Abstract

Recent experimental advances in biology allow researchers to obtain gene expression profiles at single-cell resolution over hundreds, or even thousands of cells at once. These single-cell measurements provide snapshots of the states of the cells that make up a tissue, instead of the population-level averages provided by conventional high-throughput experiments. This new data therefore provides an exciting opportunity for computational modelling.

A fundamental challenge in biology is to understand the gene regulatory networks which control how tissue development occurs in the mammalian embryo. We studied the first emergence of blood in the mammalian embryo by single cell expression analysis of 3,934 cells at four sequential developmental stages. Taking advantage of the single-cell resolution of the data, we treated expression profiles as states of an asynchronous Boolean network and framed the gene regulatory network inference as the problem of reconstructing a Boolean network from its state space. We then introduced a scalable algorithm to solve this synthesis problem. Our technique synthesises a matching Boolean network, and analysis of this model yields new predictions about blood development which our experimental collaborators were able to verify.