Noise and robustness in boolean signaling networks

Living systems are capable of information processing under strongly noisy conditions. In particular, signaling networks in cells integrate and process multiple fluctuating signals, mapping them into a multidimensional output space. Most studies so far have addressed the response of cells to a single input, ignoring the effect of other signals that act upon the same signaling network. However, even when only one input is dominant, those other inputs are still present, if only to provide a form of background noise, or chatter, that is bound to affect the response of the signaling network to the dominant input. We have studied the effect of such background noise in the signaling network of a typical human cell. Specifically, we have used a boolean model of a signal transduction network involving over 130 protein species. We observe that noise intensity determines the information paths. We have compared the results obtained with the biological network to results obtained from randomized networks, finding that the biological network exhibits a unique balance between robustness and responsiveness.

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