions, leading to non-canonical behavior. Stochasticity in synthesis and degradation contributes to variability in gene expression. The coupling between cell growth and gene expression is evident in fluctuating protein levels. Theoretical models have been proposed to explain these patterns and their exceptions.

The authors synthesize evidence that gene expression is not stationary but modulated by the cell cycle. They propose that gene activity is influenced by changes in transcription and translation factors during growth. The observed scaling of mRNA and protein levels with cell volume is a key finding. The breakdown of these scaling laws under specific conditions is also highlighted. The role of stochasticity in gene expression is emphasized in the review. The coupling between cell growth and gene expression is supported by experimental data. The authors suggest that theoretical models are emerging to unify these observations. These models aim to explain how gene expression patterns vary across genes and organisms.