Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-05-21

AUTHORS

Hiroki Ikeda, Rob J de Boer, Kei Sato, Satoru Morita, Naoko Misawa, Yoshio Koyanagi, Kazuyuki Aihara, Shingo Iwami

ABSTRACT

BackgroundMathematical modeling of virus dynamics has provided quantitative insights into viral infections such as influenza, the simian immunodeficiency virus/human immunodeficiency virus, hepatitis B, and hepatitis C. Through modeling, we can estimate the half-life of infected cells, the exponential growth rate, and the basic reproduction number (R0). To calculate R0 from virus load data, the death rate of productively infected cells is required. This can be readily estimated from treatment data collected during the chronic phase, but is difficult to determine from acute infection data. Here, we propose two new models that can reliably estimate the average life span of infected cells from acute-phase data, and apply both methods to experimental data from humanized mice infected with HIV-1.MethodsBoth new models, called as the reduced quasi-steady state (RQS) model and the piece-wise regression (PWR) model, are derived by simplification of a standard model for the acute-phase dynamics of target cells, viruses and infected cells. By having only a limited number of parameters, both models allow us to reliably estimate the death rate of productively infected cells. Simulated datasets with plausible parameter values are generated with the standard model to compare the performance of the new models with that of the major previous model (i.e., the simple exponential model). Finally, we fit models to time course data from HIV-1 infected humanized mice to estimate the several important parameters characterizing their acute infection.Results and conclusionsThe new models provided much better estimates than the previous model because they more precisely capture the de novo infection process. Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. These estimates are fairly close to those obtained in humans. More... »

PAGES

22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1742-4682-11-22

DOI

http://dx.doi.org/10.1186/1742-4682-11-22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027609943

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24885827


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Acute Disease", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cell Death", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Disease Models, Animal", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "HIV Infections", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "HIV-1", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mice", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Statistical", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.177174.3", 
          "name": [
            "Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ikeda", 
        "givenName": "Hiroki", 
        "id": "sg:person.0732120026.62", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0732120026.62"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, CH3584, the Netherlands", 
          "id": "http://www.grid.ac/institutes/grid.5477.1", 
          "name": [
            "Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, CH3584, the Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Boer", 
        "givenName": "Rob J", 
        "id": "sg:person.0774503452.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774503452.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sato", 
        "givenName": "Kei", 
        "id": "sg:person.0710074174.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710074174.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Mathematical and Systems Engineering, Shizuoka University, 432-8561, Hamamatsu, Shizuoka, Japan", 
          "id": "http://www.grid.ac/institutes/grid.263536.7", 
          "name": [
            "Department of Mathematical and Systems Engineering, Shizuoka University, 432-8561, Hamamatsu, Shizuoka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Morita", 
        "givenName": "Satoru", 
        "id": "sg:person.01076202570.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076202570.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Misawa", 
        "givenName": "Naoko", 
        "id": "sg:person.0734604661.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734604661.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Koyanagi", 
        "givenName": "Yoshio", 
        "id": "sg:person.01233375061.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01233375061.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan", 
            "Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Aihara", 
        "givenName": "Kazuyuki", 
        "id": "sg:person.01323523402.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323523402.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama, Japan", 
          "id": "http://www.grid.ac/institutes/grid.419082.6", 
          "name": [
            "Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka, Japan", 
            "Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Iwami", 
        "givenName": "Shingo", 
        "id": "sg:person.01051033261.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051033261.50"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf02464422", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030880844", 
          "https://doi.org/10.1007/bf02464422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature10831", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024992198", 
          "https://doi.org/10.1038/nature10831"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nri700", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003731496", 
          "https://doi.org/10.1038/nri700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/387188a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026180202", 
          "https://doi.org/10.1038/387188a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11901-011-0101-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023004778", 
          "https://doi.org/10.1007/s11901-011-0101-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrmicro772", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016054511", 
          "https://doi.org/10.1038/nrmicro772"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature08260", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041002773", 
          "https://doi.org/10.1038/nature08260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/s0092-8240(05)80039-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054613580", 
          "https://doi.org/10.1016/s0092-8240(05)80039-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/90968", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041042658", 
          "https://doi.org/10.1038/90968"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-05-21", 
    "datePublishedReg": "2014-05-21", 
    "description": "BackgroundMathematical modeling of virus dynamics has provided quantitative insights into viral infections such as influenza, the simian immunodeficiency virus/human immunodeficiency virus, hepatitis B, and hepatitis C. Through modeling, we can estimate the half-life of infected cells, the exponential growth rate, and the basic reproduction number (R0). To calculate R0 from virus load data, the death rate of productively infected cells is required. This can be readily estimated from treatment data collected during the chronic phase, but is difficult to determine from acute infection data. Here, we propose two new models that can reliably estimate the average life span of infected cells from acute-phase data, and apply both methods to experimental data from humanized mice infected with HIV-1.MethodsBoth new models, called as the reduced quasi-steady state (RQS) model and the piece-wise regression (PWR) model, are derived by simplification of a standard model for the acute-phase dynamics of target cells, viruses and infected cells. By having only a limited number of parameters, both models allow us to reliably estimate the death rate of productively infected cells. Simulated datasets with plausible parameter values are generated with the standard model to compare the performance of the new models with that of the major previous model (i.e., the simple exponential model). Finally, we fit models to time course data from HIV-1 infected humanized mice to estimate the several important parameters characterizing their acute infection.Results and conclusionsThe new models provided much better estimates than the previous model because they more precisely capture the de novo infection process. Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. These estimates are fairly close to those obtained in humans.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/1742-4682-11-22", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6081990", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6134570", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6137911", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1034054", 
        "issn": [
          "1742-4682"
        ], 
        "name": "Theoretical Biology and Medical Modelling", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "keywords": [
      "humanized mice", 
      "death rate", 
      "basic reproduction number", 
      "acute phase", 
      "HIV-1", 
      "infected cells", 
      "acute HIV-1 infection", 
      "HIV-1 infection", 
      "human immunodeficiency virus", 
      "humanized mouse model", 
      "acute phase data", 
      "reproduction number", 
      "time course data", 
      "average basic reproduction number", 
      "quasi-steady state model", 
      "hepatitis B", 
      "hepatitis C.", 
      "acute infection", 
      "chronic phase", 
      "immunodeficiency virus", 
      "average death rate", 
      "standard model", 
      "virus infection", 
      "exponential growth rate", 
      "mouse model", 
      "viral infection", 
      "treatment data", 
      "piece-wise regression model", 
      "infection", 
      "target cells", 
      "new model", 
      "mice", 
      "average life span", 
      "previous models", 
      "parameter values", 
      "infection data", 
      "virus dynamics", 
      "state model", 
      "regression models", 
      "plausible parameter values", 
      "virus", 
      "cells", 
      "course data", 
      "best estimate", 
      "experimental data", 
      "average exponential growth rate", 
      "life span", 
      "infection process", 
      "influenza", 
      "dynamics", 
      "PWR model", 
      "rate", 
      "modeling", 
      "model", 
      "quantitative insights", 
      "parameters", 
      "limited number", 
      "important parameters", 
      "estimates", 
      "data", 
      "humans", 
      "simplification", 
      "estimation", 
      "number", 
      "R0", 
      "C.", 
      "load data", 
      "applications", 
      "phase", 
      "growth rate", 
      "performance", 
      "results", 
      "dataset", 
      "span", 
      "values", 
      "insights", 
      "method", 
      "process"
    ], 
    "name": "Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model", 
    "pagination": "22", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027609943"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1742-4682-11-22"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "24885827"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1742-4682-11-22", 
      "https://app.dimensions.ai/details/publication/pub.1027609943"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T15:59", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_645.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/1742-4682-11-22"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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.10.1186/1742-4682-11-22'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1742-4682-11-22'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1742-4682-11-22'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1742-4682-11-22'


 

This table displays all metadata directly associated to this object as RDF triples.

278 TRIPLES      21 PREDICATES      120 URIs      104 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1742-4682-11-22 schema:about N5120d825c8274ad38cb127fb75c30033
2 N58df89d0f654421e971cf7995019e25b
3 N881b6e7e8d35480088990d41fc15a275
4 N896fba042e514f30ae1b3e36b024d218
5 Ncdbd7e5d60934817b23bbd19170598dd
6 Nd743b51f24264dd19306a7ce8efc45e0
7 Nd91fbde5d1a64b6f9886d0c016969e50
8 Nebf3603b974a439ea7bb5c53585f595d
9 Nfb63b068461545a691cfcc446566935e
10 anzsrc-for:06
11 schema:author Nc1770382e45d4f6abb258d27cc5627ee
12 schema:citation sg:pub.10.1007/bf02464422
13 sg:pub.10.1007/s11901-011-0101-7
14 sg:pub.10.1016/s0092-8240(05)80039-4
15 sg:pub.10.1038/387188a0
16 sg:pub.10.1038/90968
17 sg:pub.10.1038/nature08260
18 sg:pub.10.1038/nature10831
19 sg:pub.10.1038/nri700
20 sg:pub.10.1038/nrmicro772
21 schema:datePublished 2014-05-21
22 schema:datePublishedReg 2014-05-21
23 schema:description BackgroundMathematical modeling of virus dynamics has provided quantitative insights into viral infections such as influenza, the simian immunodeficiency virus/human immunodeficiency virus, hepatitis B, and hepatitis C. Through modeling, we can estimate the half-life of infected cells, the exponential growth rate, and the basic reproduction number (R0). To calculate R0 from virus load data, the death rate of productively infected cells is required. This can be readily estimated from treatment data collected during the chronic phase, but is difficult to determine from acute infection data. Here, we propose two new models that can reliably estimate the average life span of infected cells from acute-phase data, and apply both methods to experimental data from humanized mice infected with HIV-1.MethodsBoth new models, called as the reduced quasi-steady state (RQS) model and the piece-wise regression (PWR) model, are derived by simplification of a standard model for the acute-phase dynamics of target cells, viruses and infected cells. By having only a limited number of parameters, both models allow us to reliably estimate the death rate of productively infected cells. Simulated datasets with plausible parameter values are generated with the standard model to compare the performance of the new models with that of the major previous model (i.e., the simple exponential model). Finally, we fit models to time course data from HIV-1 infected humanized mice to estimate the several important parameters characterizing their acute infection.Results and conclusionsThe new models provided much better estimates than the previous model because they more precisely capture the de novo infection process. Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. These estimates are fairly close to those obtained in humans.
24 schema:genre article
25 schema:isAccessibleForFree true
26 schema:isPartOf Nda659c7484004f639f39866511f992a8
27 Nf8ce0fdb8d574a07b53a97046401084f
28 sg:journal.1034054
29 schema:keywords C.
30 HIV-1
31 HIV-1 infection
32 PWR model
33 R0
34 acute HIV-1 infection
35 acute infection
36 acute phase
37 acute phase data
38 applications
39 average basic reproduction number
40 average death rate
41 average exponential growth rate
42 average life span
43 basic reproduction number
44 best estimate
45 cells
46 chronic phase
47 course data
48 data
49 dataset
50 death rate
51 dynamics
52 estimates
53 estimation
54 experimental data
55 exponential growth rate
56 growth rate
57 hepatitis B
58 hepatitis C.
59 human immunodeficiency virus
60 humanized mice
61 humanized mouse model
62 humans
63 immunodeficiency virus
64 important parameters
65 infected cells
66 infection
67 infection data
68 infection process
69 influenza
70 insights
71 life span
72 limited number
73 load data
74 method
75 mice
76 model
77 modeling
78 mouse model
79 new model
80 number
81 parameter values
82 parameters
83 performance
84 phase
85 piece-wise regression model
86 plausible parameter values
87 previous models
88 process
89 quantitative insights
90 quasi-steady state model
91 rate
92 regression models
93 reproduction number
94 results
95 simplification
96 span
97 standard model
98 state model
99 target cells
100 time course data
101 treatment data
102 values
103 viral infection
104 virus
105 virus dynamics
106 virus infection
107 schema:name Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model
108 schema:pagination 22
109 schema:productId N1f232f84a0c74800a2e23aedda2a38a0
110 N3d6becf2d6d6485c8375eae47e64991c
111 Nc268b871891d483db98f86135fd8e731
112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027609943
113 https://doi.org/10.1186/1742-4682-11-22
114 schema:sdDatePublished 2022-09-02T15:59
115 schema:sdLicense https://scigraph.springernature.com/explorer/license/
116 schema:sdPublisher N1ca99fe1132541e2bb46db13ff9c03fe
117 schema:url https://doi.org/10.1186/1742-4682-11-22
118 sgo:license sg:explorer/license/
119 sgo:sdDataset articles
120 rdf:type schema:ScholarlyArticle
121 N1ca99fe1132541e2bb46db13ff9c03fe schema:name Springer Nature - SN SciGraph project
122 rdf:type schema:Organization
123 N1f232f84a0c74800a2e23aedda2a38a0 schema:name pubmed_id
124 schema:value 24885827
125 rdf:type schema:PropertyValue
126 N2b01857d198c41e497fdd7a0d2808d77 rdf:first sg:person.01051033261.50
127 rdf:rest rdf:nil
128 N2f993ed9d2ca4a5c9281430d8b0183a2 rdf:first sg:person.0774503452.97
129 rdf:rest Nb80ac123cbf54b0f93f4457145ff3211
130 N3d6becf2d6d6485c8375eae47e64991c schema:name doi
131 schema:value 10.1186/1742-4682-11-22
132 rdf:type schema:PropertyValue
133 N3d85210602af4072a19c43177cbba171 rdf:first sg:person.0734604661.00
134 rdf:rest Nfd462a0d5696405b820f1c57dce03539
135 N5120d825c8274ad38cb127fb75c30033 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Mice
137 rdf:type schema:DefinedTerm
138 N58df89d0f654421e971cf7995019e25b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Cell Death
140 rdf:type schema:DefinedTerm
141 N881b6e7e8d35480088990d41fc15a275 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
142 schema:name Disease Models, Animal
143 rdf:type schema:DefinedTerm
144 N896fba042e514f30ae1b3e36b024d218 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
145 schema:name Models, Statistical
146 rdf:type schema:DefinedTerm
147 N9029aea0da904778ac3b69ac6d39809d rdf:first sg:person.01323523402.06
148 rdf:rest N2b01857d198c41e497fdd7a0d2808d77
149 Nb80ac123cbf54b0f93f4457145ff3211 rdf:first sg:person.0710074174.33
150 rdf:rest Nee6e9de2393a49be8b19aef31c478b78
151 Nc1770382e45d4f6abb258d27cc5627ee rdf:first sg:person.0732120026.62
152 rdf:rest N2f993ed9d2ca4a5c9281430d8b0183a2
153 Nc268b871891d483db98f86135fd8e731 schema:name dimensions_id
154 schema:value pub.1027609943
155 rdf:type schema:PropertyValue
156 Ncdbd7e5d60934817b23bbd19170598dd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name HIV Infections
158 rdf:type schema:DefinedTerm
159 Nd743b51f24264dd19306a7ce8efc45e0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
160 schema:name Acute Disease
161 rdf:type schema:DefinedTerm
162 Nd91fbde5d1a64b6f9886d0c016969e50 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name HIV-1
164 rdf:type schema:DefinedTerm
165 Nda659c7484004f639f39866511f992a8 schema:issueNumber 1
166 rdf:type schema:PublicationIssue
167 Nebf3603b974a439ea7bb5c53585f595d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
168 schema:name Animals
169 rdf:type schema:DefinedTerm
170 Nee6e9de2393a49be8b19aef31c478b78 rdf:first sg:person.01076202570.34
171 rdf:rest N3d85210602af4072a19c43177cbba171
172 Nf8ce0fdb8d574a07b53a97046401084f schema:volumeNumber 11
173 rdf:type schema:PublicationVolume
174 Nfb63b068461545a691cfcc446566935e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
175 schema:name Humans
176 rdf:type schema:DefinedTerm
177 Nfd462a0d5696405b820f1c57dce03539 rdf:first sg:person.01233375061.92
178 rdf:rest N9029aea0da904778ac3b69ac6d39809d
179 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
180 schema:name Biological Sciences
181 rdf:type schema:DefinedTerm
182 sg:grant.6081990 http://pending.schema.org/fundedItem sg:pub.10.1186/1742-4682-11-22
183 rdf:type schema:MonetaryGrant
184 sg:grant.6134570 http://pending.schema.org/fundedItem sg:pub.10.1186/1742-4682-11-22
185 rdf:type schema:MonetaryGrant
186 sg:grant.6137911 http://pending.schema.org/fundedItem sg:pub.10.1186/1742-4682-11-22
187 rdf:type schema:MonetaryGrant
188 sg:journal.1034054 schema:issn 1742-4682
189 schema:name Theoretical Biology and Medical Modelling
190 schema:publisher Springer Nature
191 rdf:type schema:Periodical
192 sg:person.01051033261.50 schema:affiliation grid-institutes:grid.419082.6
193 schema:familyName Iwami
194 schema:givenName Shingo
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051033261.50
196 rdf:type schema:Person
197 sg:person.01076202570.34 schema:affiliation grid-institutes:grid.263536.7
198 schema:familyName Morita
199 schema:givenName Satoru
200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076202570.34
201 rdf:type schema:Person
202 sg:person.01233375061.92 schema:affiliation grid-institutes:grid.258799.8
203 schema:familyName Koyanagi
204 schema:givenName Yoshio
205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01233375061.92
206 rdf:type schema:Person
207 sg:person.01323523402.06 schema:affiliation grid-institutes:grid.26999.3d
208 schema:familyName Aihara
209 schema:givenName Kazuyuki
210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323523402.06
211 rdf:type schema:Person
212 sg:person.0710074174.33 schema:affiliation grid-institutes:grid.258799.8
213 schema:familyName Sato
214 schema:givenName Kei
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710074174.33
216 rdf:type schema:Person
217 sg:person.0732120026.62 schema:affiliation grid-institutes:grid.177174.3
218 schema:familyName Ikeda
219 schema:givenName Hiroki
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0732120026.62
221 rdf:type schema:Person
222 sg:person.0734604661.00 schema:affiliation grid-institutes:grid.258799.8
223 schema:familyName Misawa
224 schema:givenName Naoko
225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0734604661.00
226 rdf:type schema:Person
227 sg:person.0774503452.97 schema:affiliation grid-institutes:grid.5477.1
228 schema:familyName de Boer
229 schema:givenName Rob J
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774503452.97
231 rdf:type schema:Person
232 sg:pub.10.1007/bf02464422 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030880844
233 https://doi.org/10.1007/bf02464422
234 rdf:type schema:CreativeWork
235 sg:pub.10.1007/s11901-011-0101-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023004778
236 https://doi.org/10.1007/s11901-011-0101-7
237 rdf:type schema:CreativeWork
238 sg:pub.10.1016/s0092-8240(05)80039-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054613580
239 https://doi.org/10.1016/s0092-8240(05)80039-4
240 rdf:type schema:CreativeWork
241 sg:pub.10.1038/387188a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026180202
242 https://doi.org/10.1038/387188a0
243 rdf:type schema:CreativeWork
244 sg:pub.10.1038/90968 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041042658
245 https://doi.org/10.1038/90968
246 rdf:type schema:CreativeWork
247 sg:pub.10.1038/nature08260 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041002773
248 https://doi.org/10.1038/nature08260
249 rdf:type schema:CreativeWork
250 sg:pub.10.1038/nature10831 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024992198
251 https://doi.org/10.1038/nature10831
252 rdf:type schema:CreativeWork
253 sg:pub.10.1038/nri700 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003731496
254 https://doi.org/10.1038/nri700
255 rdf:type schema:CreativeWork
256 sg:pub.10.1038/nrmicro772 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016054511
257 https://doi.org/10.1038/nrmicro772
258 rdf:type schema:CreativeWork
259 grid-institutes:grid.177174.3 schema:alternateName Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka, Japan
260 schema:name Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka, Japan
261 rdf:type schema:Organization
262 grid-institutes:grid.258799.8 schema:alternateName Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan
263 schema:name Laboratory of Viral Pathogenesis, Institute for Virus Research, Kyoto University, Kyoto, Japan
264 rdf:type schema:Organization
265 grid-institutes:grid.263536.7 schema:alternateName Department of Mathematical and Systems Engineering, Shizuoka University, 432-8561, Hamamatsu, Shizuoka, Japan
266 schema:name Department of Mathematical and Systems Engineering, Shizuoka University, 432-8561, Hamamatsu, Shizuoka, Japan
267 rdf:type schema:Organization
268 grid-institutes:grid.26999.3d schema:alternateName Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
269 schema:name Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
270 Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan
271 rdf:type schema:Organization
272 grid-institutes:grid.419082.6 schema:alternateName Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama, Japan
273 schema:name Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka, Japan
274 Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama, Japan
275 rdf:type schema:Organization
276 grid-institutes:grid.5477.1 schema:alternateName Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, CH3584, the Netherlands
277 schema:name Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, CH3584, the Netherlands
278 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...