Gene regulatory networks (GRNs) describe how a collection of genes governs
the processes within a cell. Understanding how GRNs manage to consistently
perform a particular function constitutes a key question in cell biology. GRNs
are frequently modeled as Boolean networks, which are intuitive, simple to
describe, and can yield qualitative results even when data is sparse.
We generate an expandable database of published, expert-curated Boolean GRN
models, and extracted the rules governing these networks. A meta-analysis of
this diverse set of models enables us to identify fundamental design principles
of GRNs.
The biological term canalization reflects a cell's ability to maintain a
stable phenotype despite ongoing environmental perturbations. Accordingly,
Boolean canalizing functions are functions where the output is already
determined if a specific variable takes on its canalizing input, regardless of
all other inputs. We provide a detailed analysis of the prevalence of
canalization and show that most rules describing the regulatory logic are
highly canalizing. Independent from this, we also find that most rules exhibit
a high level of redundancy. An analysis of the prevalence of small network
motifs, e.g. feed-forward loops or feedback loops, in the wiring diagram of the
identified models reveals several highly abundant types of motifs, as well as a
surprisingly high overabundance of negative regulations in complex feedback
loops. Lastly, we provide the strongest evidence thus far in favor of the
hypothesis that GRNs operate at the critical edge between order and chaos.Comment: 12 pages, 8 figure