Receptor arrays optimized for natural odor statistics.
Natural odors typically consist of many molecules at different concentrations. It is unclear how the numerous odorant molecules and their possible mixtures are discriminated by relatively few olfactory receptors. Using an information theoretic model, we show that a receptor array is optimal for this task if it achieves two possibly conflicting goals: (i) Each receptor should respond to half of all odors and (ii) the response of different receptors should be uncorrelated when averaged over odors presented with natural statistics. We use these design principles to predict statistics of the affinities between receptors and odorant molecules for a broad class of odor statistics. We also show that optimal receptor arrays can be tuned to either resolve concentrations well or distinguish mixtures reliably. Finally, we use our results to predict properties of experimentally measured receptor arrays. Our work can thus be used to better understand natural olfaction, and it also suggests ways to improve artificial sensor arrays.
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138; Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA 02138;
David
D
Zwicker
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138; Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA 02138; Department of Physics, University of Chicago, Chicago, IL 60637; James Franck Institute, University of Chicago, Chicago, IL 60637 brenner@seas.harvard.edu amurugan@uchicago.edu.
Arvind
A
Murugan
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138; Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA 02138; brenner@seas.harvard.edu amurugan@uchicago.edu.
Michael P
MP
Brenner
D001699:Biometry; D006801:Humans; D007257:Information Theory; D009812:Odorants; D018035:Receptors, Odorant / Q000502:physiology*; D012903:Smell
D018035:Receptors, Odorant
information theory; molecular recognition; natural statistics; olfaction; sensing