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Single-cell studies often show significant phenotypic variability due to thestochastic nature of intra-cellular biochemical reactions. When the numbers ofmolecules, e.g., transcription factors and regulatory enzymes, are in lowabundance, fluctuations in biochemical activities become significant and such"noise" can propagate through regulatory cascades in terms of biochemicalreaction networks. Here we develop an intuitive, yet fully quantitative methodfor analyzing how noise affects cellular phenotypes based on identifying asystem's nonlinearities and noise propagations. We observe that such noise cansimultaneously enhance sensitivities in one behavioral region while reducingsensitivities in another. Employing this novel phenomenon we designed threebiochemical signal processing modules: (a) A gene regulatory network that actsas a concentration detector with both enhanced amplitude and sensitivity. (b) Anon-cooperative positive feedback system, with a graded dose-response in thedeterministic case, that serves as a bistable switch due to noise-inducedbimodality. (c) A noise-induced linear amplifier for gene regulation thatrequires no feedback. The methods developed in the present work allow one tounderstand and engineer nonlinear biochemical signal processors based onfluctuation-induced phenotypes.

Nonlinear Biochemical Signal Processing via Noise Propagation