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Proc Natl Acad Sci U S A. 2017 May 30;114(22):5755-5760. doi: 10.1073/pnas.1615660114. Epub 2017 May 12.

Fundamental trade-offs between information flow in single cells and cellular populations.

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Center for Computational Biology, University of Kansas, Lawrence, KS 66047.
Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115.
Center for Computational Biology, University of Kansas, Lawrence, KS 66047;
Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047.
Santa Fe Institute, Santa Fe, NM 87501.


Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3-4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.


apoptosis; cellular heterogeneity; information theory; signal transduction

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