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Genetic regulatory networks (GRNs) have been widely studied, yet there is alack of understanding with regards to the final size and properties of thesenetworks, mainly due to no network currently being complete. In this study, weanalyzed the distribution of GRN structural properties across a large set ofdistinct prokaryotic organisms and found a set of constrained characteristicssuch as network density and number of regulators. Our results allowed us toestimate the number of interactions that complete networks would have, avaluable insight that could aid in the daunting task of network curation,prediction, and validation. Using state-of-the-art statistical approaches, wealso provided new evidence to settle a previously stated controversy thatraised the possibility of complete biological networks being random andtherefore attributing the observed scale-free properties to an artifactemerging from the sampling process during network discovery. Furthermore, weidentified a set of properties that enabled us to assess the consistency of theconnectivity distribution for various GRNs against different alternativestatistical distributions. Our results favor the hypothesis that highlyconnected nodes (hubs) are not a consequence of network incompleteness.Finally, an interaction coverage computed for the GRNs as a proxy forcompleteness revealed that high-throughput based reconstructions of GRNs couldyield biased networks with a low average clustering coefficient, showing thatclassical targeted discovery of interactions is still needed.

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions