A gene expression-based mathematical modeling approach for breast cancer tumor growth and shrinkage
In this paper, we introduce a personalized parameterization approach, namely pr e p-g , to explore impact of gene expression values from breast cancer patients on tumor growth and shrinkage characteristics using xenograft models. In construction of pr e p-g parameterization, in addition to individua...
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| Veröffentlicht in: | Network modeling and analysis in health informatics and bioinformatics (Wien) 2015-12, Vol.4 (1), p.28, Article 28 |
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| creator | Saribudak, Aydin Gundry, Stephen Zou, Jianmin Uyar, M. Ümit |
| description | In this paper, we introduce a personalized parameterization approach, namely
pr
e
p-g
, to explore impact of gene expression values from breast cancer patients on tumor growth and shrinkage characteristics using xenograft models. In construction of
pr
e
p-g
parameterization, in addition to individual effects of the breast cancer-related gene expressions, the impact of the correlation among them and the contribution of their multiple orders are considered. Tumor growth behavior, and delay and shrinkage effects of anti-cancer agents are examined in six case studies using xenograft models implanted with breast cancer cell lines. Tumor growth parameters for
er+
cell lines
bt-474
and
mcf-7
, and drug-related shrinkage parameters for cell lines
mda-mb-231
,
mda-mb-468
and
bt-474
under the monotherapy of drugs paclitaxel and doxorubicin are computed. Consistency of the experimental data reported in several studies in literature for multiple breast cancer cell lines in mice models and the computed results from
pr
e
p-g
are encouraging, which indicates that construction of mathematical models for tumor growth and shrinkage by combining gene expressions and clinical information may be feasible. |
| doi_str_mv | 10.1007/s13721-015-0099-9 |
| format | Article |
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pr
e
p-g
, to explore impact of gene expression values from breast cancer patients on tumor growth and shrinkage characteristics using xenograft models. In construction of
pr
e
p-g
parameterization, in addition to individual effects of the breast cancer-related gene expressions, the impact of the correlation among them and the contribution of their multiple orders are considered. Tumor growth behavior, and delay and shrinkage effects of anti-cancer agents are examined in six case studies using xenograft models implanted with breast cancer cell lines. Tumor growth parameters for
er+
cell lines
bt-474
and
mcf-7
, and drug-related shrinkage parameters for cell lines
mda-mb-231
,
mda-mb-468
and
bt-474
under the monotherapy of drugs paclitaxel and doxorubicin are computed. Consistency of the experimental data reported in several studies in literature for multiple breast cancer cell lines in mice models and the computed results from
pr
e
p-g
are encouraging, which indicates that construction of mathematical models for tumor growth and shrinkage by combining gene expressions and clinical information may be feasible.</description><identifier>ISSN: 2192-6662</identifier><identifier>EISSN: 2192-6670</identifier><identifier>DOI: 10.1007/s13721-015-0099-9</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Animal models ; Anticancer properties ; Antitumor agents ; Applications of Graph Theory and Complex Networks ; Bioinformatics ; Breast cancer ; Cancer therapies ; Cell culture ; Chemotherapy ; Computation ; Computational Biology/Bioinformatics ; Computer Science ; Doxorubicin ; Estrogens ; Gene expression ; Growth models ; Health Informatics ; Mathematical models ; Mouse devices ; Original Article ; Paclitaxel ; Parameterization ; Parameters ; Patients ; Pharmacokinetics ; Shrinkage ; Tumor cell lines ; Tumors ; Xenografts ; Xenotransplantation</subject><ispartof>Network modeling and analysis in health informatics and bioinformatics (Wien), 2015-12, Vol.4 (1), p.28, Article 28</ispartof><rights>Springer-Verlag Wien 2015</rights><rights>Springer-Verlag Wien 2015.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-d8598c1e76c025cd6c0bd1f702075200bf43c75f924fdd4cbdc3458dfc13f6d83</citedby><cites>FETCH-LOGICAL-c316t-d8598c1e76c025cd6c0bd1f702075200bf43c75f924fdd4cbdc3458dfc13f6d83</cites><orcidid>0000-0003-0792-1251</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/s13721-015-0099-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919735101?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,21389,27924,27925,33530,33744,41488,42557,43659,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Saribudak, Aydin</creatorcontrib><creatorcontrib>Gundry, Stephen</creatorcontrib><creatorcontrib>Zou, Jianmin</creatorcontrib><creatorcontrib>Uyar, M. Ümit</creatorcontrib><title>A gene expression-based mathematical modeling approach for breast cancer tumor growth and shrinkage</title><title>Network modeling and analysis in health informatics and bioinformatics (Wien)</title><addtitle>Netw Model Anal Health Inform Bioinforma</addtitle><description>In this paper, we introduce a personalized parameterization approach, namely
pr
e
p-g
, to explore impact of gene expression values from breast cancer patients on tumor growth and shrinkage characteristics using xenograft models. In construction of
pr
e
p-g
parameterization, in addition to individual effects of the breast cancer-related gene expressions, the impact of the correlation among them and the contribution of their multiple orders are considered. Tumor growth behavior, and delay and shrinkage effects of anti-cancer agents are examined in six case studies using xenograft models implanted with breast cancer cell lines. Tumor growth parameters for
er+
cell lines
bt-474
and
mcf-7
, and drug-related shrinkage parameters for cell lines
mda-mb-231
,
mda-mb-468
and
bt-474
under the monotherapy of drugs paclitaxel and doxorubicin are computed. Consistency of the experimental data reported in several studies in literature for multiple breast cancer cell lines in mice models and the computed results from
pr
e
p-g
are encouraging, which indicates that construction of mathematical models for tumor growth and shrinkage by combining gene expressions and clinical information may be feasible.</description><subject>Animal models</subject><subject>Anticancer properties</subject><subject>Antitumor agents</subject><subject>Applications of Graph Theory and Complex Networks</subject><subject>Bioinformatics</subject><subject>Breast cancer</subject><subject>Cancer therapies</subject><subject>Cell culture</subject><subject>Chemotherapy</subject><subject>Computation</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computer Science</subject><subject>Doxorubicin</subject><subject>Estrogens</subject><subject>Gene expression</subject><subject>Growth models</subject><subject>Health Informatics</subject><subject>Mathematical models</subject><subject>Mouse devices</subject><subject>Original Article</subject><subject>Paclitaxel</subject><subject>Parameterization</subject><subject>Parameters</subject><subject>Patients</subject><subject>Pharmacokinetics</subject><subject>Shrinkage</subject><subject>Tumor cell lines</subject><subject>Tumors</subject><subject>Xenografts</subject><subject>Xenotransplantation</subject><issn>2192-6662</issn><issn>2192-6670</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UE1LxDAQDaLgsu4P8BbwHM2kTdMcl8UvWPCi55Dmo-26TWvSRf33Rlb05DDMDMN7b4aH0CXQa6BU3CQoBANCgRNKpSTyBC0YSEaqStDT37li52iV0o7mqHMCXyCzxq0LDruPKbqU-jGQRidn8aDnzuXSG73Hw2jdvg8t1tMUR2067MeIm-h0mrHRwbiI58OQd20c3-cO62Bx6mIfXnXrLtCZ1_vkVj99iV7ubp83D2T7dP-4WW-JKaCaia25rA04URnKuLG5NRa8oIwKzihtfFkYwb1kpbe2NI01Rclr6w0UvrJ1sURXR93849vBpVntxkMM-aRiEqQoOFDIKDiiTBxTis6rKfaDjp8KqPq2Ux3tVNlO9W2nkpnDjpyUsaF18U_5f9IXrJJ4vw</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>Saribudak, Aydin</creator><creator>Gundry, Stephen</creator><creator>Zou, Jianmin</creator><creator>Uyar, M. 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Ümit</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-d8598c1e76c025cd6c0bd1f702075200bf43c75f924fdd4cbdc3458dfc13f6d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Animal models</topic><topic>Anticancer properties</topic><topic>Antitumor agents</topic><topic>Applications of Graph Theory and Complex Networks</topic><topic>Bioinformatics</topic><topic>Breast cancer</topic><topic>Cancer therapies</topic><topic>Cell culture</topic><topic>Chemotherapy</topic><topic>Computation</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computer Science</topic><topic>Doxorubicin</topic><topic>Estrogens</topic><topic>Gene expression</topic><topic>Growth models</topic><topic>Health Informatics</topic><topic>Mathematical models</topic><topic>Mouse devices</topic><topic>Original Article</topic><topic>Paclitaxel</topic><topic>Parameterization</topic><topic>Parameters</topic><topic>Patients</topic><topic>Pharmacokinetics</topic><topic>Shrinkage</topic><topic>Tumor cell lines</topic><topic>Tumors</topic><topic>Xenografts</topic><topic>Xenotransplantation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saribudak, Aydin</creatorcontrib><creatorcontrib>Gundry, Stephen</creatorcontrib><creatorcontrib>Zou, Jianmin</creatorcontrib><creatorcontrib>Uyar, M. Ümit</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</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>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 Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science 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><jtitle>Network modeling and analysis in health informatics and bioinformatics (Wien)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saribudak, Aydin</au><au>Gundry, Stephen</au><au>Zou, Jianmin</au><au>Uyar, M. Ümit</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A gene expression-based mathematical modeling approach for breast cancer tumor growth and shrinkage</atitle><jtitle>Network modeling and analysis in health informatics and bioinformatics (Wien)</jtitle><stitle>Netw Model Anal Health Inform Bioinforma</stitle><date>2015-12-01</date><risdate>2015</risdate><volume>4</volume><issue>1</issue><spage>28</spage><pages>28-</pages><artnum>28</artnum><issn>2192-6662</issn><eissn>2192-6670</eissn><abstract>In this paper, we introduce a personalized parameterization approach, namely
pr
e
p-g
, to explore impact of gene expression values from breast cancer patients on tumor growth and shrinkage characteristics using xenograft models. In construction of
pr
e
p-g
parameterization, in addition to individual effects of the breast cancer-related gene expressions, the impact of the correlation among them and the contribution of their multiple orders are considered. Tumor growth behavior, and delay and shrinkage effects of anti-cancer agents are examined in six case studies using xenograft models implanted with breast cancer cell lines. Tumor growth parameters for
er+
cell lines
bt-474
and
mcf-7
, and drug-related shrinkage parameters for cell lines
mda-mb-231
,
mda-mb-468
and
bt-474
under the monotherapy of drugs paclitaxel and doxorubicin are computed. Consistency of the experimental data reported in several studies in literature for multiple breast cancer cell lines in mice models and the computed results from
pr
e
p-g
are encouraging, which indicates that construction of mathematical models for tumor growth and shrinkage by combining gene expressions and clinical information may be feasible.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s13721-015-0099-9</doi><orcidid>https://orcid.org/0000-0003-0792-1251</orcidid></addata></record> |
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| ispartof | Network modeling and analysis in health informatics and bioinformatics (Wien), 2015-12, Vol.4 (1), p.28, Article 28 |
| issn | 2192-6662 2192-6670 |
| language | eng |
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| source | ProQuest Central (Alumni Edition); ProQuest Central UK/Ireland; SpringerLink Journals - AutoHoldings; ProQuest Central |
| subjects | Animal models Anticancer properties Antitumor agents Applications of Graph Theory and Complex Networks Bioinformatics Breast cancer Cancer therapies Cell culture Chemotherapy Computation Computational Biology/Bioinformatics Computer Science Doxorubicin Estrogens Gene expression Growth models Health Informatics Mathematical models Mouse devices Original Article Paclitaxel Parameterization Parameters Patients Pharmacokinetics Shrinkage Tumor cell lines Tumors Xenografts Xenotransplantation |
| title | A gene expression-based mathematical modeling approach for breast cancer tumor growth and shrinkage |
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