The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling


The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems, e.g. by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of dynamical interactions reflects canalization—the system is robust to interventions in redundant pathways, but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally-validated models derived from systems biology, we demonstrate that: (a) redundant pathways are prevalent in biological models of biochemical regulation, (b) the effective graph provides a statistical but precise characterization of multivariate dynamics in a causal graph form, and (c) the effective graph provides an accurate explanation of how perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general, and biochemical regulation in particular.

Proceedings of the National Academy of Sciences (PNAS) 118 (12)

In the Media:

  1. PNAS ``Identifying `more equal than others’ edges in diverse biochemical networks’’

  2. Gulbenkian Science ``Uncovering the `master switches’ of biochemical networks can explain the effects of drugs in the destruction of cancer cells’’

  3. Publico (In Portuguese) ``Criado modelo que distingue principais interaccoes de genes com organismo’’

Alexander J. Gates
Alexander J. Gates
Assistant Professor

I am a computational social scientist and network scientist with a passion for uncovering how interconnectedness shapes our lives.