Ontology type: schema:ScholarlyArticle
2016-06-28
AUTHORSKrzysztof Puszynski, Alberto Gandolfi, Alberto d’Onofrio
ABSTRACTIn this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose. More... »
PAGES395-410
http://scigraph.springernature.com/pub.10.1007/s10928-016-9480-2
DOIhttp://dx.doi.org/10.1007/s10928-016-9480-2
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1009961091
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/27352096
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1115",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Pharmacology and Pharmaceutical Sciences",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Dose-Response Relationship, Drug",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Feedback, Physiological",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Regulatory Networks",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Biological",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Pharmaceutical Preparations",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Pharmacological Phenomena",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Stochastic Processes",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Transcription Factors",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Transcription, Genetic",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Institute of Automatic Control, Silesian University of Technology, Akademicka 16, Gliwice, Poland",
"id": "http://www.grid.ac/institutes/grid.6979.1",
"name": [
"Institute of Automatic Control, Silesian University of Technology, Akademicka 16, Gliwice, Poland"
],
"type": "Organization"
},
"familyName": "Puszynski",
"givenName": "Krzysztof",
"id": "sg:person.01060502606.33",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01060502606.33"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Istituto di Analisi dei Sistemi ed Informatica \u201cA. Ruberti\u201d - CNR, Via dei Taurini 19, Rome, Italy",
"id": "http://www.grid.ac/institutes/grid.419461.f",
"name": [
"Istituto di Analisi dei Sistemi ed Informatica \u201cA. Ruberti\u201d - CNR, Via dei Taurini 19, Rome, Italy"
],
"type": "Organization"
},
"familyName": "Gandolfi",
"givenName": "Alberto",
"id": "sg:person.0623363352.52",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0623363352.52"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "International Prevention Research Institute, 95 Cours Lafayette, Lyon, France",
"id": "http://www.grid.ac/institutes/grid.419381.6",
"name": [
"International Prevention Research Institute, 95 Cours Lafayette, Lyon, France"
],
"type": "Organization"
},
"familyName": "d\u2019Onofrio",
"givenName": "Alberto",
"id": "sg:person.0622225547.87",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0622225547.87"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1186/1471-2199-9-63",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028885990",
"https://doi.org/10.1186/1471-2199-9-63"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrg1615",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014958319",
"https://doi.org/10.1038/nrg1615"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/onc.2008.166",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041105899",
"https://doi.org/10.1038/onc.2008.166"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/35042675",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023763406",
"https://doi.org/10.1038/35042675"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-12145-1_15",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029586398",
"https://doi.org/10.1007/978-3-319-12145-1_15"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10928-005-2105-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000526621",
"https://doi.org/10.1007/s10928-005-2105-9"
],
"type": "CreativeWork"
}
],
"datePublished": "2016-06-28",
"datePublishedReg": "2016-06-28",
"description": "In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.",
"genre": "article",
"id": "sg:pub.10.1007/s10928-016-9480-2",
"inLanguage": "en",
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.9741753",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1016394",
"issn": [
"1567-567X",
"2168-5789"
],
"name": "Journal of Pharmacokinetics and Pharmacodynamics",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "4",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "43"
}
],
"keywords": [
"gene switching",
"single gene copy",
"average gene",
"stochastic gene",
"transcription factors",
"corresponding protein",
"gene copies",
"genes",
"slow genes",
"molecular circuits",
"basic mechanisms",
"protein",
"absence of drug",
"stochastic fluctuations",
"primary role",
"cells",
"transcription",
"experimental dose-response curves",
"negative feedback",
"significant fraction",
"mechanism",
"role",
"copies",
"translation",
"absence",
"dose-response curve",
"concentration",
"intermediate values",
"deterministic approximation",
"fraction",
"response",
"switching rate",
"high drug concentrations",
"drugs",
"levels",
"factors",
"rate",
"certain drugs",
"success",
"sigmoidal shape",
"prediction",
"deactivation rate",
"switching",
"OFF state",
"class",
"large class",
"fluctuations",
"drug concentrations",
"impact",
"time",
"elimination",
"shape",
"success of therapy",
"onset",
"successive translations",
"maximum",
"phenomenon",
"state",
"interesting phenomenon",
"ON",
"magnitude",
"deterministic setting",
"therapy",
"circuit",
"threshold",
"values",
"feedback",
"dose",
"pharmacodynamics",
"cases",
"curves",
"simulations",
"drug dose",
"setting",
"stochastic prediction",
"paper",
"approximation",
"numerical simulations"
],
"name": "The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms",
"pagination": "395-410",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1009961091"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s10928-016-9480-2"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"27352096"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s10928-016-9480-2",
"https://app.dimensions.ai/details/publication/pub.1009961091"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:31",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_684.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s10928-016-9480-2"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10928-016-9480-2'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10928-016-9480-2'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10928-016-9480-2'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10928-016-9480-2'
This table displays all metadata directly associated to this object as RDF triples.
226 TRIPLES
22 PREDICATES
120 URIs
106 LITERALS
17 BLANK NODES