Finding Intervention Points in the Pathogenesis of Dengue Viral Infection
We use probabilistic Boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. We use an aggregated function approach to derive the DHF Infection Model....
Gespeichert in:
| Veröffentlicht in: | 2006 International Conference of the IEEE Engineering in Medicine and Biology Society 2006, Vol.2006, p.5315-5321 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5321 |
|---|---|
| container_issue | |
| container_start_page | 5315 |
| container_title | 2006 International Conference of the IEEE Engineering in Medicine and Biology Society |
| container_volume | 2006 |
| creator | Tay, J.C. Tan, P. |
| description | We use probabilistic Boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. We use an aggregated function approach to derive the DHF Infection Model. Two basins of attractors are observed with synchronous updating; the Null Infection cycle attractor shows an expected cross-regulation of Th1 and Th2 cytokines corresponding to the homeostasis of an uninfected person, while the DHF Infection attractor shows the onset of DHF. With asynchronous updating, our model remains valid with clinical comparisons against qualitative changes in signal durations. In order to find intervention points that could prevent DHF we design a genetic algorithm to shift the DHF attractor to the DF attractor basin by using the DF final state as the fitness measure. Our simulation results identify TGF-beta, IL-8 and IL-13 as the intervention points which are consistent with known clinical results to prevent DHF from occurring |
| doi_str_mv | 10.1109/IEMBS.2006.259796 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_6IE</sourceid><recordid>TN_cdi_ieee_primary_4463004</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4463004</ieee_id><sourcerecordid>68360154</sourcerecordid><originalsourceid>FETCH-LOGICAL-i214t-763db8d134c8ff70532ec9574eb30499d9b324ad0eced0c53004b5c69878dc8a3</originalsourceid><addsrcrecordid>eNo9j0tPwzAQhC0BolXpD0BIyCduKXZsx_YR-oBIRVTiIW5RYm9ao9QpcYLEvydVC3vZw3wzO4vQJSUTSom-TedP9y-TmJBkEgstdXKCxloqymPOCWExP0VDKoSMqCQfAzQO4ZP0w3Qvx-doQKXmQmk6ROnCeev8Gqe-heYbfOtqj1e1823AzuN2A3iVt5t6DR6CC7gu8Qz8ugP87pq86n0lmL3pAp2VeRVgfNwj9LaYv04fo-XzQzq9W0YupryNZMJsoSxl3KiylESwGIwWkkPBCNfa6qLvn1sCBiwxghHCC2ESraSyRuVshG4Oubum_uogtNnWBQNVlXuou5AliiWECt6D10ewK7Zgs13jtnnzk_093wNXB8ABwL_MebK_yX4BSVZmWA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>68360154</pqid></control><display><type>article</type><title>Finding Intervention Points in the Pathogenesis of Dengue Viral Infection</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Tay, J.C. ; Tan, P.</creator><creatorcontrib>Tay, J.C. ; Tan, P.</creatorcontrib><description>We use probabilistic Boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. We use an aggregated function approach to derive the DHF Infection Model. Two basins of attractors are observed with synchronous updating; the Null Infection cycle attractor shows an expected cross-regulation of Th1 and Th2 cytokines corresponding to the homeostasis of an uninfected person, while the DHF Infection attractor shows the onset of DHF. With asynchronous updating, our model remains valid with clinical comparisons against qualitative changes in signal durations. In order to find intervention points that could prevent DHF we design a genetic algorithm to shift the DHF attractor to the DF attractor basin by using the DF final state as the fitness measure. Our simulation results identify TGF-beta, IL-8 and IL-13 as the intervention points which are consistent with known clinical results to prevent DHF from occurring</description><identifier>ISSN: 1557-170X</identifier><identifier>ISBN: 9781424400324</identifier><identifier>ISBN: 1424400325</identifier><identifier>DOI: 10.1109/IEMBS.2006.259796</identifier><identifier>PMID: 17945891</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Biological system modeling ; Cities and towns ; Decision support systems ; Dengue Virus - metabolism ; Disease Outbreaks ; Electric shock ; Gene Expression Regulation ; Genetic algorithms ; Hemorrhaging ; Humans ; Immune system ; Interleukin-13 - metabolism ; Interleukin-8 - metabolism ; Models, Biological ; Models, Statistical ; Models, Theoretical ; Pathogens ; Probability ; Programming Languages ; Severe Dengue - diagnosis ; Severe Dengue - therapy ; Stochastic Processes ; Transforming Growth Factor beta - metabolism ; USA Councils ; White blood cells</subject><ispartof>2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, Vol.2006, p.5315-5321</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4463004$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,4051,4052,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4463004$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17945891$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tay, J.C.</creatorcontrib><creatorcontrib>Tan, P.</creatorcontrib><title>Finding Intervention Points in the Pathogenesis of Dengue Viral Infection</title><title>2006 International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>IEMBS</addtitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><description>We use probabilistic Boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. We use an aggregated function approach to derive the DHF Infection Model. Two basins of attractors are observed with synchronous updating; the Null Infection cycle attractor shows an expected cross-regulation of Th1 and Th2 cytokines corresponding to the homeostasis of an uninfected person, while the DHF Infection attractor shows the onset of DHF. With asynchronous updating, our model remains valid with clinical comparisons against qualitative changes in signal durations. In order to find intervention points that could prevent DHF we design a genetic algorithm to shift the DHF attractor to the DF attractor basin by using the DF final state as the fitness measure. Our simulation results identify TGF-beta, IL-8 and IL-13 as the intervention points which are consistent with known clinical results to prevent DHF from occurring</description><subject>Algorithms</subject><subject>Biological system modeling</subject><subject>Cities and towns</subject><subject>Decision support systems</subject><subject>Dengue Virus - metabolism</subject><subject>Disease Outbreaks</subject><subject>Electric shock</subject><subject>Gene Expression Regulation</subject><subject>Genetic algorithms</subject><subject>Hemorrhaging</subject><subject>Humans</subject><subject>Immune system</subject><subject>Interleukin-13 - metabolism</subject><subject>Interleukin-8 - metabolism</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Models, Theoretical</subject><subject>Pathogens</subject><subject>Probability</subject><subject>Programming Languages</subject><subject>Severe Dengue - diagnosis</subject><subject>Severe Dengue - therapy</subject><subject>Stochastic Processes</subject><subject>Transforming Growth Factor beta - metabolism</subject><subject>USA Councils</subject><subject>White blood cells</subject><issn>1557-170X</issn><isbn>9781424400324</isbn><isbn>1424400325</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNo9j0tPwzAQhC0BolXpD0BIyCduKXZsx_YR-oBIRVTiIW5RYm9ao9QpcYLEvydVC3vZw3wzO4vQJSUTSom-TedP9y-TmJBkEgstdXKCxloqymPOCWExP0VDKoSMqCQfAzQO4ZP0w3Qvx-doQKXmQmk6ROnCeev8Gqe-heYbfOtqj1e1823AzuN2A3iVt5t6DR6CC7gu8Qz8ugP87pq86n0lmL3pAp2VeRVgfNwj9LaYv04fo-XzQzq9W0YupryNZMJsoSxl3KiylESwGIwWkkPBCNfa6qLvn1sCBiwxghHCC2ESraSyRuVshG4Oubum_uogtNnWBQNVlXuou5AliiWECt6D10ewK7Zgs13jtnnzk_093wNXB8ABwL_MebK_yX4BSVZmWA</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Tay, J.C.</creator><creator>Tan, P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope></search><sort><creationdate>2006</creationdate><title>Finding Intervention Points in the Pathogenesis of Dengue Viral Infection</title><author>Tay, J.C. ; Tan, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i214t-763db8d134c8ff70532ec9574eb30499d9b324ad0eced0c53004b5c69878dc8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Biological system modeling</topic><topic>Cities and towns</topic><topic>Decision support systems</topic><topic>Dengue Virus - metabolism</topic><topic>Disease Outbreaks</topic><topic>Electric shock</topic><topic>Gene Expression Regulation</topic><topic>Genetic algorithms</topic><topic>Hemorrhaging</topic><topic>Humans</topic><topic>Immune system</topic><topic>Interleukin-13 - metabolism</topic><topic>Interleukin-8 - metabolism</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Models, Theoretical</topic><topic>Pathogens</topic><topic>Probability</topic><topic>Programming Languages</topic><topic>Severe Dengue - diagnosis</topic><topic>Severe Dengue - therapy</topic><topic>Stochastic Processes</topic><topic>Transforming Growth Factor beta - metabolism</topic><topic>USA Councils</topic><topic>White blood cells</topic><toplevel>online_resources</toplevel><creatorcontrib>Tay, J.C.</creatorcontrib><creatorcontrib>Tan, P.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>2006 International Conference of the IEEE Engineering in Medicine and Biology Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tay, J.C.</au><au>Tan, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Finding Intervention Points in the Pathogenesis of Dengue Viral Infection</atitle><jtitle>2006 International Conference of the IEEE Engineering in Medicine and Biology Society</jtitle><stitle>IEMBS</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2006</date><risdate>2006</risdate><volume>2006</volume><spage>5315</spage><epage>5321</epage><pages>5315-5321</pages><issn>1557-170X</issn><isbn>9781424400324</isbn><isbn>1424400325</isbn><abstract>We use probabilistic Boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. We use an aggregated function approach to derive the DHF Infection Model. Two basins of attractors are observed with synchronous updating; the Null Infection cycle attractor shows an expected cross-regulation of Th1 and Th2 cytokines corresponding to the homeostasis of an uninfected person, while the DHF Infection attractor shows the onset of DHF. With asynchronous updating, our model remains valid with clinical comparisons against qualitative changes in signal durations. In order to find intervention points that could prevent DHF we design a genetic algorithm to shift the DHF attractor to the DF attractor basin by using the DF final state as the fitness measure. Our simulation results identify TGF-beta, IL-8 and IL-13 as the intervention points which are consistent with known clinical results to prevent DHF from occurring</abstract><cop>United States</cop><pub>IEEE</pub><pmid>17945891</pmid><doi>10.1109/IEMBS.2006.259796</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1557-170X |
| ispartof | 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, Vol.2006, p.5315-5321 |
| issn | 1557-170X |
| language | eng |
| recordid | cdi_ieee_primary_4463004 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithms Biological system modeling Cities and towns Decision support systems Dengue Virus - metabolism Disease Outbreaks Electric shock Gene Expression Regulation Genetic algorithms Hemorrhaging Humans Immune system Interleukin-13 - metabolism Interleukin-8 - metabolism Models, Biological Models, Statistical Models, Theoretical Pathogens Probability Programming Languages Severe Dengue - diagnosis Severe Dengue - therapy Stochastic Processes Transforming Growth Factor beta - metabolism USA Councils White blood cells |
| title | Finding Intervention Points in the Pathogenesis of Dengue Viral Infection |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T02%3A57%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Finding%20Intervention%20Points%20in%20the%20Pathogenesis%20of%20Dengue%20Viral%20Infection&rft.jtitle=2006%20International%20Conference%20of%20the%20IEEE%20Engineering%20in%20Medicine%20and%20Biology%20Society&rft.au=Tay,%20J.C.&rft.date=2006&rft.volume=2006&rft.spage=5315&rft.epage=5321&rft.pages=5315-5321&rft.issn=1557-170X&rft.isbn=9781424400324&rft.isbn_list=1424400325&rft_id=info:doi/10.1109/IEMBS.2006.259796&rft_dat=%3Cproquest_6IE%3E68360154%3C/proquest_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=68360154&rft_id=info:pmid/17945891&rft_ieee_id=4463004&rfr_iscdi=true |