(614b) Synthetic Networks Unravel Divergent Features in Direct and Indirect Connectivities

Reverse engineering and identification of biological pathways commonly involves an iterative process between experiments, data processing, and theoretical analysis. The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools. Despite concurrent advances in quality and quantity of available data and the computing resources and algorithms, difficulties in deciphering certain types of network architectures are still prevalent. One such bottleneck is discriminating between direct and indirect connections. In a simple cascade motif formulation where an input node is activating an intermediary node which in turn is activating an output node, a reverse engineering algorithm may incorrectly predict a direct activation edge from the input node to the output. The pervasiveness of this systematic error justifies the urgent need for novel approaches and insights. We previously introduced the strategy of using a synthetic gene network in human cells as a benchmark for reverse engineering validation and refinement. In this work, we extend the notions of abstraction, emulation, benchmarking and validation in the context of identifying specific traits associated with direct and indirect connectivities. For this purpose, we constructed a pair of synthetic networks that emulate cascade and feedforward loop motifs. After subjecting our networks to cycles of perturbation and reconstruction, we converge to traits that can assist in identifying these networks in the context of naive reverse engineering, a useful supplement to existing inference methodologies.