Discrete and Continuum Approximations for Collective Cell Migration in a Scratch Assay with Cell Size Dynamics
Scratch assays are routinely used to study the collective spreading of cell populations. In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell pro...
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description | Scratch assays are routinely used to study the collective spreading of cell populations. In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell proliferation, scratch assays are often performed after treating the cells with a drug that inhibits proliferation. Mitomycin-C is a drug that is commonly used to suppress cell proliferation in this context. However, in addition to suppressing cell proliferation, mitomycin-C also causes cells to change size during the experiment, as each cell in the population approximately doubles in size as a result of treatment. Therefore, to describe a scratch assay that incorporates the effects of cell-to-cell crowding, cell-to-cell adhesion, and dynamic changes in cell size, we present a new stochastic model that incorporates these mechanisms. Our agent-based stochastic model takes the form of a system of Langevin equations that is the system of stochastic differential equations governing the evolution of the population of agents. We incorporate a time-dependent interaction force that is used to mimic the dynamic increase in size of the agents. To provide a mathematical description of the average behaviour of the stochastic model we present continuum limit descriptions using both a standard mean-field approximation and a more sophisticated moment dynamics approximation that accounts for the density of agents and density of pairs of agents in the stochastic model. Comparing the accuracy of the two continuum descriptions for a typical scratch assay geometry shows that the incorporation of agent growth in the system is associated with a decrease in accuracy of the standard mean-field description. In contrast, the moment dynamics description provides a more accurate prediction of the evolution of the scratch assay when the increase in size of individual agents is included in the model. |
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In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell proliferation, scratch assays are often performed after treating the cells with a drug that inhibits proliferation. Mitomycin-C is a drug that is commonly used to suppress cell proliferation in this context. However, in addition to suppressing cell proliferation, mitomycin-C also causes cells to change size during the experiment, as each cell in the population approximately doubles in size as a result of treatment. Therefore, to describe a scratch assay that incorporates the effects of cell-to-cell crowding, cell-to-cell adhesion, and dynamic changes in cell size, we present a new stochastic model that incorporates these mechanisms. Our agent-based stochastic model takes the form of a system of Langevin equations that is the system of stochastic differential equations governing the evolution of the population of agents. We incorporate a time-dependent interaction force that is used to mimic the dynamic increase in size of the agents. To provide a mathematical description of the average behaviour of the stochastic model we present continuum limit descriptions using both a standard mean-field approximation and a more sophisticated moment dynamics approximation that accounts for the density of agents and density of pairs of agents in the stochastic model. Comparing the accuracy of the two continuum descriptions for a typical scratch assay geometry shows that the incorporation of agent growth in the system is associated with a decrease in accuracy of the standard mean-field description. In contrast, the moment dynamics description provides a more accurate prediction of the evolution of the scratch assay when the increase in size of individual agents is included in the model.</description><identifier>ISSN: 0092-8240</identifier><identifier>EISSN: 1522-9602</identifier><identifier>DOI: 10.1007/s11538-018-0398-2</identifier><identifier>PMID: 29372496</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Approximation ; Assaying ; Cell Adhesion ; Cell adhesion & migration ; Cell Biology ; Cell growth ; Cell migration ; Cell Migration Assays - methods ; Cell Movement ; Cell proliferation ; Cell Proliferation - drug effects ; Cell Size ; Descriptions ; Differential equations ; Evolution ; Humans ; Life Sciences ; Male ; Mathematical analysis ; Mathematical and Computational Biology ; Mathematical Concepts ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Mitomycin - pharmacology ; Mitomycin C ; Model accuracy ; Models, Biological ; Original Article ; PC-3 Cells ; Stochastic models ; Stochastic Processes ; Stochasticity</subject><ispartof>Bulletin of mathematical biology, 2018-04, Vol.80 (4), p.738-757</ispartof><rights>Society for Mathematical Biology 2018</rights><rights>Bulletin of Mathematical Biology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-e4cc08b5410f8b3c9a09675a729a19cd9d045ddc5f65f3b14d76333ffb0b17c73</citedby><cites>FETCH-LOGICAL-c415t-e4cc08b5410f8b3c9a09675a729a19cd9d045ddc5f65f3b14d76333ffb0b17c73</cites><orcidid>0000-0001-6254-313X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11538-018-0398-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11538-018-0398-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29372496$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Matsiaka, Oleksii M.</creatorcontrib><creatorcontrib>Penington, Catherine J</creatorcontrib><creatorcontrib>Baker, Ruth E.</creatorcontrib><creatorcontrib>Simpson, Matthew J.</creatorcontrib><title>Discrete and Continuum Approximations for Collective Cell Migration in a Scratch Assay with Cell Size Dynamics</title><title>Bulletin of mathematical biology</title><addtitle>Bull Math Biol</addtitle><addtitle>Bull Math Biol</addtitle><description>Scratch assays are routinely used to study the collective spreading of cell populations. In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell proliferation, scratch assays are often performed after treating the cells with a drug that inhibits proliferation. Mitomycin-C is a drug that is commonly used to suppress cell proliferation in this context. However, in addition to suppressing cell proliferation, mitomycin-C also causes cells to change size during the experiment, as each cell in the population approximately doubles in size as a result of treatment. Therefore, to describe a scratch assay that incorporates the effects of cell-to-cell crowding, cell-to-cell adhesion, and dynamic changes in cell size, we present a new stochastic model that incorporates these mechanisms. Our agent-based stochastic model takes the form of a system of Langevin equations that is the system of stochastic differential equations governing the evolution of the population of agents. We incorporate a time-dependent interaction force that is used to mimic the dynamic increase in size of the agents. To provide a mathematical description of the average behaviour of the stochastic model we present continuum limit descriptions using both a standard mean-field approximation and a more sophisticated moment dynamics approximation that accounts for the density of agents and density of pairs of agents in the stochastic model. Comparing the accuracy of the two continuum descriptions for a typical scratch assay geometry shows that the incorporation of agent growth in the system is associated with a decrease in accuracy of the standard mean-field description. In contrast, the moment dynamics description provides a more accurate prediction of the evolution of the scratch assay when the increase in size of individual agents is included in the model.</description><subject>Approximation</subject><subject>Assaying</subject><subject>Cell Adhesion</subject><subject>Cell adhesion & migration</subject><subject>Cell Biology</subject><subject>Cell growth</subject><subject>Cell migration</subject><subject>Cell Migration Assays - methods</subject><subject>Cell Movement</subject><subject>Cell proliferation</subject><subject>Cell Proliferation - drug effects</subject><subject>Cell Size</subject><subject>Descriptions</subject><subject>Differential equations</subject><subject>Evolution</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Male</subject><subject>Mathematical analysis</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical Concepts</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Mitomycin - pharmacology</subject><subject>Mitomycin C</subject><subject>Model accuracy</subject><subject>Models, Biological</subject><subject>Original Article</subject><subject>PC-3 Cells</subject><subject>Stochastic models</subject><subject>Stochastic Processes</subject><subject>Stochasticity</subject><issn>0092-8240</issn><issn>1522-9602</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</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>eNp1kctOwzAQRS0EoqXwAWyQJTZsAn7ESbysWl5SEYvC2nIcp3WVOMVOgPL1uKQghMTCsqx77nhmLgCnGF1ihNIrjzGjWYRwOJRnEdkDQ8wIiXiCyD4YIsRJlJEYDcCR9ysUPJzyQzAgnKYk5skQ2KnxyulWQ2kLOGlsa2zX1XC8Xrvm3dSyNY31sGxcEKtKq9a8ajjRVQUfzMJ9ydBYKOFchZdawrH3cgPfTLvssbn50HC6sbI2yh-Dg1JWXp_s7hF4vrl-mtxFs8fb-8l4FqkYszbSsVIoy1mMUZnlVHGJeJIymRIuMVcFL1DMikKxMmElzXFcpAmltCxzlONUpXQELvq6YYqXTvtW1GHO0I60uum8wJwThAllW_T8D7pqOmdDd1uKxixNGA8U7inlGu-dLsXahe24jcBIbMMQfRgihCG2YQgSPGe7yl1e6-LH8b39AJAe8EGyC-1-ff1v1U8G1ZSB</recordid><startdate>20180401</startdate><enddate>20180401</enddate><creator>Matsiaka, Oleksii M.</creator><creator>Penington, Catherine J</creator><creator>Baker, Ruth E.</creator><creator>Simpson, Matthew J.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SS</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6254-313X</orcidid></search><sort><creationdate>20180401</creationdate><title>Discrete and Continuum Approximations for Collective Cell Migration in a Scratch Assay with Cell Size Dynamics</title><author>Matsiaka, Oleksii M. ; Penington, Catherine J ; Baker, Ruth E. ; Simpson, Matthew J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-e4cc08b5410f8b3c9a09675a729a19cd9d045ddc5f65f3b14d76333ffb0b17c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Approximation</topic><topic>Assaying</topic><topic>Cell Adhesion</topic><topic>Cell adhesion & migration</topic><topic>Cell Biology</topic><topic>Cell growth</topic><topic>Cell migration</topic><topic>Cell Migration Assays - methods</topic><topic>Cell Movement</topic><topic>Cell proliferation</topic><topic>Cell Proliferation - drug effects</topic><topic>Cell Size</topic><topic>Descriptions</topic><topic>Differential equations</topic><topic>Evolution</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Male</topic><topic>Mathematical analysis</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical Concepts</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Mitomycin - pharmacology</topic><topic>Mitomycin C</topic><topic>Model accuracy</topic><topic>Models, Biological</topic><topic>Original Article</topic><topic>PC-3 Cells</topic><topic>Stochastic models</topic><topic>Stochastic Processes</topic><topic>Stochasticity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Matsiaka, Oleksii M.</creatorcontrib><creatorcontrib>Penington, Catherine J</creatorcontrib><creatorcontrib>Baker, Ruth E.</creatorcontrib><creatorcontrib>Simpson, Matthew J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Bulletin of mathematical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Matsiaka, Oleksii M.</au><au>Penington, Catherine J</au><au>Baker, Ruth E.</au><au>Simpson, Matthew J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Discrete and Continuum Approximations for Collective Cell Migration in a Scratch Assay with Cell Size Dynamics</atitle><jtitle>Bulletin of mathematical biology</jtitle><stitle>Bull Math Biol</stitle><addtitle>Bull Math Biol</addtitle><date>2018-04-01</date><risdate>2018</risdate><volume>80</volume><issue>4</issue><spage>738</spage><epage>757</epage><pages>738-757</pages><issn>0092-8240</issn><eissn>1522-9602</eissn><abstract>Scratch assays are routinely used to study the collective spreading of cell populations. In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell proliferation, scratch assays are often performed after treating the cells with a drug that inhibits proliferation. Mitomycin-C is a drug that is commonly used to suppress cell proliferation in this context. However, in addition to suppressing cell proliferation, mitomycin-C also causes cells to change size during the experiment, as each cell in the population approximately doubles in size as a result of treatment. Therefore, to describe a scratch assay that incorporates the effects of cell-to-cell crowding, cell-to-cell adhesion, and dynamic changes in cell size, we present a new stochastic model that incorporates these mechanisms. Our agent-based stochastic model takes the form of a system of Langevin equations that is the system of stochastic differential equations governing the evolution of the population of agents. We incorporate a time-dependent interaction force that is used to mimic the dynamic increase in size of the agents. To provide a mathematical description of the average behaviour of the stochastic model we present continuum limit descriptions using both a standard mean-field approximation and a more sophisticated moment dynamics approximation that accounts for the density of agents and density of pairs of agents in the stochastic model. Comparing the accuracy of the two continuum descriptions for a typical scratch assay geometry shows that the incorporation of agent growth in the system is associated with a decrease in accuracy of the standard mean-field description. 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subjects | Approximation Assaying Cell Adhesion Cell adhesion & migration Cell Biology Cell growth Cell migration Cell Migration Assays - methods Cell Movement Cell proliferation Cell Proliferation - drug effects Cell Size Descriptions Differential equations Evolution Humans Life Sciences Male Mathematical analysis Mathematical and Computational Biology Mathematical Concepts Mathematical models Mathematics Mathematics and Statistics Mitomycin - pharmacology Mitomycin C Model accuracy Models, Biological Original Article PC-3 Cells Stochastic models Stochastic Processes Stochasticity |
title | Discrete and Continuum Approximations for Collective Cell Migration in a Scratch Assay with Cell Size Dynamics |
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