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Background  Cytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactions  in vivo  using murine lactation as a dynamic, physiological model system.    Results  Circulatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups. The resultant networks were very robust and assembled about structural hubs, with evidence that interleukin (IL)-12 (p40), IL-13 and monocyte chemoattractant protein (MCP)-1 were the primary drivers of network behavior. Network structural conservation across physiological scenarios coupled with the successful empirical validation of our approach suggested that  in silico  network perturbations can predict  in vivo  qualitative responses.  In silico  perturbation of network components also captured biological features of cytokine interactions (antagonism, synergy, redundancy).    Conclusion  These findings highlight the potential of network-based approaches in identifying novel cytokine pharmacological targets and in predicting the effects of their exogenous manipulation in inflammatory/immune disorders.    Electronic supplementary material  The online version of this article (doi:10.1186/s12918-015-0226-3) contains supplementary material, which is available to authorized users.

Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo