Exploring the impact of resource limitations on gene network reconstruction

The characterization of biological networks via mathematical models often involves cycles of experimental perturbations and measurements, followed by the use of a network inference method. Here we study an engineered genetic circuit, introduced in a recent paper by the authors, and report additional analysis and interpretation. Using this synthetic network as a benchmark, we find that the application of the modular response analysis (MRA) network inference method leads to the discovery of a hidden, nontrivial “ghost” regulatory edge, which was not explicitly engineered into the network. Importantly, this result is not evident from direct inspection of the experimental measurements and global response coefficients. To probe the global to local conversion in MRA, we use conditionally randomized global response matrices to obtain distributions of local response coefficients and demonstrate that sign changes are numerically possible. Additionally, using simulations of a cascade network in a biochemical setting which does not take into account resource limitations, we show that MRA cannot return “ghost” edges, which points to the impact of the cellular milieu and in particular the use of shared resources. Taking resource availability into account during reverse engineering may allow for closer approximation of the cellular environment and points to a potential opportunity for network characterization strategies.