Maintaining balanced growth in a changing environment is a fundamental
systems-level challenge for cellular physiology, particularly in microorganisms.
While the complete set of regulatory and functional pathways supporting growth
and cellular proliferation are not yet known, portions of them are well
understood. In particular, cellular proliferation is governed by mechanisms that
are highly conserved from unicellular to multicellular organisms, and the
disruption of these processes in metazoans is a major factor in the development
of cancer. In this paper, we develop statistical methodology to identify
quantitative aspects of the regulatory mechanisms underlying cellular
proliferation in Saccharomyces cerevisiae. We find that the
expression levels of a small set of genes can be exploited to predict the
instantaneous growth rate of any cellular culture with high accuracy. The
predictions obtained in this fashion are robust to changing biological
conditions, experimental methods, and technological platforms. The proposed
model is also effective in predicting growth rates for the related yeast
Saccharomyces bayanus and the highly diverged yeast
Schizosaccharomyces pombe, suggesting that the underlying
regulatory signature is conserved across a wide range of unicellular evolution.
We investigate the biological significance of the gene expression signature that
the predictions are based upon from multiple perspectives: by perturbing the
regulatory network through the Ras/PKA pathway, observing strong upregulation of
growth rate even in the absence of appropriate nutrients, and discovering
putative transcription factor binding sites, observing enrichment in
growth-correlated genes. More broadly, the proposed methodology enables
biological insights about growth at an instantaneous time scale, inaccessible by
direct experimental methods. Data and tools enabling others to apply our methods
are available at http://function.princeton.edu/growthrate.