Information flow in a mammalian signal transduction pathway

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The mammalian signal transduction network relays detailed information about the presence and concentration of ligands on the outside of the cell to the nucleus, and alters cellular behavior by changing gene expression. Since signal transduction pathways exhibit striking similarities to typical communication systems, the framework of information theory can be directly applied to better understand cellular signaling. During the current funding period of the priority program InKoMBio, we determined the information transmission capacities of the prototypic MAPK pathway using a combination of single cell experimentation and information theoretical calculations. Surprisingly, our results indicate that the signaling network transmits less than one bit of information. Rather than faithfully reporting extracellular concentrations of the ligand EGF, it responds in a binary manner. In addition, molecular noise interferes with a robust encoding of the presence of the input signal, limiting the information content even further. We observed similarly limited channel capacities for two other signaling pathways, the TGFbeta/SMAD and p53 networks.As many studies in different biological model systems suggest that cells can gain more information than one bit about their environment using signaling pathways, we aim to investigate what is limiting the information transmission capabilities at the single cell level and how cells maximize the amount of information gained from external and internal sources to ensure a proper physiological response. We hypothesize that the pathways integrate information from the cellular context, which could explain the apparently low channel capacity. We therefore propose to use information theory, single cell experimentation and mathematical modeling to study the influence of contextual information, by addressing the following specific questions: (i) how does the state of a cell influence the response to an external signal, (ii) how does the context of previous stimuli influence the response and (iii) what are common principles of context-dependent signaling across different pathways? We will use live-cell imaging and immunofluorescence assays to measure signaling and context, and calculate the contribution of contextual information using conditional mutual information, context trees and parsimonious Bayesian networks. To gain a predictive understanding of the underlying molecular mechanisms, we will expand existing mathematical models of the pathways to include the interacting regulatory processes that provide context and analyze their information theoretical properties. Using network perturbations, we will experimentally validate model predictions.