RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells

Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other's expression levels. Theoretical predictions suggest that not only the mean expression le...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Genome Biology 2017-02, Vol.18 (1), p.37-37, Article 37
Hauptverfasser: Bosia, Carla, Sgrò, Francesco, Conti, Laura, Baldassi, Carlo, Brusa, Davide, Cavallo, Federica, Cunto, Ferdinando Di, Turco, Emilia, Pagnani, Andrea, Zecchina, Riccardo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 37
container_issue 1
container_start_page 37
container_title Genome Biology
container_volume 18
creator Bosia, Carla
Sgrò, Francesco
Conti, Laura
Baldassi, Carlo
Brusa, Davide
Cavallo, Federica
Cunto, Ferdinando Di
Turco, Emilia
Pagnani, Andrea
Zecchina, Riccardo
description Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other's expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range. We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other's fluctuations in a miRNA-dependent manner in single cells. We show that miRNA-target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10-1000 copies of targets per cell. Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.
doi_str_mv 10.1186/s13059-017-1162-x
format Article
fullrecord <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5319025</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A481900052</galeid><sourcerecordid>A481900052</sourcerecordid><originalsourceid>FETCH-LOGICAL-c595t-6113399d0ff05e0e615f2db1537435f7815d20d4f72c5fd2b97826c8c75346343</originalsourceid><addsrcrecordid>eNqNkk1v1DAQhiMEoqXwA7igSFzgkOLPOL4grSo-KipACCRultcZ77pK7K2doO2ZP84sW0qXE_LBH_O84_H4raqnlJxS2rWvCuVE6oZQ1VDasmZ7rzqmQolGteT7_Tvro-pRKZeEUC1Y-7A6Yh3DJdfH1c8vHxeldmncwBTiqvYp12NwOf0-H-dptsNwXYfohxmig3paQ8g17hyGppBiwWBt63VYrRGMKTZDiGD_pml62EDsIU71aGOEvBMUvGyA2sEwlMfVA2-HAk9u5pPq29s3X8_eNxef3p2fLS4aJ7WcmpZSzrXuifdEAoGWSs_6JZVcCS696qjsGemFV8xJ37OlVh1rXeeU5KLlgp9Ur_d5N_NyhN5hRdkOZpPDaPO1STaYw0gMa7NKP4zkVBMmMcGLmwQ5Xc1QJjOGsnuCjZDmYminVCckleJ_UNIKxThF9Pk_6GWac8ROGMaI0kRx3SF1uqdWdgCD_5GwRIejB2x0iuADni9Eh6USIhkKXh4IkJlgO63sXIr58Pn8kKV7Fn-slAz-timUmJ3VzN5qBq1mdlYzW9Q8u9vNW8Ufb_FfPwTPLw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2207907398</pqid></control><display><type>article</type><title>RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Springer Nature OA Free Journals</source><source>PubMed Central</source><source>SpringerLink Journals - AutoHoldings</source><creator>Bosia, Carla ; Sgrò, Francesco ; Conti, Laura ; Baldassi, Carlo ; Brusa, Davide ; Cavallo, Federica ; Cunto, Ferdinando Di ; Turco, Emilia ; Pagnani, Andrea ; Zecchina, Riccardo</creator><creatorcontrib>Bosia, Carla ; Sgrò, Francesco ; Conti, Laura ; Baldassi, Carlo ; Brusa, Davide ; Cavallo, Federica ; Cunto, Ferdinando Di ; Turco, Emilia ; Pagnani, Andrea ; Zecchina, Riccardo</creatorcontrib><description>Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other's expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range. We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other's fluctuations in a miRNA-dependent manner in single cells. We show that miRNA-target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10-1000 copies of targets per cell. Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.</description><identifier>ISSN: 1474-760X</identifier><identifier>ISSN: 1474-7596</identifier><identifier>EISSN: 1474-760X</identifier><identifier>DOI: 10.1186/s13059-017-1162-x</identifier><identifier>PMID: 28219439</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Acids ; Analysis ; Biology ; Cancer ; Cell differentiation ; Cell Survival - genetics ; Competition ; Controlled conditions ; Experiments ; Flow cytometry ; Gene Expression ; Gene Expression Regulation ; Genes, Reporter ; Genetic transcription ; Mammalian cells ; MicroRNA ; MicroRNAs ; MicroRNAs - genetics ; Mimicry ; miRNA ; Physiology ; Plasmids ; Plasmids - genetics ; Proteins ; RNA Interference ; RNA, Messenger - genetics ; Single-Cell Analysis ; Stochastic models ; Transcription, Genetic</subject><ispartof>Genome Biology, 2017-02, Vol.18 (1), p.37-37, Article 37</ispartof><rights>COPYRIGHT 2017 BioMed Central Ltd.</rights><rights>2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c595t-6113399d0ff05e0e615f2db1537435f7815d20d4f72c5fd2b97826c8c75346343</citedby><cites>FETCH-LOGICAL-c595t-6113399d0ff05e0e615f2db1537435f7815d20d4f72c5fd2b97826c8c75346343</cites><orcidid>0000-0002-8960-3443</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319025/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319025/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28219439$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bosia, Carla</creatorcontrib><creatorcontrib>Sgrò, Francesco</creatorcontrib><creatorcontrib>Conti, Laura</creatorcontrib><creatorcontrib>Baldassi, Carlo</creatorcontrib><creatorcontrib>Brusa, Davide</creatorcontrib><creatorcontrib>Cavallo, Federica</creatorcontrib><creatorcontrib>Cunto, Ferdinando Di</creatorcontrib><creatorcontrib>Turco, Emilia</creatorcontrib><creatorcontrib>Pagnani, Andrea</creatorcontrib><creatorcontrib>Zecchina, Riccardo</creatorcontrib><title>RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells</title><title>Genome Biology</title><addtitle>Genome Biol</addtitle><description>Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other's expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range. We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other's fluctuations in a miRNA-dependent manner in single cells. We show that miRNA-target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10-1000 copies of targets per cell. Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.</description><subject>Acids</subject><subject>Analysis</subject><subject>Biology</subject><subject>Cancer</subject><subject>Cell differentiation</subject><subject>Cell Survival - genetics</subject><subject>Competition</subject><subject>Controlled conditions</subject><subject>Experiments</subject><subject>Flow cytometry</subject><subject>Gene Expression</subject><subject>Gene Expression Regulation</subject><subject>Genes, Reporter</subject><subject>Genetic transcription</subject><subject>Mammalian cells</subject><subject>MicroRNA</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>Mimicry</subject><subject>miRNA</subject><subject>Physiology</subject><subject>Plasmids</subject><subject>Plasmids - genetics</subject><subject>Proteins</subject><subject>RNA Interference</subject><subject>RNA, Messenger - genetics</subject><subject>Single-Cell Analysis</subject><subject>Stochastic models</subject><subject>Transcription, Genetic</subject><issn>1474-760X</issn><issn>1474-7596</issn><issn>1474-760X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>KPI</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkk1v1DAQhiMEoqXwA7igSFzgkOLPOL4grSo-KipACCRultcZ77pK7K2doO2ZP84sW0qXE_LBH_O84_H4raqnlJxS2rWvCuVE6oZQ1VDasmZ7rzqmQolGteT7_Tvro-pRKZeEUC1Y-7A6Yh3DJdfH1c8vHxeldmncwBTiqvYp12NwOf0-H-dptsNwXYfohxmig3paQ8g17hyGppBiwWBt63VYrRGMKTZDiGD_pml62EDsIU71aGOEvBMUvGyA2sEwlMfVA2-HAk9u5pPq29s3X8_eNxef3p2fLS4aJ7WcmpZSzrXuifdEAoGWSs_6JZVcCS696qjsGemFV8xJ37OlVh1rXeeU5KLlgp9Ur_d5N_NyhN5hRdkOZpPDaPO1STaYw0gMa7NKP4zkVBMmMcGLmwQ5Xc1QJjOGsnuCjZDmYminVCckleJ_UNIKxThF9Pk_6GWac8ROGMaI0kRx3SF1uqdWdgCD_5GwRIejB2x0iuADni9Eh6USIhkKXh4IkJlgO63sXIr58Pn8kKV7Fn-slAz-timUmJ3VzN5qBq1mdlYzW9Q8u9vNW8Ufb_FfPwTPLw</recordid><startdate>20170220</startdate><enddate>20170220</enddate><creator>Bosia, Carla</creator><creator>Sgrò, Francesco</creator><creator>Conti, Laura</creator><creator>Baldassi, Carlo</creator><creator>Brusa, Davide</creator><creator>Cavallo, Federica</creator><creator>Cunto, Ferdinando Di</creator><creator>Turco, Emilia</creator><creator>Pagnani, Andrea</creator><creator>Zecchina, Riccardo</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>KPI</scope><scope>IAO</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8960-3443</orcidid></search><sort><creationdate>20170220</creationdate><title>RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells</title><author>Bosia, Carla ; Sgrò, Francesco ; Conti, Laura ; Baldassi, Carlo ; Brusa, Davide ; Cavallo, Federica ; Cunto, Ferdinando Di ; Turco, Emilia ; Pagnani, Andrea ; Zecchina, Riccardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c595t-6113399d0ff05e0e615f2db1537435f7815d20d4f72c5fd2b97826c8c75346343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Acids</topic><topic>Analysis</topic><topic>Biology</topic><topic>Cancer</topic><topic>Cell differentiation</topic><topic>Cell Survival - genetics</topic><topic>Competition</topic><topic>Controlled conditions</topic><topic>Experiments</topic><topic>Flow cytometry</topic><topic>Gene Expression</topic><topic>Gene Expression Regulation</topic><topic>Genes, Reporter</topic><topic>Genetic transcription</topic><topic>Mammalian cells</topic><topic>MicroRNA</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>Mimicry</topic><topic>miRNA</topic><topic>Physiology</topic><topic>Plasmids</topic><topic>Plasmids - genetics</topic><topic>Proteins</topic><topic>RNA Interference</topic><topic>RNA, Messenger - genetics</topic><topic>Single-Cell Analysis</topic><topic>Stochastic models</topic><topic>Transcription, Genetic</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bosia, Carla</creatorcontrib><creatorcontrib>Sgrò, Francesco</creatorcontrib><creatorcontrib>Conti, Laura</creatorcontrib><creatorcontrib>Baldassi, Carlo</creatorcontrib><creatorcontrib>Brusa, Davide</creatorcontrib><creatorcontrib>Cavallo, Federica</creatorcontrib><creatorcontrib>Cunto, Ferdinando Di</creatorcontrib><creatorcontrib>Turco, Emilia</creatorcontrib><creatorcontrib>Pagnani, Andrea</creatorcontrib><creatorcontrib>Zecchina, Riccardo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Global Issues</collection><collection>Gale Academic OneFile</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech 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>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</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 Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Genome Biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bosia, Carla</au><au>Sgrò, Francesco</au><au>Conti, Laura</au><au>Baldassi, Carlo</au><au>Brusa, Davide</au><au>Cavallo, Federica</au><au>Cunto, Ferdinando Di</au><au>Turco, Emilia</au><au>Pagnani, Andrea</au><au>Zecchina, Riccardo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells</atitle><jtitle>Genome Biology</jtitle><addtitle>Genome Biol</addtitle><date>2017-02-20</date><risdate>2017</risdate><volume>18</volume><issue>1</issue><spage>37</spage><epage>37</epage><pages>37-37</pages><artnum>37</artnum><issn>1474-760X</issn><issn>1474-7596</issn><eissn>1474-760X</eissn><abstract>Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other's expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range. We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other's fluctuations in a miRNA-dependent manner in single cells. We show that miRNA-target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10-1000 copies of targets per cell. Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>28219439</pmid><doi>10.1186/s13059-017-1162-x</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-8960-3443</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1474-760X
ispartof Genome Biology, 2017-02, Vol.18 (1), p.37-37, Article 37
issn 1474-760X
1474-7596
1474-760X
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5319025
source MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Springer Nature OA Free Journals; PubMed Central; SpringerLink Journals - AutoHoldings
subjects Acids
Analysis
Biology
Cancer
Cell differentiation
Cell Survival - genetics
Competition
Controlled conditions
Experiments
Flow cytometry
Gene Expression
Gene Expression Regulation
Genes, Reporter
Genetic transcription
Mammalian cells
MicroRNA
MicroRNAs
MicroRNAs - genetics
Mimicry
miRNA
Physiology
Plasmids
Plasmids - genetics
Proteins
RNA Interference
RNA, Messenger - genetics
Single-Cell Analysis
Stochastic models
Transcription, Genetic
title RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T06%3A50%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=RNAs%20competing%20for%20microRNAs%20mutually%20influence%20their%20fluctuations%20in%20a%20highly%20non-linear%20microRNA-dependent%20manner%20in%20single%20cells&rft.jtitle=Genome%20Biology&rft.au=Bosia,%20Carla&rft.date=2017-02-20&rft.volume=18&rft.issue=1&rft.spage=37&rft.epage=37&rft.pages=37-37&rft.artnum=37&rft.issn=1474-760X&rft.eissn=1474-760X&rft_id=info:doi/10.1186/s13059-017-1162-x&rft_dat=%3Cgale_pubme%3EA481900052%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2207907398&rft_id=info:pmid/28219439&rft_galeid=A481900052&rfr_iscdi=true