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Synopsis The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists. 

Social challenges like territorial intrusions evoke behavioral responses in widely diverging species. Recent work has showed that evolutionary “toolkits”—genes and modules with lineage‐specific variations but deep conservation of function—participate in the behavioral response to social challenge. Here, we develop a multispecies computational‐experimental approach to characterize such a toolkit at a systems level. Brain transcriptomic responses to social challenge was probed via RNA‐seq profiling in three diverged species—honey bees, mice and three‐spined stickleback fish—following a common methodology, allowing fair comparisons across species. Data were collected from multiple brain regions and multiple time points after social challenge exposure, achieving anatomical and temporal resolution substantially greater than previous work. We developed statistically rigorous analyses equipped to find homologous functional groups among these species at the levels of individual genes, functional and coexpressed gene modules, and transcription factor subnetworks. We identified six orthogroups involved in response to social challenge, including groups represented by mouse genesNpas4andNr4a1, as well as common modulation of systems such as transcriptional regulators, ion channels, G‐protein‐coupled receptors and synaptic proteins. We also identified conserved coexpression modules enriched for mitochondrial fatty acid metabolism and heat shock that constitute the shared neurogenomic response. Our analysis suggests a toolkit wherein nuclear receptors, interacting with chaperones, induce transcriptional changes in mitochondrial activity, neural cytoarchitecture and synaptic transmission after social challenge. It shows systems‐level mechanisms that have been repeatedly co‐opted during evolution of analogous behaviors, thus advancing the genetic toolkit concept beyond individual genes.