Person:

Barzel, Baruch

Predicting physical and functional links between cellular components is a fundamental challenge of biology and network science. Yet, correlations, a ubiquitous input for biological link prediction, are affected by both direct and indirect effects, confounding our ability to identify true pairwise interactions. Here we exploit the fundamental properties of dynamical correlations in networks to develop a method to silence indirect effects. The method receives as input the observed correlations between node pairs and uses a matrix transformation to turn the correlation matrix into a highly discriminative silenced matrix, which enhances only the terms associated with direct causal links. Achieving perfect accuracy in model systems, we test the method against empirical data collected for the Escherichia coli regulatory interaction network, showing that it improves on the best preforming link prediction methods. Overall the silencing methodology helps translate the abundant correlation data into valuable local information, with applications ranging from link prediction to inferring the dynamical mechanisms governing biological networks.

Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system's response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system's dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system's dynamics.