Predicting chemical environments of bacteria from receptor signaling
Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistic...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
October 23, 2014
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| In: |
PLoS Computational Biology
Year: 2014, Volume: 10, Issue: 10 |
| ISSN: | 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1003870 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1371/journal.pcbi.1003870 Verlag, lizenzpflichtig, Volltext: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003870 
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| Author Notes: | Diana Clausznitzer, Gabriele Micali, Silke Neumann, Victor Sourjik, Robert G. Endres |
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| 520 | |a Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistics of the stimuli. Based on dose-response curves from in vivo fluorescence resonance energy transfer (FRET) experiments of the bacterial chemotaxis sensory system, we predict the chemical gradients chemotactic Escherichia coli cells typically encounter in their natural environment. To predict average gradients cells experience, we revaluate the phenomenological Weber's law and its generalizations to the Weber-Fechner law and fold-change detection. To obtain full distributions of gradients we use information theory and simulations, considering limitations of information transmission from both cell-external and internal noise. We identify broad distributions of exponential gradients, which lead to log-normal stimuli and maximal drift velocity. Our results thus provide a first step towards deciphering the chemical nature of complex, experimentally inaccessible cellular microenvironments, such as the human intestine. | ||
| 650 | 4 | |a Chemotaxis | |
| 650 | 4 | |a Dose prediction methods | |
| 650 | 4 | |a Fluorescence resonance energy transfer | |
| 650 | 4 | |a Gaussian noise | |
| 650 | 4 | |a Information theory | |
| 650 | 4 | |a Sensory receptors | |
| 650 | 4 | |a Sensory systems | |
| 650 | 4 | |a Swimming | |
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