A Bayesian model predicts the response of axons to molecular gradients Export

@article{Mortimer2009Bayesian, abstract = {10.1073/pnas.0900715106 Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian ” ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings.}, author = {Mortimer, Duncan and Feldner, Julia and Vaughan, Timothy and Vetter, Irina and Pujic, Zac and Rosoff, William J. and Burrage, Kevin and Dayan, Peter and Richards, Linda J. and Goodhill, Geoffrey J.}, citeulike-article-id = {5220471}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0900715106}, citeulike-linkout-1 = {http://www.pnas.org/content/106/25/10296.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/106/25/10296.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19541606}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19541606}, day = {23}, doi = {10.1073/pnas.0900715106}, journal = {Proceedings of the National Academy of Sciences}, keywords = {bayesian, fun, gradient}, month = {June}, number = {25}, pages = {10296--10301}, posted-at = {2009-07-21 20:08:39}, priority = {2}, title = {A Bayesian model predicts the response of axons to molecular gradients}, url = {http://dx.doi.org/10.1073/pnas.0900715106}, volume = {106}, year = {2009} }

TY - JOUR ID - Mortimer2009Bayesian L3 - citeulike-article-id:5220471 N2 - 10.1073/pnas.0900715106 Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian “ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings. IS - 25 JF - Proceedings of the National Academy of Sciences EP - 10301 TI - A Bayesian model predicts the response of axons to molecular gradients VL - 106 SP - 10296 KW - bayesian KW - fun KW - gradient AU - Mortimer, Duncan AU - Feldner, Julia AU - Vaughan, Timothy AU - Vetter, Irina AU - Pujic, Zac AU - Rosoff, William AU - Burrage, Kevin AU - Dayan, Peter AU - Richards, Linda AU - Goodhill, Geoffrey PY - 2009/06/23/ UR - http://dx.doi.org/10.1073/pnas.0900715106 ER -