  Gene regulatory networks (GRNs) are increasingly used for explaining
biological processes with complex transcriptional regulation. A GRN links the
expression levels of a set of genes via regulatory controls that gene products
exert on one another. Boolean networks are a common modeling choice since they
balance between detail and ease of analysis. However, even for Boolean networks
the problem of fitting a given network model to an expression dataset is
NP-Complete. Previous methods have addressed this issue heuristically or by
focusing on acyclic networks and specific classes of regulation functions. In
this paper we introduce a novel algorithm for this problem that makes use of
sampling in order to handle large datasets. Our algorithm can handle time
series data for any network type and steady state data for acyclic networks.
Using in-silico time series data we demonstrate good performance on large
datasets with a significant level of noise.
