Discriminating Direct and Indirect Connectivities in Biological Networks



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National Academy of Sciences



Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks.;


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Cells, Resampling (Statistics), Synthetic biology, Perturbation (Mathematics), Reverse engineering


This work was funded by the US National Institutes of Health Grants GM098984, GM096271, CA17001801, National Science Foundation Grant CBNET-1105524, and the University of Texas at Dallas. E.S. partially supported by Air Force Office of Scientific Research Grant FA9550- 14-1-0060.


©2015 National Academy of Sciences