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Decoding Complex Chemical Mixtures with a Physical Model of a Sensor Array

Author(s): Tsitron, Julia; Ault, Addison D; Broach, James R; Morozov, Alexandre V

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Abstract: Combinatorial sensor arrays, such as the olfactory system, can detect a large number of analytes using a relatively small number of receptors. However, the complex pattern of receptor responses to even a single analyte, coupled with the nonlinearity of responses to mixtures of analytes, makes quantitative prediction of compound concentrations in a mixture a challenging task. Here we develop a physical model that explicitly takes receptor-ligand interactions into account, and apply it to infer concentrations of highly related sugar nucleotides from the output of four engineered G-protein-coupled receptors. We also derive design principles that enable accurate mixture discrimination with cross-specific sensor arrays. The optimal sensor parameters exhibit relatively weak dependence on component concentrations, making a single designed array useful for analyzing a sizable range of mixtures. The maximum number of mixture components that can be successfully discriminated is twice the number of sensors in the array. Finally, antagonistic receptor responses, well-known to play an important role in natural olfactory systems, prove to be essential for the accurate prediction of component concentrations.
Publication Date: 20-Oct-2011
Electronic Publication Date: 20-Oct-2011
Citation: Tsitron, Julia, Ault, Addison D, Broach, James R, Morozov, Alexandre V. (2011). Decoding Complex Chemical Mixtures with a Physical Model of a Sensor Array. PLoS Computational Biology, 7 (10), e1002224 - e1002224. doi:10.1371/journal.pcbi.1002224
DOI: doi:10.1371/journal.pcbi.1002224
EISSN: 1553-7358
Pages: 1 - 13
Type of Material: Journal Article
Journal/Proceeding Title: PLoS Computational Biology
Version: Final published version. This is an open access article.

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