Genetic network reverse-engineering and network size; can we identify large GRNs?

The reverse engineering of genetic regulatory networks (GRNs) is a highly challenging optimisation problem, surrounded by many unresolved questions concerning the extent to which we can regard a reverse-engineered GRN to reflect the target GRN, which we call the fidelity of the reverse engineered GRN. Related questions concern the ability of reverse-engineering algorithms to find networks that fit the data under consideration, that is, their accuracy. Most research works with networks two orders of magnitude smaller than those of biological interest, and the following question is consequently unexplored: how can we expect fidelity and accuracy to vary with network size? Answers to this question will reveal whether or not we can reliably extrapolate, to large networks, results obtained on the ability of reverse-engineering methods on small networks. We use real-world data to explore accuracy and fidelity of a simple GRN reverse-engineering approach, over sizes of networks varying from 100 to 6,000. We find that accurate networks can be found with ease at any size. However, the diversity of accurate reverse-engineered GRNs increases sharply between 100 and around 2,000 genes, then settling down to a maximal level, indicating that the fidelity of reverse-engineered networks is likely to decrease sharply with size.