Abstract

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 talk, we will introduce statistical and computational methods to identify quantitative aspects of the regulatory mechanisms underlying cell proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes accurately predict the instantaneous growth rate of any cellular culture, robust to changing biological conditions, experimental methods, and technological platforms. Our model also predicts 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 identified gene expression signature from multiple perspectives: by perturbing the regulatory network through the Ras/cAMP/PKA pathway, observing strong up-regulation of growth rate even in the absence of appropriate nutrients, and by discovering potential transcription factor binding sites enriched in growth-correlated genes. Most importantly, statistical and computational methods enable substantive biological insights about growth at instantaneous time scales inaccessible by direct experimental methods.

Short Bio

Edo Airoldi is an Assistant Professor of Statistics at Harvard, affiliated with the FAS Center for Systems Biology. He received a PhD in computer science from Carnegie Mellon University in 2006, and was a postdoctoral fellow at Princeton University from 2006-2008, in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science. His research interests include statistical and computational elements of complex networks, and the study of signaling and regulatory dynamics driving metabolism, cellular proliferation, cellular differentiation and development in unicellular organisms, mammalian systems and cancer.