Information processing by heterogeneous cell populations

Cells’ responses to dynamic changes in the environment are corrupted by inherent stochasticity in intracellular signaling networks. The mutual information (or its maximum, the channel capacity) quantifies the fidelity of cellular signaling. However, current approaches to estimate channel capacity average over the extensive heterogeneity in cell state variables that typically characterizes cell populations. To explicitly account for cell-to-cell differences in quantifying signaling network fidelity, we develop a novel information theoretic framework, cell-state conditioned mutual information (CeeMI). CeeMI models individual cells in a population as unique information channels and estimates the average mutual information between the input and the output as an average over cell state variables. We estimate CeeMI for two signaling pathways and show that it is significantly larger than traditional estimates. Using the IGFR/FoxO pathway, we verify that that individual cells can differentiate between multiple levels of environmental stimuli. Importantly, our approach allows us to identify intracellular biochemical parameters that make some cells good and others bad and sensing their environment. We believe that our new framework will significantly improve our understanding of how cells detect changes in their environment.