A Statistical Approach to Cellular Resource Allocation Models

Understanding how cells regulate growth despite molecular complexity remains a central question in quantitative biology. While thousands of genes respond to environmental perturbations, the population growth rate varies smoothly across conditions, suggesting the existence of simple organizing principles. Here, we show that statistical analysis of mRNA composition across environmental conditions reveals growth tradeoffs across organisms including E. coli and S. pombe . Using partial least squares regression, we identify two opposing gene sectors whose coordinated expression encodes growth rate. A minimal transcription-translation model, constrained by empirical scaling laws of total mRNA and ribosomal fractions, explains this tradeoff as a necessary consequence of the empirical observations. Extending the model to include charged tRNA dynamics reveals distinct regulatory regimes: E. coli operates co-limited by ribosomal mRNA and charged tRNA availability, whereas S. cerevisiae is primarily ribosomal mRNA-limited. Together, these results provide a statistical method to determine key tradeoffs across organisms and offer a framework to interpret organism-specific growth regimes.