Title |
Decoding Complex Chemical Mixtures with a Physical Model of a Sensor Array
|
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Published in |
PLoS Computational Biology, October 2011
|
DOI | 10.1371/journal.pcbi.1002224 |
Pubmed ID | |
Authors |
Julia Tsitron, Addison D. Ault, James R. Broach, Alexandre V. Morozov |
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 non-linearity 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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 4% |
Unknown | 22 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 17% |
Researcher | 4 | 17% |
Professor | 3 | 13% |
Student > Bachelor | 2 | 9% |
Student > Doctoral Student | 2 | 9% |
Other | 5 | 22% |
Unknown | 3 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 7 | 30% |
Engineering | 4 | 17% |
Biochemistry, Genetics and Molecular Biology | 2 | 9% |
Computer Science | 2 | 9% |
Physics and Astronomy | 2 | 9% |
Other | 2 | 9% |
Unknown | 4 | 17% |