Automated matching of two‐time X‐ray photon correlation maps from phase‐separating proteins with Cahn–Hilliard‐type simulations using auto‐encoder networks
Machine learning methods are used for an automated classification of experimental two‐time X‐ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard‐type simulations of liqu...
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| Veröffentlicht in: | Journal of applied crystallography 2022-08, Vol.55 (4), p.751-757 |
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| container_title | Journal of applied crystallography |
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| creator | Timmermann, Sonja Starostin, Vladimir Girelli, Anita Ragulskaya, Anastasia Rahmann, Hendrik Reiser, Mario Begam, Nafisa Randolph, Lisa Sprung, Michael Westermeier, Fabian Zhang, Fajun Schreiber, Frank Gutt, Christian |
| description | Machine learning methods are used for an automated classification of experimental two‐time X‐ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard‐type simulations of liquid–liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto‐encoder network and a differential evolution based algorithm. The method presented here is a first step towards handling large amounts of dynamic data measured at high‐brilliance synchrotron and X‐ray free‐electron laser sources, facilitating fast comparison with phase field models of phase separation.
Two‐time correlation maps are classified in a simulation framework using an auto‐encoder network. |
| doi_str_mv | 10.1107/S1600576722004435 |
| format | Article |
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Two‐time correlation maps are classified in a simulation framework using an auto‐encoder network.</description><identifier>ISSN: 1600-5767</identifier><identifier>ISSN: 0021-8898</identifier><identifier>EISSN: 1600-5767</identifier><identifier>DOI: 10.1107/S1600576722004435</identifier><identifier>PMID: 35974741</identifier><language>eng</language><publisher>5 Abbey Square, Chester, Cheshire CH1 2HU, England: International Union of Crystallography</publisher><subject>auto‐encoders ; Cahn–Hilliard ; Coders ; Correlation ; Evolutionary algorithms ; Evolutionary computation ; Liquid phases ; Machine learning ; Matching ; Phase separation ; Photons ; protein dynamics ; Proteins ; Research Papers ; Simulation ; Synchrotrons ; XPCS ; X‐ray photon correlation spectroscopy</subject><ispartof>Journal of applied crystallography, 2022-08, Vol.55 (4), p.751-757</ispartof><rights>2022 Sonja Timmermann et al. published by IUCr Journals.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Sonja Timmermann et al. 2022 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3698-bc6c043ab355a49cf120904361fa2b9b535392257ccc3b9051274138972a24453</citedby><cites>FETCH-LOGICAL-c3698-bc6c043ab355a49cf120904361fa2b9b535392257ccc3b9051274138972a24453</cites><orcidid>0000-0003-4533-6256 ; 0000-0001-7639-8594 ; 0000-0003-3659-6718 ; 0000-0002-0051-8542 ; 0000-0003-0160-9478 ; 0000-0002-3872-2450 ; 0000-0002-5391-8080 ; 0000-0002-1426-7182 ; 0000-0002-5428-9044 ; 0000-0003-0696-206X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1107%2FS1600576722004435$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1107%2FS1600576722004435$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Timmermann, Sonja</creatorcontrib><creatorcontrib>Starostin, Vladimir</creatorcontrib><creatorcontrib>Girelli, Anita</creatorcontrib><creatorcontrib>Ragulskaya, Anastasia</creatorcontrib><creatorcontrib>Rahmann, Hendrik</creatorcontrib><creatorcontrib>Reiser, Mario</creatorcontrib><creatorcontrib>Begam, Nafisa</creatorcontrib><creatorcontrib>Randolph, Lisa</creatorcontrib><creatorcontrib>Sprung, Michael</creatorcontrib><creatorcontrib>Westermeier, Fabian</creatorcontrib><creatorcontrib>Zhang, Fajun</creatorcontrib><creatorcontrib>Schreiber, Frank</creatorcontrib><creatorcontrib>Gutt, Christian</creatorcontrib><title>Automated matching of two‐time X‐ray photon correlation maps from phase‐separating proteins with Cahn–Hilliard‐type simulations using auto‐encoder networks</title><title>Journal of applied crystallography</title><description>Machine learning methods are used for an automated classification of experimental two‐time X‐ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard‐type simulations of liquid–liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto‐encoder network and a differential evolution based algorithm. The method presented here is a first step towards handling large amounts of dynamic data measured at high‐brilliance synchrotron and X‐ray free‐electron laser sources, facilitating fast comparison with phase field models of phase separation.
Two‐time correlation maps are classified in a simulation framework using an auto‐encoder network.</description><subject>auto‐encoders</subject><subject>Cahn–Hilliard</subject><subject>Coders</subject><subject>Correlation</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Liquid phases</subject><subject>Machine learning</subject><subject>Matching</subject><subject>Phase separation</subject><subject>Photons</subject><subject>protein dynamics</subject><subject>Proteins</subject><subject>Research Papers</subject><subject>Simulation</subject><subject>Synchrotrons</subject><subject>XPCS</subject><subject>X‐ray photon correlation spectroscopy</subject><issn>1600-5767</issn><issn>0021-8898</issn><issn>1600-5767</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNqFUsuKFDEUDaI4D_0AdwE3bnrMs6qyEYZGbWVA8IW7kEqnpjJWJWWSsundfILgR_hf8yXeogfxsXCTXO55cG5yEXpEyRmlpH76jlaEyLqqGSNECC7voOOltVp6d3-rj9BJzleE0IV6Hx1xqWpRC3qMfpzPJY6muC2G0_Y-XOLY4bKLN9ffih8d_gRFMns89bHEgG1MyQ2meKhHM2XcpTgCaLIDYnaTSQCCy5RicT5kvPOlx2vTh5vr7xs_DN6k7WK-nxzOfpwPZhnPeZEZyAOoCzZuXcLBQZT0OT9A9zozZPfw9j5FH148f7_erC7evHy1Pr9YWV6pZtXayhLBTculNELZjjKioFHRzrBWtZJLrhiTtbWWt4pIyuAZeKNqZpgQkp-iZwffaW5Ht7UulGQGPSU_mrTX0Xj9JxJ8ry_jV624aJqGgMGTW4MUv8wuFz36bN0wmODinDWrCRcUmBVQH_9FvYpzCjCeZhV8UAWBFhY9sGyKOSfX_QpDiV7WQP-zBqBRB83OD27_f4F-vX7LPm4klQ3_CbDqvVQ</recordid><startdate>202208</startdate><enddate>202208</enddate><creator>Timmermann, Sonja</creator><creator>Starostin, Vladimir</creator><creator>Girelli, Anita</creator><creator>Ragulskaya, Anastasia</creator><creator>Rahmann, Hendrik</creator><creator>Reiser, Mario</creator><creator>Begam, Nafisa</creator><creator>Randolph, Lisa</creator><creator>Sprung, Michael</creator><creator>Westermeier, Fabian</creator><creator>Zhang, Fajun</creator><creator>Schreiber, Frank</creator><creator>Gutt, Christian</creator><general>International Union of Crystallography</general><general>Blackwell Publishing Ltd</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>L7M</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4533-6256</orcidid><orcidid>https://orcid.org/0000-0001-7639-8594</orcidid><orcidid>https://orcid.org/0000-0003-3659-6718</orcidid><orcidid>https://orcid.org/0000-0002-0051-8542</orcidid><orcidid>https://orcid.org/0000-0003-0160-9478</orcidid><orcidid>https://orcid.org/0000-0002-3872-2450</orcidid><orcidid>https://orcid.org/0000-0002-5391-8080</orcidid><orcidid>https://orcid.org/0000-0002-1426-7182</orcidid><orcidid>https://orcid.org/0000-0002-5428-9044</orcidid><orcidid>https://orcid.org/0000-0003-0696-206X</orcidid></search><sort><creationdate>202208</creationdate><title>Automated matching of two‐time X‐ray photon correlation maps from phase‐separating proteins with Cahn–Hilliard‐type simulations using auto‐encoder networks</title><author>Timmermann, Sonja ; Starostin, Vladimir ; Girelli, Anita ; Ragulskaya, Anastasia ; Rahmann, Hendrik ; Reiser, Mario ; Begam, Nafisa ; Randolph, Lisa ; Sprung, Michael ; Westermeier, Fabian ; Zhang, Fajun ; Schreiber, Frank ; Gutt, Christian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3698-bc6c043ab355a49cf120904361fa2b9b535392257ccc3b9051274138972a24453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>auto‐encoders</topic><topic>Cahn–Hilliard</topic><topic>Coders</topic><topic>Correlation</topic><topic>Evolutionary algorithms</topic><topic>Evolutionary computation</topic><topic>Liquid phases</topic><topic>Machine learning</topic><topic>Matching</topic><topic>Phase separation</topic><topic>Photons</topic><topic>protein dynamics</topic><topic>Proteins</topic><topic>Research Papers</topic><topic>Simulation</topic><topic>Synchrotrons</topic><topic>XPCS</topic><topic>X‐ray photon correlation spectroscopy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Timmermann, Sonja</creatorcontrib><creatorcontrib>Starostin, Vladimir</creatorcontrib><creatorcontrib>Girelli, Anita</creatorcontrib><creatorcontrib>Ragulskaya, Anastasia</creatorcontrib><creatorcontrib>Rahmann, Hendrik</creatorcontrib><creatorcontrib>Reiser, Mario</creatorcontrib><creatorcontrib>Begam, Nafisa</creatorcontrib><creatorcontrib>Randolph, Lisa</creatorcontrib><creatorcontrib>Sprung, Michael</creatorcontrib><creatorcontrib>Westermeier, Fabian</creatorcontrib><creatorcontrib>Zhang, Fajun</creatorcontrib><creatorcontrib>Schreiber, Frank</creatorcontrib><creatorcontrib>Gutt, Christian</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of applied crystallography</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Timmermann, Sonja</au><au>Starostin, Vladimir</au><au>Girelli, Anita</au><au>Ragulskaya, Anastasia</au><au>Rahmann, Hendrik</au><au>Reiser, Mario</au><au>Begam, Nafisa</au><au>Randolph, Lisa</au><au>Sprung, Michael</au><au>Westermeier, Fabian</au><au>Zhang, Fajun</au><au>Schreiber, Frank</au><au>Gutt, Christian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated matching of two‐time X‐ray photon correlation maps from phase‐separating proteins with Cahn–Hilliard‐type simulations using auto‐encoder networks</atitle><jtitle>Journal of applied crystallography</jtitle><date>2022-08</date><risdate>2022</risdate><volume>55</volume><issue>4</issue><spage>751</spage><epage>757</epage><pages>751-757</pages><issn>1600-5767</issn><issn>0021-8898</issn><eissn>1600-5767</eissn><abstract>Machine learning methods are used for an automated classification of experimental two‐time X‐ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard‐type simulations of liquid–liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto‐encoder network and a differential evolution based algorithm. The method presented here is a first step towards handling large amounts of dynamic data measured at high‐brilliance synchrotron and X‐ray free‐electron laser sources, facilitating fast comparison with phase field models of phase separation.
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| source | Access via Wiley Online Library; Alma/SFX Local Collection |
| subjects | auto‐encoders Cahn–Hilliard Coders Correlation Evolutionary algorithms Evolutionary computation Liquid phases Machine learning Matching Phase separation Photons protein dynamics Proteins Research Papers Simulation Synchrotrons XPCS X‐ray photon correlation spectroscopy |
| title | Automated matching of two‐time X‐ray photon correlation maps from phase‐separating proteins with Cahn–Hilliard‐type simulations using auto‐encoder networks |
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