Abstract
Summary Quantitative, directional network structure inference remains challenging even for small systems, particularly when loops and cycles are present. We report a method that robustly infers direct, signed connections between network nodes from noisy, sparse perturbation time course data requiring only one perturbation per node. We find good sensitivity and specificity for classification, as well as quantitative agreement in randomized 2- and 3-node systems having varied and complex dynamics. Experimental application of the method to the ERK and AKT pathways, widely important in mammalian signaling, reveals evidence of bi-directional cross-talk coupled with strong negative feedback on both pathways, consistent with prior knowledge. Systematic application of this method can reduce important subnetwork structural uncertainty, enabling better prediction of dynamics, response to perturbations such as drugs, and understanding of biological networks. The method is general and can be applied to any network inference problem where perturbation time course experiments are possible.
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