Detecting regulatory mechanisms in endocrine time series measurements
The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormo...
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| Veröffentlicht in: | PloS one 2012-03, Vol.7 (3), p.e32985-e32985 |
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| creator | Vis, Daniel J Westerhuis, Johan A Hoefsloot, Huub C J Roelfsema, Ferdinand Hendriks, Margriet M W B Smilde, Age K |
| description | The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance. |
| doi_str_mv | 10.1371/journal.pone.0032985 |
| format | Article |
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Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0032985</identifier><identifier>PMID: 22461889</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Activation ; Adrenocorticotropic hormone ; Adrenocorticotropic Hormone - blood ; Adult ; Age ; Algorithms ; Biology ; Body mass index ; Circadian rhythm ; Circadian Rhythm - physiology ; Circadian rhythms ; Cohort Studies ; Corticotropin ; Cortisol ; Data analysis ; Design ; Endocrine System - physiology ; Endocrine System - secretion ; Endocrinology ; Estradiol - blood ; Experiments ; Female ; Follicle Stimulating Hormone - blood ; Gender ; Glucocorticoids ; Hormones ; Hormones - blood ; Human Growth Hormone - blood ; Humans ; Life sciences ; Lifestyles ; Luteinizing Hormone - blood ; Male ; Mathematics ; Measurement ; Medical research ; Medicine ; Metabolic disorders ; Middle Aged ; Models, Biological ; Physiology ; Regulatory mechanisms (biology) ; Simulation ; Testosterone - blood ; Time Factors ; Time measurement ; Time series</subject><ispartof>PloS one, 2012-03, Vol.7 (3), p.e32985-e32985</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>2012 Vis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Vis et al. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c691t-16d6bab258f2f9f438eeaa02eac240d49c7a21d64b168805ed875e729bba0fa63</citedby><cites>FETCH-LOGICAL-c691t-16d6bab258f2f9f438eeaa02eac240d49c7a21d64b168805ed875e729bba0fa63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312884/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312884/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22461889$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Preiss, Thomas</contributor><creatorcontrib>Vis, Daniel J</creatorcontrib><creatorcontrib>Westerhuis, Johan A</creatorcontrib><creatorcontrib>Hoefsloot, Huub C J</creatorcontrib><creatorcontrib>Roelfsema, Ferdinand</creatorcontrib><creatorcontrib>Hendriks, Margriet M W B</creatorcontrib><creatorcontrib>Smilde, Age K</creatorcontrib><title>Detecting regulatory mechanisms in endocrine time series measurements</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance.</description><subject>Activation</subject><subject>Adrenocorticotropic hormone</subject><subject>Adrenocorticotropic Hormone - blood</subject><subject>Adult</subject><subject>Age</subject><subject>Algorithms</subject><subject>Biology</subject><subject>Body mass index</subject><subject>Circadian rhythm</subject><subject>Circadian Rhythm - physiology</subject><subject>Circadian rhythms</subject><subject>Cohort Studies</subject><subject>Corticotropin</subject><subject>Cortisol</subject><subject>Data analysis</subject><subject>Design</subject><subject>Endocrine System - physiology</subject><subject>Endocrine System - secretion</subject><subject>Endocrinology</subject><subject>Estradiol - blood</subject><subject>Experiments</subject><subject>Female</subject><subject>Follicle Stimulating Hormone - blood</subject><subject>Gender</subject><subject>Glucocorticoids</subject><subject>Hormones</subject><subject>Hormones - blood</subject><subject>Human Growth Hormone - 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Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>22461889</pmid><doi>10.1371/journal.pone.0032985</doi><tpages>e32985</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Activation Adrenocorticotropic hormone Adrenocorticotropic Hormone - blood Adult Age Algorithms Biology Body mass index Circadian rhythm Circadian Rhythm - physiology Circadian rhythms Cohort Studies Corticotropin Cortisol Data analysis Design Endocrine System - physiology Endocrine System - secretion Endocrinology Estradiol - blood Experiments Female Follicle Stimulating Hormone - blood Gender Glucocorticoids Hormones Hormones - blood Human Growth Hormone - blood Humans Life sciences Lifestyles Luteinizing Hormone - blood Male Mathematics Measurement Medical research Medicine Metabolic disorders Middle Aged Models, Biological Physiology Regulatory mechanisms (biology) Simulation Testosterone - blood Time Factors Time measurement Time series |
| title | Detecting regulatory mechanisms in endocrine time series measurements |
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