is supported by NIH K01 award 1K01DK095008. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09). holds a Packard Fellowship for Science and Engineering. This research was supported by the National Science Foundation CAREER award through grant MCB-1149728 and National Centers for Biomedical Computing Grant 1U54CA121852-01A1 and the NIH S10 Shared instrumentation grant NIH S10 SIG S10RR027582-01. We respectfully appreciate the provision of mouse samples and key mechanistic discussions with C.

We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. In comparing signaling between naïve and antigen-exposed CD4 + T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. However, the challenge of developing and applying computational approaches to interpret such complex data remains. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins.

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