A mathematical model explains saturating axon guidance responses to molecular gradients
Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molec...
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description | Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most accurate predictive model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo. |
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Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most accurate predictive model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.</description><identifier>ISSN: 2050-084X</identifier><identifier>EISSN: 2050-084X</identifier><identifier>DOI: 10.7554/eLife.12248</identifier><identifier>PMID: 26830461</identifier><language>eng</language><publisher>England: eLife Science Publications, Ltd</publisher><subject>Animal behavior ; Animals ; Axon guidance ; Axons ; Axons - drug effects ; Axons - physiology ; Cells, Cultured ; Chemotaxis ; Growth cones ; Growth Cones - drug effects ; Growth Cones - physiology ; Invertebrates ; Lab-On-A-Chip Devices ; Mathematical models ; Microfluidics ; Models, Theoretical ; Nervous system ; Neural circuitry ; Neuroscience ; Noise ; Physiological aspects ; Rats, Wistar ; Stochasticity</subject><ispartof>eLife, 2016-02, Vol.5, p.e12248-e12248</ispartof><rights>COPYRIGHT 2016 eLife Science Publications, Ltd.</rights><rights>2016, Nguyen et al. This work is licensed under the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/3.0/ ) (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016, Nguyen et al 2016 Nguyen et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-9789-9355 ; 0000-0003-3476-1839 ; 0000-0002-5775-7915</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4755759/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4755759/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26830461$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nguyen, Huyen</creatorcontrib><creatorcontrib>Dayan, Peter</creatorcontrib><creatorcontrib>Pujic, Zac</creatorcontrib><creatorcontrib>Cooper-White, Justin</creatorcontrib><creatorcontrib>Goodhill, Geoffrey J</creatorcontrib><title>A mathematical model explains saturating axon guidance responses to molecular gradients</title><title>eLife</title><addtitle>Elife</addtitle><description>Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most accurate predictive model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.</description><subject>Animal behavior</subject><subject>Animals</subject><subject>Axon guidance</subject><subject>Axons</subject><subject>Axons - drug effects</subject><subject>Axons - physiology</subject><subject>Cells, Cultured</subject><subject>Chemotaxis</subject><subject>Growth cones</subject><subject>Growth Cones - drug effects</subject><subject>Growth Cones - physiology</subject><subject>Invertebrates</subject><subject>Lab-On-A-Chip Devices</subject><subject>Mathematical models</subject><subject>Microfluidics</subject><subject>Models, Theoretical</subject><subject>Nervous system</subject><subject>Neural circuitry</subject><subject>Neuroscience</subject><subject>Noise</subject><subject>Physiological aspects</subject><subject>Rats, Wistar</subject><subject>Stochasticity</subject><issn>2050-084X</issn><issn>2050-084X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkd1rFDEUxQdRbKl98l0CvujDbmeSm68XYSl-FBYKVdG3kGbuTFMyyZrMyPrfm2LVrphAEnJ_51xy0jTPu3YtOYcz3PoB1x2loB41x7Tl7apV8PXxg_NRc1rKbVuHBKU6_bQ5okKxFkR33HzZkMnON1gX72wgU-oxENzvgvWxkGLnJddSHIndp0jGxfc2OiQZyy7FgoXMqYoCuiXYTMZse49xLs-aJ4MNBU_v95Pm87u3n84_rLaX7y_ON9vVyDSfV53iPQjLOROaDYJK6TQFAAXXFEE7CcA65hwXmnKJ0LeUa6wYKtFrR9lJ8-aX7265nrB3tXe2weyyn2z-YZL15rAS_Y0Z03cDNT7JdTV4dW-Q07cFy2wmXxyGYCOmpZhOCsqAspZV9OU_6G1acqzPM53mjMuOCf6XGm1A4-OQal93Z2o2IBkw0FJVav0fqs4eJ-9SxMHX-wPB6wNBZWbcz6NdSjEXH68O2RcPQ_mTxu9vZz8BtjKtTQ</recordid><startdate>20160202</startdate><enddate>20160202</enddate><creator>Nguyen, Huyen</creator><creator>Dayan, Peter</creator><creator>Pujic, Zac</creator><creator>Cooper-White, Justin</creator><creator>Goodhill, Geoffrey J</creator><general>eLife Science Publications, Ltd</general><general>eLife Sciences Publications Ltd</general><general>eLife Sciences Publications, Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>ISR</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9789-9355</orcidid><orcidid>https://orcid.org/0000-0003-3476-1839</orcidid><orcidid>https://orcid.org/0000-0002-5775-7915</orcidid></search><sort><creationdate>20160202</creationdate><title>A mathematical model explains saturating axon guidance responses to molecular gradients</title><author>Nguyen, Huyen ; 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Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. 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subjects | Animal behavior Animals Axon guidance Axons Axons - drug effects Axons - physiology Cells, Cultured Chemotaxis Growth cones Growth Cones - drug effects Growth Cones - physiology Invertebrates Lab-On-A-Chip Devices Mathematical models Microfluidics Models, Theoretical Nervous system Neural circuitry Neuroscience Noise Physiological aspects Rats, Wistar Stochasticity |
title | A mathematical model explains saturating axon guidance responses to molecular gradients |
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