  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. In this
paper we introduce the idea of viewing single-cell gene expression profiles as
states of an asynchronous Boolean network, and frame model inference as the
problem of reconstructing a Boolean network from its state space. We then give
a scalable algorithm to solve this synthesis problem. We apply our technique to
both simulated and real data. We first apply our technique to data simulated
from a well established model of common myeloid progenitor differentiation. We
show that our technique is able to recover the original Boolean network rules.
We then apply our technique to a large dataset taken during embryonic
development containing thousands of cell measurements. Our technique
synthesises matching Boolean networks, and analysis of these models yields new
predictions about blood development which our experimental collaborators were
able to verify.
