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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure</title>
<author><name sortKey="Flis, Anna" sort="Flis, Anna" uniqKey="Flis A" first="Anna" last="Flis">Anna Flis</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Fernandez, Aurora Pi As" sort="Fernandez, Aurora Pi As" uniqKey="Fernandez A" first="Aurora Pi As" last="Fernández">Aurora Pi As Fernández</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Zielinski, Tomasz" sort="Zielinski, Tomasz" uniqKey="Zielinski T" first="Tomasz" last="Zielinski">Tomasz Zielinski</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Mengin, Virginie" sort="Mengin, Virginie" uniqKey="Mengin V" first="Virginie" last="Mengin">Virginie Mengin</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Sulpice, Ronan" sort="Sulpice, Ronan" uniqKey="Sulpice R" first="Ronan" last="Sulpice">Ronan Sulpice</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Stratford, Kevin" sort="Stratford, Kevin" uniqKey="Stratford K" first="Kevin" last="Stratford">Kevin Stratford</name>
<affiliation><nlm:aff id="af3"><addr-line>EPCC</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>James Clerk Maxwell Building, Edinburgh EH9 3JZ</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Hume, Alastair" sort="Hume, Alastair" uniqKey="Hume A" first="Alastair" last="Hume">Alastair Hume</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af3"><addr-line>EPCC</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>James Clerk Maxwell Building, Edinburgh EH9 3JZ</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Pokhilko, Alexandra" sort="Pokhilko, Alexandra" uniqKey="Pokhilko A" first="Alexandra" last="Pokhilko">Alexandra Pokhilko</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af4"><addr-line>Institute of Molecular Cell and Systems Biology</addr-line>
,<institution>University of Glasgow</institution>
,<addr-line>Bower Building, Glasgow G12 8QQ</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Southern, Megan M" sort="Southern, Megan M" uniqKey="Southern M" first="Megan M." last="Southern">Megan M. Southern</name>
<affiliation><nlm:aff id="af5"><addr-line>Department of Biological Sciences</addr-line>
,<institution>University of Warwick</institution>
,<addr-line>Coventry CV4 7AL</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Seaton, Daniel D" sort="Seaton, Daniel D" uniqKey="Seaton D" first="Daniel D." last="Seaton">Daniel D. Seaton</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Mcwatters, Harriet G" sort="Mcwatters, Harriet G" uniqKey="Mcwatters H" first="Harriet G." last="Mcwatters">Harriet G. Mcwatters</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Stitt, Mark" sort="Stitt, Mark" uniqKey="Stitt M" first="Mark" last="Stitt">Mark Stitt</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Halliday, Karen J" sort="Halliday, Karen J" uniqKey="Halliday K" first="Karen J." last="Halliday">Karen J. Halliday</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Millar, Andrew J" sort="Millar, Andrew J" uniqKey="Millar A" first="Andrew J." last="Millar">Andrew J. Millar</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">26468131</idno>
<idno type="pmc">4632509</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632509</idno>
<idno type="RBID">PMC:4632509</idno>
<idno type="doi">10.1098/rsob.150042</idno>
<date when="2015">2015</date>
<idno type="wicri:Area/Pmc/Corpus">000067</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure</title>
<author><name sortKey="Flis, Anna" sort="Flis, Anna" uniqKey="Flis A" first="Anna" last="Flis">Anna Flis</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Fernandez, Aurora Pi As" sort="Fernandez, Aurora Pi As" uniqKey="Fernandez A" first="Aurora Pi As" last="Fernández">Aurora Pi As Fernández</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Zielinski, Tomasz" sort="Zielinski, Tomasz" uniqKey="Zielinski T" first="Tomasz" last="Zielinski">Tomasz Zielinski</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Mengin, Virginie" sort="Mengin, Virginie" uniqKey="Mengin V" first="Virginie" last="Mengin">Virginie Mengin</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Sulpice, Ronan" sort="Sulpice, Ronan" uniqKey="Sulpice R" first="Ronan" last="Sulpice">Ronan Sulpice</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Stratford, Kevin" sort="Stratford, Kevin" uniqKey="Stratford K" first="Kevin" last="Stratford">Kevin Stratford</name>
<affiliation><nlm:aff id="af3"><addr-line>EPCC</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>James Clerk Maxwell Building, Edinburgh EH9 3JZ</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Hume, Alastair" sort="Hume, Alastair" uniqKey="Hume A" first="Alastair" last="Hume">Alastair Hume</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af3"><addr-line>EPCC</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>James Clerk Maxwell Building, Edinburgh EH9 3JZ</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Pokhilko, Alexandra" sort="Pokhilko, Alexandra" uniqKey="Pokhilko A" first="Alexandra" last="Pokhilko">Alexandra Pokhilko</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="af4"><addr-line>Institute of Molecular Cell and Systems Biology</addr-line>
,<institution>University of Glasgow</institution>
,<addr-line>Bower Building, Glasgow G12 8QQ</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Southern, Megan M" sort="Southern, Megan M" uniqKey="Southern M" first="Megan M." last="Southern">Megan M. Southern</name>
<affiliation><nlm:aff id="af5"><addr-line>Department of Biological Sciences</addr-line>
,<institution>University of Warwick</institution>
,<addr-line>Coventry CV4 7AL</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Seaton, Daniel D" sort="Seaton, Daniel D" uniqKey="Seaton D" first="Daniel D." last="Seaton">Daniel D. Seaton</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Mcwatters, Harriet G" sort="Mcwatters, Harriet G" uniqKey="Mcwatters H" first="Harriet G." last="Mcwatters">Harriet G. Mcwatters</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Stitt, Mark" sort="Stitt, Mark" uniqKey="Stitt M" first="Mark" last="Stitt">Mark Stitt</name>
<affiliation><nlm:aff id="af1"><institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Halliday, Karen J" sort="Halliday, Karen J" uniqKey="Halliday K" first="Karen J." last="Halliday">Karen J. Halliday</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Millar, Andrew J" sort="Millar, Andrew J" uniqKey="Millar A" first="Andrew J." last="Millar">Andrew J. Millar</name>
<affiliation><nlm:aff id="af2"><addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</nlm:aff>
</affiliation>
</author>
</analytic>
<series><title level="j">Open Biology</title>
<idno type="eISSN">2046-2441</idno>
<imprint><date when="2015">2015</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p>Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in <italic>Arabidopsis thaliana</italic>
seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of <italic>PRR9</italic>
and <italic>ELF4</italic>
, and regulation of <italic>PRR5</italic>
by <italic>GI</italic>
. Absolute RNA quantification revealed low levels of <italic>PRR9</italic>
transcripts (peak approx. 50 copies cell<sup>−1</sup>
) compared with other clock genes, and threefold higher levels of <italic>LHY</italic>
RNA (more than 1500 copies cell<sup>−1</sup>
) than of its close relative <italic>CCA1</italic>
. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Open Biol</journal-id>
<journal-id journal-id-type="iso-abbrev">Open Biol</journal-id>
<journal-id journal-id-type="publisher-id">RSOB</journal-id>
<journal-id journal-id-type="hwp">royopenbio</journal-id>
<journal-title-group><journal-title>Open Biology</journal-title>
</journal-title-group>
<issn pub-type="epub">2046-2441</issn>
<publisher><publisher-name>The Royal Society</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26468131</article-id>
<article-id pub-id-type="pmc">4632509</article-id>
<article-id pub-id-type="doi">10.1098/rsob.150042</article-id>
<article-id pub-id-type="publisher-id">rsob150042</article-id>
<article-categories><subj-group subj-group-type="hwp-journal-coll"><subject>1001</subject>
<subject>181</subject>
<subject>197</subject>
<subject>22</subject>
<subject>129</subject>
</subj-group>
<subj-group subj-group-type="heading"><subject>Research</subject>
</subj-group>
<subj-group subj-group-type="leader"><subject>Research Articles</subject>
</subj-group>
</article-categories>
<title-group><article-title>Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure</article-title>
<alt-title alt-title-type="short">Dynamic expression of plant clock genes</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Flis</surname>
<given-names>Anna</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Fernández</surname>
<given-names>Aurora Piñas</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="author-notes" rid="AN1">†</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zielinski</surname>
<given-names>Tomasz</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mengin</surname>
<given-names>Virginie</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sulpice</surname>
<given-names>Ronan</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="author-notes" rid="AN2">‡</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Stratford</surname>
<given-names>Kevin</given-names>
</name>
<xref ref-type="aff" rid="af3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hume</surname>
<given-names>Alastair</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Pokhilko</surname>
<given-names>Alexandra</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Southern</surname>
<given-names>Megan M.</given-names>
</name>
<xref ref-type="aff" rid="af5">5</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Seaton</surname>
<given-names>Daniel D.</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>McWatters</surname>
<given-names>Harriet G.</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Stitt</surname>
<given-names>Mark</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="corresp" rid="cor1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Halliday</surname>
<given-names>Karen J.</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="corresp" rid="cor2"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Millar</surname>
<given-names>Andrew J.</given-names>
</name>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="corresp" rid="cor3"></xref>
</contrib>
</contrib-group>
<aff id="af1"><label>1</label>
<institution>Max Planck Institute of Molecular Plant Physiology</institution>
,<addr-line>Am Muehlenberg 1, 14476 Potsdam-Golm</addr-line>
,<country>Germany</country>
</aff>
<aff id="af2"><label>2</label>
<addr-line>SynthSys and School of Biological Sciences</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>C.H. Waddington Building, Edinburgh EH9 3JD</addr-line>
,<country>UK</country>
</aff>
<aff id="af3"><label>3</label>
<addr-line>EPCC</addr-line>
,<institution>University of Edinburgh</institution>
,<addr-line>James Clerk Maxwell Building, Edinburgh EH9 3JZ</addr-line>
,<country>UK</country>
</aff>
<aff id="af4"><label>4</label>
<addr-line>Institute of Molecular Cell and Systems Biology</addr-line>
,<institution>University of Glasgow</institution>
,<addr-line>Bower Building, Glasgow G12 8QQ</addr-line>
,<country>UK</country>
</aff>
<aff id="af5"><label>5</label>
<addr-line>Department of Biological Sciences</addr-line>
,<institution>University of Warwick</institution>
,<addr-line>Coventry CV4 7AL</addr-line>
,<country>UK</country>
</aff>
<author-notes><corresp id="cor1">e-mail: <email>mstitt@mpimp-golm.mpg.de</email>
</corresp>
<corresp id="cor2">e-mail: <email>karen.halliday@ed.ac.uk</email>
</corresp>
<corresp id="cor3">e-mail: <email>andrew.millar@ed.ac.uk</email>
</corresp>
<fn id="AN1" fn-type="present-address"><label>†</label>
<p>Present address: Biosciences KTN, The Roslin Institute, Easter Bush, Midlothian EH25 9RG, UK.</p>
</fn>
<fn id="AN2" fn-type="present-address"><label>‡</label>
<p>Present address: NUIG, Plant Systems Biology Laboratory, Plant and AgriBiosciences Research Centre, Botany and Plant Science, Galway, Ireland.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><month>10</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>14</day>
<month>10</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>14</day>
<month>10</month>
<year>2015</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
. </pmc-comment>
<volume>5</volume>
<issue>10</issue>
<elocation-id>150042</elocation-id>
<history><date date-type="received"><day>30</day>
<month>3</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>14</day>
<month>9</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-statement>© 2015 The Authors.</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="open-access" specific-use="vor" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>Published by the Royal Society under the terms of the Creative Commons Attribution License <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
, which permits unrestricted use, provided the original author and source are credited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:href="rsob-5-150042.pdf"></self-uri>
<abstract><p>Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in <italic>Arabidopsis thaliana</italic>
seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of <italic>PRR9</italic>
and <italic>ELF4</italic>
, and regulation of <italic>PRR5</italic>
by <italic>GI</italic>
. Absolute RNA quantification revealed low levels of <italic>PRR9</italic>
transcripts (peak approx. 50 copies cell<sup>−1</sup>
) compared with other clock genes, and threefold higher levels of <italic>LHY</italic>
RNA (more than 1500 copies cell<sup>−1</sup>
) than of its close relative <italic>CCA1</italic>
. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.</p>
</abstract>
<kwd-group><kwd>circadian rhythms</kwd>
<kwd>plant biology</kwd>
<kwd>gene regulatory networks</kwd>
<kwd>biological clocks</kwd>
<kwd>model optimization</kwd>
<kwd>data management</kwd>
</kwd-group>
<funding-group><award-group id="funding-1"><funding-source>Engineering and Physical Sciences Research Council<named-content content-type="funder-id">http://dx.doi.org/10.13039/501100000266</named-content>
</funding-source>
<award-id>High End Computing Programme</award-id>
</award-group>
</funding-group>
<funding-group><award-group id="funding-2"><funding-source>European Commission<named-content content-type="funder-id">http://dx.doi.org/10.13039/501100000780</named-content>
</funding-source>
<award-id>Collaborative Project TiMet / award 245143</award-id>
</award-group>
</funding-group>
<funding-group><award-group id="funding-3"><funding-source>Biotechnology and Biological Sciences Research Council<named-content content-type="funder-id">http://dx.doi.org/10.13039/501100000268</named-content>
</funding-source>
<award-id>ROBuST BB/F005237</award-id>
<award-id>SynthSys BB/D019621</award-id>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>cover-date</meta-name>
<meta-value>October 2015</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body><sec id="s1"><label>1.</label>
<title>Introduction</title>
<p>Circadian clocks are found widely among organisms from archaea to mammals [<xref rid="RSOB150042C1" ref-type="bibr">1</xref>
,<xref rid="RSOB150042C2" ref-type="bibr">2</xref>
]. These internal time-keepers generate approximately 24 h rhythms in the expression of 10–30% of genes, even without environmental cues. In natural conditions, circadian rhythms are entrained by light and temperature cycles. Their function is to coordinate internal processes with the external day/night cycle [<xref rid="RSOB150042C3" ref-type="bibr">3</xref>
,<xref rid="RSOB150042C4" ref-type="bibr">4</xref>
] and also, through photoperiodism, relative to the seasonal cycle [<xref rid="RSOB150042C5" ref-type="bibr">5</xref>
]. The circadian system of each organism includes a phylum-specific gene regulatory network that is required for most rhythmicity [<xref rid="RSOB150042C6" ref-type="bibr">6</xref>
], as well as non-transcriptional oscillator(s) that are less well characterized in eukaryotes [<xref rid="RSOB150042C7" ref-type="bibr">7</xref>
].</p>
<p>In plants, the clock gene network includes highly connected, negative regulators forming a complicated circuit. This has been best studied in <italic>Arabidopsis thaliana</italic>
. One simplification (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>a</italic>
) visualizes the circuit as a three-loop structure of morning and evening loops coupled around a repressilator [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
,<xref rid="RSOB150042C11" ref-type="bibr">11</xref>
]. The morning loop includes the MYB-related transcription factors LHY and CCA1, which activate expression of the pseudo-response regulators <italic>PRR9</italic>
and <italic>PRR7</italic>
[<xref rid="RSOB150042C12" ref-type="bibr">12</xref>
,<xref rid="RSOB150042C13" ref-type="bibr">13</xref>
], but inhibit expression of later-expressed genes including <italic>PRR5</italic>
and <italic>TOC1</italic>
(<italic>PRR1</italic>
). PRR9, PRR7, PRR5 and TOC1 bind to and inhibit <italic>LHY</italic>
and <italic>CCA1</italic>
expression, as predicted by modelling [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
,<xref rid="RSOB150042C14" ref-type="bibr">14</xref>
] and demonstrated by experiments [<xref rid="RSOB150042C15" ref-type="bibr">15</xref>
–<xref rid="RSOB150042C18" ref-type="bibr">18</xref>
]. LHY and CCA1 also inhibit expression of <italic>ELF3, ELF4</italic>
and <italic>LUX</italic>
(<italic>PCL1</italic>
), whose protein products interact to form another repressor, the evening complex (EC) [<xref rid="RSOB150042C19" ref-type="bibr">19</xref>
–<xref rid="RSOB150042C22" ref-type="bibr">22</xref>
]. The EC is thought to inhibit the expression of at least <italic>ELF4</italic>
and <italic>LUX</italic>
, forming a negative feedback loop, whose continued function might explain the damped oscillation of clock gene expression observed in <italic>lhy cca1</italic>
double mutant plants [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
]. GI, a large plant-specific protein, is also rhythmically expressed but functions at a post-translational level through, for example, stabilization of the TOC1-degradation factor ZTL [<xref rid="RSOB150042C23" ref-type="bibr">23</xref>
]. Light signalling controls multiple processes (electronic supplementary material, figure S1) that entrain the clock circuit to the day–night cycle. A growing number of identified processes and components remain to be fully integrated into the circuit, though even the components described are challenging to analyse.
<fig id="RSOB150042F1" orientation="portrait" position="float"><label>Figure 1.</label>
<caption><p>The clock gene network and experimental protocols. (<italic>a</italic>
) The clock gene network summarized in the activity-flow language of SBGN v. 1.0 [<xref rid="RSOB150042C8" ref-type="bibr">8</xref>
], with the principal connections in the P2012 model [<xref rid="RSOB150042C9" ref-type="bibr">9</xref>
]. The repressilator is denoted by green lines; morning loop components are filled yellow; <italic>LHY/CCA1</italic>
, red; evening loop components, blue. Light inputs are shown in electronic supplementary material, figure S1 and all modelled connections of P2011 [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
] in electronic supplementary material, figure S2. (<italic>b</italic>
) Peak-normalized RNA profiles of genes depicted in (<italic>a</italic>
), in plants of the Col-0 accession under a 12 h light : 12 h dark cycle (LD 12 : 12; experiment 2<italic>b</italic>
of panel (<italic>c</italic>
)). (<italic>c</italic>
) Graphical representation of the growth conditions. Experiments 1, 4, 5, 6 and 7 used seedlings grown in LD 12 : 12 for the number of days indicated; experiments 2 and 3 used plants grown on soil in LD 12 : 12 for the number of days indicated. Sucrose concentrations, growth temperatures and genotypes tested are shown for each experiment. Open box, light interval; black box, dark interval; light grey box, predicted darkness in constant light; dark grey box, predicted light in constant darkness; red box, red light. Sampling time in ZT (h), relative to lights-on of the first day of sampling or the last dawn before experimental treatment (ZT0). Ros, rosette; sd, seedling.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g1"></graphic>
</fig>
</p>
<p>Formal, mathematical models have been helpful in understanding the plant clock, because its components are highly interconnected by nonlinear regulation (electronic supplementary material, figure S2; reviewed in [<xref rid="RSOB150042C24" ref-type="bibr">24</xref>
]). Model development was necessarily based upon timeseries data, where the system was manipulated using mutations and by varying light or temperature inputs. More detailed models demanded greater precision and breadth in the data, which raised two major issues. First, data collation was laborious, because the numerical data underlying published timeseries graphs were rarely accessible [<xref rid="RSOB150042C25" ref-type="bibr">25</xref>
]. Although the potential benefits of data sharing are recognized [<xref rid="RSOB150042C26" ref-type="bibr">26</xref>
,<xref rid="RSOB150042C27" ref-type="bibr">27</xref>
], in practice, useful sharing requires cyber infrastructure, which is currently best-developed for omics data rather than the many focused studies in the clock literature [<xref rid="RSOB150042C28" ref-type="bibr">28</xref>
]. Second, the published data on <italic>Arabidopsis</italic>
clocks used several genetic backgrounds and growth conditions, introducing ill-defined variation to the results.</p>
<p>To provide directly comparable data, we conducted large-scale qRT-PCR assays for the RNA levels of multiple clock genes. Overlapping studies in four laboratories using different growth stages and conditions highlighted the robustness of most expression profiles and the few instances where they varied. Visualizing the data as phase plane plots suggested new dynamic interactions and their genetic regulators. Absolute RNA quantification revealed the low expression levels of <italic>ELF3</italic>
and <italic>PRR9</italic>
. To facilitate similar projects, we introduce data aggregation in the online BioDare resource, and illustrate the utility of our datasets by reoptimizing the P2011 clock model [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
] with the open-source application S<sc>ystems</sc>
B<sc>iology</sc>
S<sc>oftware</sc>
I<sc>nfrastructure</sc>
(SBSI) [<xref rid="RSOB150042C29" ref-type="bibr">29</xref>
], highlighting key areas for future experiments.</p>
</sec>
<sec id="s2"><label>2.</label>
<title>Results</title>
<sec id="s2a"><label>2.1.</label>
<title>Large-scale measurement of clock gene RNA profiles</title>
<p>This study was motivated by two projects that integrated circadian regulation into research on other plant physiological systems, which were incompatible with the growth conditions used in earlier circadian research. The Regulation of Biological Signalling by Temperature (ROBuST) project studied the interactions of ambient temperature with circadian and light signalling circuits; exogenous sucrose inhibits light signalling [<xref rid="RSOB150042C30" ref-type="bibr">30</xref>
,<xref rid="RSOB150042C31" ref-type="bibr">31</xref>
] and was therefore excluded. The Timing of Metabolism (TiMet) project studied circadian regulation of the starch pathway, among others, which is best characterized in rosette plants grown on soil. To measure the rhythmic expression in a set of clock-related genes (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>b</italic>
), we used automated systems in Golm and Edinburgh to quantify mRNA levels for components of the clock circuit every 2 h, in multiple conditions and mutant backgrounds [<xref rid="RSOB150042C32" ref-type="bibr">32</xref>
,<xref rid="RSOB150042C33" ref-type="bibr">33</xref>
] (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>c</italic>
). The ROBuST dataset tested 13-day-old, wild-type (WT) and mutant seedlings grown at 17°C on agar medium without additional sucrose. Datasets from the TiMet project tested 21-day-old rosette plants grown at 20°C on soil (TiMet ros) and 13-day-old seedlings on soil (TiMet sd1). The TiMet rosette data were collected from WT and clock mutant <italic>Arabidopsis thaliana</italic>
plants grown under light : dark (LD) cycles in two experiments, followed by constant light (LL) or constant dark (DD) in one study. Three further studies were compared, from seedlings grown on sterile agar media without sucrose (TiMet sd2, using the same medium as the ROBuST data), or with exogenous sucrose under white (McWatters, this paper; and Edwards <italic>et al</italic>
. [<xref rid="RSOB150042C34" ref-type="bibr">34</xref>
]) or red light (Southern, this paper; and [<xref rid="RSOB150042C21" ref-type="bibr">21</xref>
,<xref rid="RSOB150042C35" ref-type="bibr">35</xref>
]).</p>
</sec>
<sec id="s2b"><label>2.2.</label>
<title>Data presentation</title>
<p>Time is expressed as zeitgeber time (ZT) in hours since the last dark–light transition, by convention; the first dark–light transition within the sampling interval is 0 h on our plots. TiMet data are presented as absolute values [<xref rid="RSOB150042C33" ref-type="bibr">33</xref>
], obtained by calibrating RNA extraction efficiency with heterologous control RNAs (electronic supplementary matetial, table S1) to calculate the number of copies of each RNA per gram fresh weight (gFW). Estimated cell numbers per gFW (see electronic supplementary material) were used to calculate RNA copies per cell. The other datasets are normalized relative to a control transcript (<italic>ACTIN7</italic>
for ROBuST; <italic>ACTIN2</italic>
for Edwards and Southern; <italic>βTUBULIN4</italic>
for McWatters). <italic>ACTIN2</italic>
and <italic>GAPDH</italic>
controls were also assayed with two amplicons each in the TiMet assays, for comparison among datasets. Data were replicated in biological duplicate or triplicate samples and in equivalent sampling on successive days (0–12 h and 24–36 h in the TiMet and Edwards datasets). Data are presented on linear scales to reflect the potential for protein synthesis and hence regulatory effects on downstream targets (in keeping with most of the literature; figures <xref ref-type="fig" rid="RSOB150042F2">2</xref>
and <xref ref-type="fig" rid="RSOB150042F3">3</xref>
; electronic supplementary material, figure S5) and on logarithmic scales to reveal the full dynamic range of RNA expression, and hence the influence of multiple upstream regulators (figures <xref ref-type="fig" rid="RSOB150042F4">4</xref>
–<xref ref-type="fig" rid="RSOB150042F6">6</xref>
; electronic supplementary material, figure S3 and S4). Further technical comparison among the studies is presented in the electronic supplementary material.
<fig id="RSOB150042F2" orientation="portrait" position="float"><label>Figure 2.</label>
<caption><p>Clock gene expression in wild-type plants under LD cycles. Transcript levels in Col-0 and Ws-2 WT under LD 12 : 12 were measured by qRT-PCR, in experiment 2 (TiMet ros) including eight external RNA standards to allow absolute quantification in Col-0 and Ws-2 (<italic>a,c,e</italic>
) and in experiment 1 (ROBuST) normalized to the <italic>ACTIN7</italic>
control in Col-4 and Ws-2 (<italic>b,d,f</italic>
). Data represent transcripts of (<italic>a,b</italic>
) <italic>LHY</italic>
and <italic>CCA1</italic>
, (<italic>c,d</italic>
) <italic>PRR9</italic>
, and (<italic>e,f</italic>
) <italic>TOC1</italic>
and <italic>GI</italic>
. Error bars show SD, for two to three biological replicates. Electronic supplementary material, figure S3 shows the data on logarithmic plots.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g2"></graphic>
</fig>
<fig id="RSOB150042F3" orientation="portrait" position="float"><label>Figure 3.</label>
<caption><p>Waveforms of clock gene expression across experiments at different plant age and in the absence and presence of exogenous sucrose. This plot compares transcript abundance of <italic>CCA1</italic>
, <italic>TOC1</italic>
and <italic>GI</italic>
in 12 h photoperiods in three WTs grown in different experimental conditions in different laboratories. The data are taken from the following experiments (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
): WS ROBuST (1, seedlings), Col4 ROBuST (1, seedlings), Col0 suc Ed (6, seedlings provided with 3% exogenous sucrose), Col0 suc McW (5, seedlings provided with 3% sucrose), Col0 TiMet ros (2B, 21 day-old rosettes), WS TiMet ros (2, 21 day-old rosettes), WS TiMet sd1 (3, 10 day-old seedlings), WS TiMet sd2 (4, 13-day-old seedlings). All plants were entrained in LD 12 : 12 (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
). Values for each transcript are normalized to the peak. The results are the mean of duplicate or triplicate samples, double-plotted; error bars are not shown for clarity.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g3"></graphic>
</fig>
<fig id="RSOB150042F4" orientation="portrait" position="float"><label>Figure 4.</label>
<caption><p>Range of transcript abundance for clock genes in clock mutants. The bars show the highest and lowest mean values for the absolute abundance of transcripts for clock genes in a given genotype. The genotypes are, from left to right, Col-0 wild-type, <italic>gi-201</italic>
, <italic>prr9 prr7</italic>
double mutant, <italic>toc1</italic>
, WS WT, <italic>lhy cca1</italic>
double mutant (from experiments 2 and 2B of <xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>c</italic>
, 21-day-old rosettes) and WS (designated WS_2) and <italic>elf3</italic>
from experiment 3 (13-day-old seedlings), (<italic>a</italic>
) <italic>LHY</italic>
, (<italic>b</italic>
) <italic>CCA1</italic>
, (<italic>c</italic>
) <italic>PRR9</italic>
, (<italic>d</italic>
) <italic>PRR7</italic>
, (<italic>e</italic>
), <italic>PRR5</italic>
, (<italic>f</italic>
), <italic>TOC1</italic>
, (<italic>g</italic>
) <italic>LUX</italic>
, (<italic>h</italic>
) <italic>GI</italic>
, (<italic>i</italic>
) <italic>ELF3</italic>
, (<italic>j</italic>
) <italic>ELF4</italic>
. The underlying data are as in figures <xref ref-type="fig" rid="RSOB150042F5">5</xref>
and <xref ref-type="fig" rid="RSOB150042F6">6</xref>
.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g4"></graphic>
</fig>
<fig id="RSOB150042F5" orientation="portrait" position="float"><label>Figure 5.</label>
<caption><p>Clock gene expression in wild-type plants and clock mutants in LD, and after transition to constant light (LL) or darkness (DD). Col-0 and Ws-2 WT, the <italic>lhy-21 cca1-11</italic>
and <italic>prr7-3 prr9-1</italic>
double mutants, and the <italic>toc1-101</italic>
and <italic>gi-201</italic>
single mutants were grown in a 12 h photoperiod for 20 days, harvested through a LD cycle and then transferred to LL (<italic>a–j</italic>
) or DD (<italic>k–t</italic>
; TiMet ros, dataset 2 of <xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>c</italic>
). Transcript levels for clock genes were measured by qRT-PCR, including eight external RNA standards to allow absolute quantification. (<italic>a,k</italic>
) <italic>LHY</italic>
, (<italic>b,l</italic>
) <italic>CCA1</italic>
, (<italic>c,m</italic>
) <italic>PRR9</italic>
, (<italic>d,n</italic>
) <italic>PRR7</italic>
, (<italic>e,o</italic>
), <italic>PRR5</italic>
, (<italic>f,p</italic>
), <italic>TOC1</italic>
, (<italic>g,q</italic>
) <italic>LUX</italic>
, (<italic>h,r</italic>
) <italic>GI</italic>
, (<italic>i,s</italic>
) <italic>ELF3</italic>
, (<italic>j,t</italic>
) <italic>ELF4</italic>
. The results are the mean of duplicate samples, error bars show SD. Open box, light interval; black box, dark interval; light grey box, predicted darkness in LL; dark grey box, predicted light in DD.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g5"></graphic>
<graphic xlink:href="rsob-5-150042-g6"></graphic>
</fig>
<fig id="RSOB150042F6" orientation="portrait" position="float"><label>Figure 6.</label>
<caption><p>Clock gene expression in wild-type plants and <italic>elf3</italic>
mutants in LD. Ws-2 WT (solid lines) and <italic>elf3–4</italic>
mutant plants (dashed lines) were grown in a 12 h photoperiod for 12 days and harvested through one LD cycle (TiMet sd, dataset 3 of <xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>c</italic>
). Transcript levels for clock genes were measured by qRT-PCR, including eight external RNA standards to allow absolute quantification. (<italic>a</italic>
) <italic>LHY</italic>
, (<italic>b</italic>
) <italic>CCA1</italic>
, (<italic>c</italic>
) <italic>PRR9</italic>
, (<italic>d</italic>
) <italic>PRR7</italic>
, (<italic>e</italic>
), <italic>PRR5</italic>
, (<italic>f</italic>
), <italic>TOC1</italic>
, (<italic>g</italic>
) <italic>LUX</italic>
, (<italic>h</italic>
) <italic>GI</italic>
, (<italic>i</italic>
) <italic>ELF3</italic>
, (<italic>j</italic>
) <italic>ELF4</italic>
. The results are the mean of duplicate samples. Error bars show SD.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g7"></graphic>
</fig>
</p>
</sec>
<sec id="s2c"><label>2.3.</label>
<title>Similarity and specific variations of wild-type RNA profiles across datasets</title>
<p>Clock gene RNA expression profiles in WT plants of two accessions (Col and Ws-2) grown in LD are presented in <xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
; profiles were similar across the TiMet and ROBuST datasets, despite major differences in growth conditions. The morning clock components, <italic>CCA1</italic>
and <italic>LHY</italic>
, peaked as expected at dawn (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>a,b</italic>
), followed by <italic>PRR7</italic>
(ZT6; <xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>c,d</italic>
), <italic>PRR5</italic>
and <italic>GI</italic>
(ZT8; <xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>e–h</italic>
). Expression of the evening components, <italic>LUX, ELF4</italic>
and <italic>TOC1</italic>
, peaked at ZT8–12 (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>e–j</italic>
); peak expression of <italic>LUX</italic>
was delayed by about 2 h in Col plants relative to Ws-2 in both datasets (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>g,h</italic>
; replicated in LL data). <italic>ELF3</italic>
had a low-amplitude profile in both datasets, with lowest expression around ZT4.</p>
<p>The TiMet and ROBuST datasets differed at particular timepoints for <italic>PRR9, GI</italic>
and <italic>TOC1</italic>
. <italic>PRR9</italic>
expression was highest at ZT2–6 in both cases, with a clear peak at ZT2 in the ROBuST seedling data (consistent with many other reports from seedlings) but a broader profile in the TiMet data (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>c,d</italic>
). After its major peak at ZT8–12, <italic>TOC1</italic>
expression is consistently observed (since [<xref rid="RSOB150042C36" ref-type="bibr">36</xref>
]) to increase around ZT18, but the level of this night-time peak varied (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>e,f</italic>
). The ROBuST data for seedlings showed a peak of <italic>GI</italic>
expression at ZT2 (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>f</italic>
); little induction is evident at ZT2 in the TiMet rosette data on a linear scale (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>e</italic>
) though the logarithmic scale reveals the response (electronic supplementary material, figure S3<italic>e</italic>
). The morning peak in <italic>GI</italic>
is likely to be an acute response to lights-on. Rapid sampling in the Southern data [<xref rid="RSOB150042C35" ref-type="bibr">35</xref>
] and in a follow-up microarray study [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
] suggested that induction is rapid but transient, and therefore sensitive to sampling time. Nonetheless, the data suggest that either the magnitude or kinetics of light responsiveness vary across the conditions tested. The difference in <italic>PRR9</italic>
profiles could reflect slower activation of <italic>PRR9</italic>
in the TiMet data, consistent with lower light responsiveness in rosettes than in seedlings or with faster repression of <italic>PRR9</italic>
in seedlings. The level of <italic>GI</italic>
transcripts at ZT12 also varied from 4% to 40% of the peak level, with the lowest level in rosettes of Ws-2 (figures <xref ref-type="fig" rid="RSOB150042F2">2</xref>
<italic>e,f</italic>
and <xref ref-type="fig" rid="RSOB150042F3">3</xref>
<italic>c</italic>
). <italic>GI</italic>
expression is light sensitive at this phase [<xref rid="RSOB150042C37" ref-type="bibr">37</xref>
], so our results are consistent with variation in light responsiveness.</p>
<p>Sucrose modestly increases expression of the evening clock components <italic>TOC1</italic>
and <italic>GI</italic>
[<xref rid="RSOB150042C38" ref-type="bibr">38</xref>
], particularly in darkness [<xref rid="RSOB150042C39" ref-type="bibr">39</xref>
], and can repress <italic>PRR7</italic>
with subsequent effects on <italic>CCA1</italic>
under low light [<xref rid="RSOB150042C40" ref-type="bibr">40</xref>
], along with transcriptome-wide effects under LD cycles [<xref rid="RSOB150042C41" ref-type="bibr">41</xref>
,<xref rid="RSOB150042C42" ref-type="bibr">42</xref>
]. We therefore compared the expression profiles for <italic>CCA1, TOC1</italic>
and <italic>GI</italic>
in plants grown without (ROBuST and TiMet data) or with exogenous sucrose (McWatters, Edwards and Southern datasets; <xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
). To facilitate comparison, TiMet data were normalized to control transcripts (two amplicons each in <italic>GAPDH</italic>
and <italic>ACTIN2</italic>
), as for the other studies. Each profile was normalized to its maximum. Expression profiles of <italic>CCA1</italic>
across the different timeseries matched closely despite the differences in accession and experimental protocols (<xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
<italic>a</italic>
). The times of peak, mid-rising and mid-falling phases differed by at most 2 h (one sampling interval) among datasets. In the falling phase at ZT4, the profiles in McWatters, TiMet ros and TiMet sd2 data were delayed relative to the other data. The night-time expression of <italic>TOC1</italic>
at ZT18 varied from 20% to 60% of the main peak level (<xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
<italic>b</italic>
), with high expression in ROBuST, Edwards and TiMet sd2 datasets. The expression of <italic>GI</italic>
at ZT2 in the TiMet and Edwards seedling data was about 20% of the main peak level (<xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
<italic>c</italic>
, also in Southern data [<xref rid="RSOB150042C35" ref-type="bibr">35</xref>
]), intermediate between the levels in ROBuST and TiMet rosette data (discussed above). These features of the expression profiles showed no clear relationship with growth medium or developmental stage.</p>
</sec>
<sec id="s2d"><label>2.4.</label>
<title>Absolute quantification of clock gene transcripts</title>
<p>The absolute quantification in the TiMet ros data, which is based ultimately upon the certified amounts of synthetic commercial standards [<xref rid="RSOB150042C33" ref-type="bibr">33</xref>
], revealed wide variation in peak RNA levels among clock genes in WT plants (<xref ref-type="fig" rid="RSOB150042F4">figure 4</xref>
). Highest RNA levels were detected for <italic>LHY</italic>
at 1000–2100 copies per cell, similar to the control genes <italic>GAPDH</italic>
and <italic>ACT2</italic>
. <italic>PRR9</italic>
was least abundant at the peak, with 40–70 copies per cell; <italic>LUX</italic>
and <italic>ELF3</italic>
peaked at 105–130 copies per cell; <italic>PRR7, PRR5, GI</italic>
and <italic>TOC1</italic>
at 120–270 copies per cell; <italic>ELF4</italic>
and <italic>CCA1</italic>
at 250–600 copies per cell. RNA copy number of <italic>LHY</italic>
was threefold greater than that of <italic>CCA1</italic>
(<xref ref-type="fig" rid="RSOB150042F4">figure 4</xref>
<italic>a,b</italic>
).</p>
<p>Peak levels for the evening-expressed genes (<xref ref-type="fig" rid="RSOB150042F4">figure 4</xref>
<italic>f–j</italic>
) were slightly higher in Ws-2 than Col-0 plants, by 1.2-fold (<italic>LUX</italic>
) to 2.0-fold (<italic>ELF4</italic>
), average 1.6-fold. Several clock gene RNAs fell to low copy numbers per cell at the trough. Consequently, rhythmic amplitudes (defined here as peak divided by trough levels) also varied greatly among clock genes. The <italic>TOC1</italic>
and <italic>ELF3</italic>
profiles showed only eight- to 20-fold amplitude in Col-0, and generally smaller amplitudes in other, mutant genotypes than the other clock genes (<xref ref-type="fig" rid="RSOB150042F4">figure 4</xref>
<italic>f,i</italic>
), whereas <italic>LHY</italic>
, <italic>CCA1</italic>
, <italic>GI, ELF4</italic>
and <italic>PRR5</italic>
RNAs showed over 100-fold amplitude. This distinction was consistent in other datasets [<xref rid="RSOB150042C21" ref-type="bibr">21</xref>
,<xref rid="RSOB150042C34" ref-type="bibr">34</xref>
]. Amplitude estimates can be significantly affected by variation in the very low trough levels, which were higher in the TiMet sd1 dataset relative to the TiMet rosette data for <italic>LHY</italic>
and all the evening-expressed genes in the Ws-2 accession, for example (<xref ref-type="fig" rid="RSOB150042F4">figure 4</xref>
). Transcripts with high-amplitude profiles might be expected to control circadian timing more effectively than the low-amplitude profiles of <italic>TOC1</italic>
and <italic>ELF3</italic>
.</p>
</sec>
<sec id="s2e"><label>2.5.</label>
<title>Regulation of clock genes under environmental and genetic manipulation</title>
<p>The TiMet project measured clock gene expression in LL and DD following LD entrainment, in seedlings of two WT and four clock mutant backgrounds (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
), revealing novel aspects of clock gene regulation as well as replicating regulation observed in many earlier, smaller studies. The results are discussed below with respect to the upstream regulators of each gene, rather than the effect on the gene's downstream targets. The RNA data are therefore presented in semi-logarithmic plots that show regulator activity even at low RNA levels.</p>
<p>Comparing the three environmental conditions, peak RNA expression levels tended to fall in LL, consistent with the loss of dark-dependent regulation. The acute gene induction at the dark–light transition, faster degradation of PRR repressors in darkness and of the EC in the light are all expected to enhance rhythmic amplitude in LD. Expression levels of the clock RNAs were maintained in the first cycle in DD, except for the strongly light-regulated <italic>ELF4</italic>
[<xref rid="RSOB150042C43" ref-type="bibr">43</xref>
,<xref rid="RSOB150042C44" ref-type="bibr">44</xref>
]. Comparing the six genotypes, mutations that removed the repressors revealed the key connections in the clock circuit (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>a</italic>
). The <italic>gi</italic>
mutation, in contrast, had small or negligible effects on the timing and levels of expression except for <italic>PRR5</italic>
, as noted below.</p>
<sec id="s2e1"><label>2.5.1.</label>
<title><italic>LHY</italic>
and <italic>CCA1</italic>
</title>
<p>Our results are consistent with PRR repressors controlling both the rising and falling phases of <italic>LHY</italic>
and <italic>CCA1</italic>
expression at the transcriptional level [<xref rid="RSOB150042C14" ref-type="bibr">14</xref>
,<xref rid="RSOB150042C16" ref-type="bibr">16</xref>
–<xref rid="RSOB150042C18" ref-type="bibr">18</xref>
,<xref rid="RSOB150042C45" ref-type="bibr">45</xref>
]; several observations suggest that this activity is light-dependent. Both transcripts retain strikingly higher expression in the <italic>prr7;prr9</italic>
double mutant than in the WT, at ZT6–12 in LD and LL (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a,b</italic>
; <italic>p</italic>
< 0.05; 20- to 30-fold higher at ZT8), consistent with the absence of repression from PRR9 and PRR7 proteins. By the second day in LL, the trough of <italic>LHY</italic>
and <italic>CCA1</italic>
expression at ZT44 (68 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
) was also 20-fold higher than the WT trough level at ZT36–38 (60–62 h). Comparing LD and LL data with DD conditions revealed broader peaks of <italic>LHY</italic>
and <italic>CCA1</italic>
RNA in DD (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>k,l</italic>
), consistent with slower degradation of these transcripts in darkness [<xref rid="RSOB150042C34" ref-type="bibr">34</xref>
,<xref rid="RSOB150042C46" ref-type="bibr">46</xref>
]. In darkness, however, <italic>LHY</italic>
and <italic>CCA1</italic>
levels in the <italic>prr7;prr9</italic>
mutant behaved very similarly to the WT, both during the falling phase in DD (ZT28–38; <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>k,l</italic>
) and during the rising phase in LD (ZT16–22; <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a,b</italic>
). By dawn in LD, both transcripts peaked at the WT level, consistent with previous reports [<xref rid="RSOB150042C12" ref-type="bibr">12</xref>
,<xref rid="RSOB150042C13" ref-type="bibr">13</xref>
]. Thus, the misregulation of <italic>LHY</italic>
and <italic>CCA1</italic>
in the light in the <italic>prr7;prr9</italic>
double mutant was abolished during the dark in LD.</p>
<p>Removing TOC1, the last of the PRR repressors to be expressed, would be expected to allow an earlier rise in expression of <italic>LHY</italic>
and <italic>CCA1</italic>
during the night in the <italic>toc1</italic>
mutant under LD. This effect was relatively small (two- to 2.5-fold higher at ZT18, <italic>p</italic>
= 0.02). <italic>LHY</italic>
and <italic>CCA1</italic>
levels in <italic>toc1</italic>
mutants differed less than fourfold from WT at any point in LD. The mutant phenotype was not enhanced in the first DD cycle (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>k,l</italic>
). In LL, however, <italic>LHY</italic>
and <italic>CCA1</italic>
expression in the <italic>toc1</italic>
mutant peaked at ZT22 (46 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
) rather than at ZT26 (50 h) in Col, reached only 30–50% of WT peak level consistent with earlier data [<xref rid="RSOB150042C47" ref-type="bibr">47</xref>
], and fell much earlier than the WT (19- to 27-fold lower at ZT30, time 54 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a,b</italic>
). Thus, the molecular phenotypes of both <italic>prr7;prr9</italic>
and <italic>toc1</italic>
mutants were light-dependent.</p>
<p>The <italic>elf3</italic>
mutant reduced peak expression of both <italic>LHY</italic>
and <italic>CCA1</italic>
by five- to 10-fold (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>a,b</italic>
; electronic supplementary material, figure S5<italic>g</italic>
), with greatest effects at ZT20–24. This effect is thought to be indirect, as the EC (comprising ELF4, ELF3 and LUX) is proposed to repress the <italic>PRR</italic>
s (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>a</italic>
), as well as <italic>LUX</italic>
and <italic>ELF4</italic>
[<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
,<xref rid="RSOB150042C19" ref-type="bibr">19</xref>
,<xref rid="RSOB150042C20" ref-type="bibr">20</xref>
,<xref rid="RSOB150042C22" ref-type="bibr">22</xref>
]. De-repression of <italic>PRR</italic>
expression in mutants of the EC should therefore explain the effects of <italic>elf3</italic>
on <italic>LHY</italic>
and <italic>CCA1</italic>
.</p>
</sec>
<sec id="s2e2"><label>2.5.2.</label>
<title><italic>PRR9</italic>
and <italic>PRR7</italic>
</title>
<p><italic>PRR7</italic>
was the most severely affected gene in the <italic>elf3</italic>
mutant under LD, maintaining 25–85% of the WT peak level at all times (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>d</italic>
), consistent with de-repression of the <italic>PRR7</italic>
promoter [<xref rid="RSOB150042C21" ref-type="bibr">21</xref>
]. The resulting, 30- to 50-fold overexpression of <italic>PRR7</italic>
in <italic>elf3</italic>
at ZT20–24 is consistent with reduced expression of <italic>LHY</italic>
and <italic>CCA1</italic>
at this time<italic>. PRR9</italic>
transcript levels retained a 100-fold rhythmic amplitude under LD in the <italic>elf3</italic>
mutant, indicative of ELF3-independent regulation (see Discussion). Nonetheless, <italic>PRR9</italic>
expression was also de-repressed from ZT10 in <italic>elf3</italic>
(<italic>p</italic>
= 0.05), rising 2–4 h before dawn (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>c</italic>
), and presumably also contributing to reduce <italic>LHY</italic>
and <italic>CCA1</italic>
expression.</p>
<p>The early-expressed <italic>PRRs</italic>
are thought to be repressed by the later-expressed <italic>PRR5</italic>
and <italic>TOC1</italic>
(<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>a</italic>
). The <italic>toc1</italic>
mutation had modest effects on <italic>PRR9</italic>
or <italic>PRR7</italic>
profiles under LD cycles (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>c,d</italic>
), though the changes observed (such as an early rise in <italic>PRR7</italic>
at ZT20–24) were not consistently significant in the TiMet and ROBuST datasets, or in DD in the TiMet data (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>n</italic>
). <italic>toc1</italic>
also had little effect on <italic>LHY</italic>
and <italic>CCA1</italic>
levels in these conditions (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a,b</italic>
). In LL, however, removing TOC1 prevented full repression of the <italic>PRR</italic>
s. The trough of <italic>PRR7</italic>
expression was at a 10-fold higher level than in the WT (<italic>p</italic>
< 0.05) and 8 h earlier (ZT12 rather than ZT20, 36 h rather than 44 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>d</italic>
). Higher expression of the repressor <italic>PRR7</italic>
at 38–52 h (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>d</italic>
) is consistent with the lower peak expression of <italic>CCA1</italic>
and <italic>LHY</italic>
in <italic>toc1</italic>
under LL (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>a</italic>
[<xref rid="RSOB150042C9" ref-type="bibr">9</xref>
]). Taken together, these results suggested that TOC1 repressor function was most effective under constant light conditions, where the <italic>toc1</italic>
mutant was originally identified [<xref rid="RSOB150042C48" ref-type="bibr">48</xref>
].</p>
<p>Light-dependent regulation was also evident in WT plants. Peak <italic>PRR9</italic>
expression levels fell less than twofold in the first cycle of DD (<italic>p</italic>
> 0.16; <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>m</italic>
). Peak <italic>PRR7</italic>
expression tended to increase (threefold or less) in all genotypes in DD (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>n</italic>
; electronic supplementary material, figure S4<italic>c</italic>
). The <italic>gi</italic>
mutant was an exception, which slowed the rise of all the transcripts in DD except <italic>ELF3</italic>
and <italic>ELF4</italic>
(<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>k–t</italic>
; electronic supplementary material, figure S5<italic>d</italic>
). Peak expression for some genes was reduced in <italic>gi</italic>
below WT levels, including <italic>PRR7</italic>
(<italic>p</italic>
= 0.02–0.03 at ZT26–28 h). Trough RNA levels in the WT plants rose more dramatically in DD, for <italic>PRR7</italic>
and other clock genes (except for <italic>LHY</italic>
): the lowest expression of <italic>PRR7</italic>
in Col was 1.5 ± 0.4 copies per cell at ZT20 but 65 ± 6.8 copies per cell at ZT40 (electronic supplementary material, figure S4<italic>c</italic>
). The Edwards dataset showed similar de-repression of <italic>CCA1</italic>
and <italic>GI</italic>
trough levels in DD (electronic supplementary material, figure S4<italic>a</italic>
,<italic>b</italic>
[<xref rid="RSOB150042C34" ref-type="bibr">34</xref>
]). Lastly, we tested the effect of CCA1 and LHY on the <italic>PRR</italic>
transcripts, using the <italic>lhy;cca1</italic>
double mutant. In the WT, the repression of the evening-expressed genes by LHY and CCA1 in the early day delays the expression of these and other target genes until the evening. The double mutation advanced the peak phase of all the other clock genes to ZT2–4, as expected, except for <italic>ELF3</italic>
(see below). Despite the de-repression, peak levels were not consistently increased relative to the Ws-2 control. Peak expression of <italic>PRR9, PRR7</italic>
and <italic>PRR5</italic>
(<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>c–e</italic>
) was slightly reduced (up to twofold) in the <italic>lhy;cca1</italic>
background under LD, consistent with earlier results [<xref rid="RSOB150042C12" ref-type="bibr">12</xref>
]. By ZT8 (or ZT4 for <italic>PRR9</italic>
), all the clock genes were expressed at lower levels in <italic>lhy;cca1</italic>
than in the WT (<italic>p</italic>
< 0.01–0.04), consistent with expression of all the PRR repressors. In the <italic>lhy;cca1</italic>
double mutant in DD, however, the <italic>PRR</italic>
genes had broad peaks that rose earlier than in the WT (ZT22–30) but did not fall earlier (ZT34–40; <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>m–o</italic>
; electronic supplementary material, figure S4<italic>d</italic>
). The absence of early repression in DD again suggests that inter-regulation of the <italic>PRR</italic>
s is light-dependent.</p>
</sec>
<sec id="s2e3"><label>2.5.3.</label>
<title><italic>PRR5</italic>
and <italic>TOC1</italic>
</title>
<p>The later-expressed <italic>PRR</italic>
s are repressed by LHY and CCA1, so longer expression of <italic>LHY</italic>
and <italic>CCA1</italic>
in the <italic>prr7;prr9</italic>
double mutants delayed their expression in LD and LL conditions (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>e,f</italic>
), as expected. In contrast, under DD conditions, <italic>PRR5</italic>
expression in <italic>prr7;prr9</italic>
rose indistinguishably from the WT at ZT26–34 h and peaked slightly (twofold) above the WT level (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>o</italic>
). The <italic>lhy;cca1</italic>
double mutant caused the phase advance noted above, as the loss of LHY and CCA1 repressors increased <italic>TOC1</italic>
levels in the early day. Peak <italic>TOC1</italic>
RNA levels in the <italic>lhy;cca1</italic>
mutant did not change consistently from WT levels in the TiMet data under LD (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>f</italic>
), and were lower than the WT in the ROBuST dataset (<italic>p</italic>
< 0.01; electronic supplementary material, figure S5<italic>c</italic>
).</p>
<p>Our detailed datasets also allowed us to compare expression waveforms. For example, <italic>PRR5</italic>
rises and falls 10-fold within 5 h in both TiMet and ROBuST data (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>e,o</italic>
; electronic supplementary material, figures S3<italic>g</italic>
,<italic>h</italic>
, S5<italic>a</italic>
). This narrow peak indicates highly nonlinear control, consistent with negative autoregulation and/or inhibition by TOC1 [<xref rid="RSOB150042C15" ref-type="bibr">15</xref>
,<xref rid="RSOB150042C49" ref-type="bibr">49</xref>
]. Moreover, our results indicate that this <italic>PRR5</italic>
waveform depends upon GI function. The <italic>gi-201</italic>
mutant had limited effects overall but slowed the fall in <italic>PRR5</italic>
mRNA in LD and LL (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>e</italic>
), creating an asymmetric profile in <italic>PRR5</italic>
RNA that was also observed in the <italic>gi-11</italic>
tested in the ROBuST data (electronic supplementary material, figure S5<italic>a</italic>
,<italic>b</italic>
). Repression by the EC might also contribute to the falling phase of the <italic>PRR5</italic>
profile. Removing this repression in the <italic>elf3</italic>
mutant resulted in moderate de-repression of <italic>PRR5</italic>
and <italic>TOC1</italic>
in the late night (<italic>p</italic>
< 0.01, ZT0/24; <italic>p</italic>
= 0.01 for <italic>PRR5</italic>
ZT22) and potentially in the early morning (<italic>p</italic>
= 0.06–0.08; ZT2–4; <xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>e,f</italic>
). In contrast, de-repression of the early <italic>PRR</italic>
s in <italic>elf3</italic>
was greatest in the early night (see above), indicating that the profile of regulators varies among the PRR family members (see Discussion).</p>
</sec>
<sec id="s2e4"><label>2.5.4.</label>
<title>GI</title>
<p>The main peak of <italic>GI</italic>
expression in the late day behaves similarly to <italic>PRR5</italic>
, with delayed expression in the <italic>prr7;prr9</italic>
double mutant owing to longer expression of LHY and CCA1 under LD and LL but not DD, and an advanced phase in the <italic>lhy;cca1</italic>
double mutant (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>h,p</italic>
). In contrast to <italic>PRR5</italic>
but similarly to <italic>PRR9</italic>
and <italic>PRR7, GI</italic>
was de-repressed from ZT10 in the <italic>elf3</italic>
mutant (<italic>p</italic>
< 0.01), consistent with [<xref rid="RSOB150042C21" ref-type="bibr">21</xref>
] and the Southern dataset (electronic supplementary material, figure S5<italic>f</italic>
). The Southern dataset showed that the expression of <italic>GI</italic>
was similar in <italic>elf3</italic>
and <italic>elf4</italic>
mutants, but there was much less effect on <italic>CCA1</italic>
in <italic>elf4</italic>
than <italic>elf3</italic>
(electronic supplementary material, figure S5<italic>g</italic>
), indicating that the effects of the EC components can be distinct.</p>
</sec>
<sec id="s2e5"><label>2.5.5.</label>
<title>ELF3</title>
<p>The <italic>ELF3</italic>
rhythmic profile has low amplitude, as noted above, with a trough at ZT2–4 and peaks at both ZT8 and ZT18–20 in WT plants under LD in the TiMet and ROBuST datasets (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>i,s</italic>
; electronic supplementary material, figures S3<italic>i</italic>
–<italic>j</italic>
, S5<italic>d</italic>
). The trough of ELF3 expression is de-repressed at ZT4 in the <italic>lhy;cca1</italic>
double mutant (<italic>p</italic>
< 0.01), though there is no peak at this time, in contrast to all the other clock genes. The rise in <italic>ELF3</italic>
expression is delayed in the <italic>prr7;prr9</italic>
double mutant (<italic>p</italic>
< 0.01–0.05, at ZT6–10), consistent with repression by increased levels of LHY and CCA1 (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>i</italic>
). The <italic>elf3–4</italic>
allele contains a small deletion in the coding region [<xref rid="RSOB150042C50" ref-type="bibr">50</xref>
] and accumulates the mutant RNA. The mutant expression profile suggests de-repression at ZT2 (<italic>p</italic>
= 0.06; <xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>i</italic>
), consistent with lower expression of <italic>LHY</italic>
and <italic>CCA1</italic>
in <italic>elf3</italic>
(noted above).</p>
</sec>
<sec id="s2e6"><label>2.5.6.</label>
<title><italic>ELF4</italic>
and <italic>LUX</italic>
</title>
<p>The two remaining EC components tested, <italic>ELF4</italic>
and <italic>LUX,</italic>
share the evening expression peak determined by <italic>LHY/CCA1-</italic>
mediated repression, with a phase advance in <italic>lhy;cca1</italic>
and a delay in <italic>prr7;prr9</italic>
in LD and LL conditions (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>g,j</italic>
). Strikingly, however, the phase separation among the clock genes was lost in the <italic>lhy;cca1</italic>
double mutant under LL, such that <italic>PRR9</italic>
and <italic>ELF4</italic>
peaked together at 50 and 66 h (discussed below). Thus, LHY and CCA1 contribute to the 4 h separation of peak times between <italic>PRR9</italic>
(54 h) and <italic>ELF4</italic>
(58 h) in the Ws-2 control under LL. In DD, peak expression of <italic>ELF4</italic>
was the most reduced of all the genes, to less than 10% of the LD peak level (<italic>p</italic>
< 0.01 in Col and Ws; <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>t</italic>
), consistent with the loss of light activation [<xref rid="RSOB150042C44" ref-type="bibr">44</xref>
] and/or sugar signalling. <italic>ELF4</italic>
was also de-repressed earlier in the <italic>toc1</italic>
mutant under DD than the other genes (ZT28–36 h; <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>j</italic>
), rising as early as in the <italic>lhy;cca1</italic>
double mutant. Under LD conditions, the <italic>toc1</italic>
mutant de-repressed <italic>ELF4</italic>
at ZT2–6, earlier than WT. Peak expression of <italic>LUX</italic>
did not fall significantly in DD (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>q</italic>
).</p>
<p><italic>LUX</italic>
was broadly de-repressed in the <italic>elf3</italic>
mutant, remaining at the WT peak level at ZT6–22 h (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>g</italic>
), in a similar pattern to <italic>PRR7</italic>
. This result is consistent with LUX binding to its cognate promoter [<xref rid="RSOB150042C20" ref-type="bibr">20</xref>
] resulting in negative autoregulation (<xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>a</italic>
[<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
]). <italic>ELF4</italic>
expression in the <italic>elf3</italic>
mutant, in contrast, showed a pattern more similar to <italic>TOC1</italic>
and <italic>PRR5</italic>
(see above)<italic>,</italic>
with de-repression only from ZT22–ZT6 h (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>j</italic>
).</p>
</sec>
</sec>
<sec id="s2f"><label>2.6.</label>
<title>Alternative visualization gives new insights into co-regulation of clock genes</title>
<p>Data visualization is critical in analysing the complex interactions within the clock gene circuit, in order to generate new hypotheses. Timeseries plots do not show these interactions directly. They can be revealed in phase plane diagrams that plot the levels of two components against each other (<xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
), though this format is less familiar (see electronic supplementary material). First, phase plane plots emphasize the relative timing of clock components, rather than control by the light : dark cycle. For example, <italic>GI</italic>
rose without (before) <italic>TOC1</italic>
, especially in Col plants of the TiMet and ROBuST datasets that were grown without exogenous sucrose. High <italic>TOC1</italic>
levels extended later than high <italic>GI</italic>
, particularly in Ws-2 plants of the TiMet datasets (<xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>a</italic>
). Second, this visualization can reveal interactions among the components plotted. For example, <xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>b</italic>
shows <italic>TOC1</italic>
RNA levels in younger plants were maintained at 35–55% of the peak level at ZT20–22, when <italic>CCA1</italic>
expression rose above 50% of its peak level. <italic>TOC1</italic>
levels were lower for the same <italic>CCA1</italic>
level in rosette plants. The logarithmic scale shows this more clearly (<xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>c</italic>
). This suggests that CCA1 protein is not yet an effective repressor of <italic>TOC1</italic>
at this phase, especially in younger tissues.
<fig id="RSOB150042F7" orientation="portrait" position="float"><label>Figure 7.</label>
<caption><p>Phase plane diagrams reveal pairwise gene interactions. (<italic>a–c</italic>
) Normalized RNA profiles of <xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
are represented as phase plane diagrams, plotting (<italic>a</italic>
) <italic>GI</italic>
and <italic>TOC1</italic>
, and <italic>TOC1</italic>
and <italic>CCA1</italic>
on (<italic>b</italic>
) linear and (<italic>c</italic>
) logarithmic scales. Larger markers indicate ZT0 datapoint, arrows indicate the direction of time. (<italic>d–f</italic>
) RNA profiles of <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
are represented as phase plane diagrams on logarithmic scales, plotting data for <italic>ELF4</italic>
and <italic>PRR9</italic>
(<italic>d</italic>
) in wild-type Col plants under LD and LL (0–22 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
, dashed line; 24–70 h, solid line), and (<italic>e</italic>
) in Col plants under LD and <italic>lhy cca1</italic>
double mutants under LD and LL (solid blue line), with (<italic>f</italic>
) a rescaled view of a subset of the data from the <italic>lhy cca1</italic>
double mutants. Larger markers indicate 0 (ZT0) and 12 h (ZT12) datapoints in the cycle labelled LD. These timepoints are equivalent to 24 (ZT0) and 36 h (ZT12) in the cycle labelled LL. Arrows indicate the direction of time. (<italic>d</italic>
) Red dashed line marks falling <italic>ELF4</italic>
levels during the night-time trough of <italic>PRR9</italic>
in LD<italic>.</italic>
(<italic>f</italic>
) Red dashed line marks correlated <italic>PRR9</italic>
and <italic>ELF4</italic>
levels; arrowheads mark an earlier peak on each cycle in <italic>PRR9.</italic>
Timepoints 48 (ZT24) to 70 h (ZT46) under LL are plotted in brown to emphasize the similar profiles on successive days.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g8"></graphic>
</fig>
</p>
<p>Finally, the phase plane diagrams can show how the interaction of two genes depends upon a third regulator. Expression peaks of <italic>PRR9</italic>
and <italic>ELF4</italic>
were far out of phase in the WT (<xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>d</italic>
), for example. Data from LL (filled symbols) suggest a negative correlation in the subjective night, when <italic>ELF4</italic>
falls as <italic>PRR9</italic>
rises. However, the two genes peak then fall together in the <italic>lhy cca1</italic>
double mutant under LL, at ZT26 and ZT42 (<xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>e</italic>
; equivalent to timepoints 50 and 66 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
), creating a diagonal with a positive gradient (red dashed line, <xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>f</italic>
). <italic>PRR9</italic>
also had an earlier peak that was not shared by <italic>ELF4</italic>
(ZT22 and ZT38, or 46 and 62 h in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
; black arrowheads in <xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>f</italic>
). Both features were reproduced on two successive cycles, though <italic>PRR9</italic>
expression was less than 1% of the WT peak level. Thus <italic>LHY</italic>
, <italic>CCA1</italic>
and the LD cycle all differentiate <italic>PRR9</italic>
expression from <italic>ELF4</italic>
, but in their absence, <italic>PRR9</italic>
and <italic>ELF4</italic>
expression profiles are similar for much of the circadian cycle (six of eight timepoints in the short, 16 h cycle of the mutant), presumably controlled by the other PRRs and/or the EC. Likewise, phase plane diagrams for the <italic>prr7;prr9</italic>
double mutant (electronic supplementary material, figure S6) suggested that not only CCA1 and LHY, but also the PRRs repress <italic>ELF4</italic>
in the WT. In addition to visualization, many other aspects of data management benefit significantly from online data infrastructure.</p>
</sec>
<sec id="s2g"><label>2.7.</label>
<title>Online infrastructure for data sharing</title>
<p>Our open-source BioDare (Biological Data repository) [<xref rid="RSOB150042C51" ref-type="bibr">51</xref>
] supports data from many small-scale experiments that collectively represent a significant resource (<xref ref-type="table" rid="RSOB150042TB1">table 1</xref>
). Empirical evidence indicates that these data are essential to understand complex biological regulation, and mathematical analysis shows why this is the case (see Discussion). In addition to six rhythm-analysis algorithms [<xref rid="RSOB150042C52" ref-type="bibr">52</xref>
] and protocols for analysis, statistical summary and visualization [<xref rid="RSOB150042C53" ref-type="bibr">53</xref>
], BioDare facilitates data sharing and public dissemination by providing a stable identifier for each experiment. Detailed metadata (experimental description) ensure that the data can be reused appropriately. Results can be compared across studies and laboratories (‘data aggregation’) by searching the metadata for genotype, marker gene and other terms (<xref ref-type="fig" rid="RSOB150042F8">figure 8</xref>
). Increased expression of <italic>GI</italic>
in the <italic>elf3</italic>
mutant, for example, is highlighted despite the greater technical variability of manual assay preparation in the Southern dataset compared with the later, robotized assays in the TiMet data (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>h</italic>
; electronic supplementary material, figure S5<italic>f</italic>
and Methods).
<fig id="RSOB150042F8" orientation="portrait" position="float"><label>Figure 8.</label>
<caption><p>Computational infrastructure for systems chronobiology. Customized wizards in the P<sc>edro</sc>
XML editor capture detailed metadata (right panel, showing <italic>CCA1</italic>
<italic>:</italic>
<italic>LUC</italic>
in sample wizard). Rather than filling 3705 metadata fields for this experiment, as a naive spreadsheet would require, P<sc>edro</sc>
captures the information with only 156 entries. After uploading the metadata and numerical data to BioDare, results can be displayed in the web browser (centre panel) with powerful secondary processing functions. The left-hand sidebar in this screen has shortcuts to common tasks and recent activity. A naive text search for ‘CCA1’ returned 394 experiments (exp'ts), whereas BioDare's ‘aggregate’ function retrieved six specific results by searching the structured metadata, with secondary filters. The search shown (right panel) aggregated qPCR assays of <italic>CCA1</italic>
in wild-type plants (see main text) including datasets 1, 3, 4 and 6 of <xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
<italic>c</italic>
. The export button above the graph downloads the data shown to a spreadsheet-compatible file.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g9"></graphic>
</fig>
<table-wrap id="RSOB150042TB1" orientation="portrait" position="float"><label>Table 1.</label>
<caption><p>Usage statistics of BioDare (Feb 2015), from originating groups and selected external users. An experiment represents a dataset similar to one of the above-described studies, which includes multiple timeseries, from samples of multiple genotypes, assays or reporters and/or environmental conditions. Totals include minor users that are not listed individually; the total number of data points is over 41 million.</p>
</caption>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
</colgroup>
<thead valign="bottom"><tr><th align="left" rowspan="1" colspan="1">research group</th>
<th align="left" rowspan="1" colspan="1">location</th>
<th align="left" rowspan="1" colspan="1">experiments</th>
<th align="left" rowspan="1" colspan="1">% total experiments</th>
<th align="left" rowspan="1" colspan="1">timeseries</th>
<th align="left" rowspan="1" colspan="1">% total timeseries</th>
</tr>
</thead>
<tbody><tr><td rowspan="1" colspan="1">A. J. Millar</td>
<td rowspan="1" colspan="1">Edinburgh, UK</td>
<td rowspan="1" colspan="1">332</td>
<td rowspan="1" colspan="1">14</td>
<td rowspan="1" colspan="1">41 890</td>
<td rowspan="1" colspan="1">18</td>
</tr>
<tr><td rowspan="1" colspan="1">A. Hall</td>
<td rowspan="1" colspan="1">Liverpool, UK</td>
<td rowspan="1" colspan="1">261</td>
<td rowspan="1" colspan="1">11</td>
<td rowspan="1" colspan="1">79 228</td>
<td rowspan="1" colspan="1">34</td>
</tr>
<tr><td rowspan="1" colspan="1">D. Bell-Pedersen</td>
<td rowspan="1" colspan="1">Texas A&M, USA</td>
<td rowspan="1" colspan="1">138</td>
<td rowspan="1" colspan="1">6</td>
<td rowspan="1" colspan="1">1428</td>
<td rowspan="1" colspan="1">1</td>
</tr>
<tr><td rowspan="1" colspan="1">J. Agren</td>
<td rowspan="1" colspan="1">Uppsala, Sweden</td>
<td rowspan="1" colspan="1">18</td>
<td rowspan="1" colspan="1">1</td>
<td rowspan="1" colspan="1">9370</td>
<td rowspan="1" colspan="1">4</td>
</tr>
<tr><td rowspan="1" colspan="1">K .J. Halliday</td>
<td rowspan="1" colspan="1">Edinburgh, UK</td>
<td rowspan="1" colspan="1">230</td>
<td rowspan="1" colspan="1">10</td>
<td rowspan="1" colspan="1">5043</td>
<td rowspan="1" colspan="1">2</td>
</tr>
<tr><td rowspan="1" colspan="1">L. Larrondo</td>
<td rowspan="1" colspan="1">Santiago, Chile</td>
<td rowspan="1" colspan="1">75</td>
<td rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1">6429</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr><td rowspan="1" colspan="1">M. Jones</td>
<td rowspan="1" colspan="1">Essex, UK</td>
<td rowspan="1" colspan="1">89</td>
<td rowspan="1" colspan="1">4</td>
<td rowspan="1" colspan="1">3148</td>
<td rowspan="1" colspan="1">1</td>
</tr>
<tr><td rowspan="1" colspan="1">M. Hastings</td>
<td rowspan="1" colspan="1">MRC LMB, UK</td>
<td rowspan="1" colspan="1">1071</td>
<td rowspan="1" colspan="1">45</td>
<td rowspan="1" colspan="1">58 770</td>
<td rowspan="1" colspan="1">25</td>
</tr>
<tr><td rowspan="1" colspan="1">S. Harmer</td>
<td rowspan="1" colspan="1">UC Davis, USA</td>
<td rowspan="1" colspan="1">37</td>
<td rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1">11 353</td>
<td rowspan="1" colspan="1">5</td>
</tr>
<tr><td rowspan="1" colspan="1">S. A. Kay</td>
<td rowspan="1" colspan="1">USC, USA</td>
<td rowspan="1" colspan="1">38</td>
<td rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1">12 972</td>
<td rowspan="1" colspan="1">6</td>
</tr>
<tr><td rowspan="1" colspan="1">All BioDare</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">2344</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">232 844</td>
<td rowspan="1" colspan="1"></td>
</tr>
</tbody>
</table>
</table-wrap>
</p>
</sec>
<sec id="s2h"><label>2.8.</label>
<title>Optimizing clock models with public resources</title>
<p>One goal of such comparisons is to determine how much of the available data is matched by a particular mathematical model: the ROBuST and TiMet experiments were designed to test models of the clock gene circuit under different growth conditions. However, testing complex models against large datasets requires skills that are rare among plant molecular researchers. We therefore tested whether our comprehensive data and better computational resources could make modelling more accessible. The open-source SBSI allows non-programmers to optimize model parameters in order to match diverse data, on large, parallel computers [<xref rid="RSOB150042C29" ref-type="bibr">29</xref>
]. As a test case, we addressed a recognized limitation of the original P2011 model [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
], termed P2011.1.1. The model was developed to understand circadian clock function under light–dark cycles and, separately, under constant light. Following a transition from LD to LL (as in <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a–j</italic>
), the first peak in expression of the combined <italic>LHY/CCA1</italic>
component under constant light occurred at ZT28.4 h (52.4 h in <xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>a</italic>
), about 2.5 h later than in the TiMet ros data (as noted [<xref rid="RSOB150042C25" ref-type="bibr">25</xref>
,<xref rid="RSOB150042C54" ref-type="bibr">54</xref>
]). The model's light–dark function was replaced with the input signal step function [<xref rid="RSOB150042C55" ref-type="bibr">55</xref>
] to represent the LD–LL transition in the community model exchange format, SBML [<xref rid="RSOB150042C56" ref-type="bibr">56</xref>
]. The resulting model P2011.1.2 was optimized in SBSI (see electronic supplementary material), testing model simulations with many alternative parameter sets against the TiMet ros RNA dataset, including the LD–LL transition (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a–j</italic>
), and against circadian period values for clock mutants and WT plants [<xref rid="RSOB150042C29" ref-type="bibr">29</xref>
].</p>
<p>The optimized parameter set of model P2011.2.1 more closely matched the data, including an earlier peak of <italic>LHY/CCA1</italic>
in LL at ZT26.5 h (<xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
<italic>a</italic>
) and a closer match to <italic>TOC1</italic>
and <italic>GI</italic>
profiles in LD (ZT10–12 h; <xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
<italic>b,c</italic>
), while retaining other qualitative behaviours. <italic>LHY/CCA1</italic>
expression rises in LL after the PRR repressor proteins are degraded. Consistent with this notion, removing <italic>TOC1</italic>
, the last gene in the <italic>PRR</italic>
repressor wave, advanced the phase of the entire clock mechanism in LL. Results for <italic>PRR7</italic>
are shown in <xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
<italic>d,e</italic>
. PRR protein degradation rates were not strongly affected in P2011.2.1; rather, overall PRR levels were lower than in P2011.1.2 (not shown). In the simulated <italic>toc1</italic>
mutant, the peak of <italic>LHY/CCA1</italic>
was 1.4 h earlier than simulated WT in P2011.1.2, 2.5 h earlier in P2011.2.1, but 4 h earlier in the data (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>a,b</italic>
). The simulations of <italic>PRR7</italic>
show the same improved timing of the new model for the WT (<xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
<italic>d</italic>
) and the <italic>toc1</italic>
mutant in LD (<xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
<italic>e</italic>
), but an earlier phase of the <italic>toc1</italic>
mutant data under LL. Regulatory interactions among the <italic>PRR</italic>
genes will repay further analysis [<xref rid="RSOB150042C9" ref-type="bibr">9</xref>
,<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
] in future models (see Discussion).
<fig id="RSOB150042F9" orientation="portrait" position="float"><label>Figure 9.</label>
<caption><p>Model re-optimization. Comparison of measured transcript levels from <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
(experimental data, symbols), with simulation of models P2011.1.2 (old model, dotted line) and P2011.2.1 (new model, solid line), which resulted from fitting to these data using SBSI. 0–24 h, LD; 24–72 h, LL. (<italic>a</italic>
) <italic>LHY</italic>
and <italic>CCA1</italic>
transcripts are combined in the model, so the average of <italic>LHY</italic>
and <italic>CCA1</italic>
data is plotted. The peak of <italic>LHY/CCA1</italic>
under LL was delayed in the P2011.1.2 model (52.4 h) relative to the peak in the data (50 h), which was closely matched by the P2011.2.1 model (50.5 h). (<italic>b</italic>
) <italic>GI</italic>
transcript, (<italic>c</italic>
) <italic>TOC1</italic>
transcript and (<italic>d</italic>
) <italic>PRR7</italic>
transcript in Col-0 WT. (<italic>e</italic>
) <italic>PRR7</italic>
transcript in the <italic>toc1</italic>
mutant shows a greater phase-advance in LL than either model. Chi-square cost value for match to TiMet ros Col-0 data in LD-LL was 20.2 for P2011.1.2, 7.6 for P2011.2.1. Chi-square cost for match to TiMet ros <italic>toc1</italic>
data in LD-LL was 39.7 for P2011.1.2, 13.1 for P2011.2.1.</p>
</caption>
<graphic xlink:href="rsob-5-150042-g10"></graphic>
</fig>
</p>
<p>The computation time required for P2011.2.1 was only approximately 30 core-hours, because the model parameters were varied within only a narrow range (two- to threefold change) from their starting values in P2011.1.2 [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
]. The P2011.1.2 parameters had been manually determined to match a wide range of data and qualitative behaviours in the clock literature; many were derived from the parent model P2010 [<xref rid="RSOB150042C14" ref-type="bibr">14</xref>
]. When the first model of a system is developed, in contrast, most or all parameter values may be unknown. We therefore tested our approach in such scenarios (<xref ref-type="table" rid="RSOB150042TB2">table 2</xref>
). Allowing parameter values to differ by up to 100-fold from the values in P2011.1.2 created a very large parameter space that was nonetheless centred on a known, viable region. In contrast, starting parameters from nominal values (0.1, 1, etc.) and testing each parameter over the same range (such as 0.001–10) removed that anchor. Viable parameter sets that gave cost values similar to the unmodified P2011.2.1 were identified in each test, with computation times up to four core years for P2011.6.1, using the UK national supercomputing resource HECToR. These parameter sets are not intended to replace P2011.1.2 but to demonstrate that similar results can be achieved by a more accessible approach using the TiMet data and SBSI, without new programming or laborious, manual model development. The P2011 model versions and the cognate graphical network diagram (electronic supplementary material, figure S2) are publicly accessible from the PlaSMo repository and elsewhere (see appendix A).
<table-wrap id="RSOB150042TB2" orientation="portrait" position="float"><label>Table 2.</label>
<caption><p>Optimization of model parameters from loose constraints. The starting P2011.1.2 model was optimized in SBSI to fit the TiMet ros dataset and additional period constraints (see electronic supplementary material, Methods). Model, version number of the resulting model. PlaSMo ID, model identifier in the PlaSMo resource. Job, computational job code. Start, the default parameters values from P2011.1.2 or nominal values (Nom). Range, the range of parameter values that were searched, either as fold change above and below the P2011.1.2 values or as a fixed range. Set-up trials, the number of randomly chosen parameter sets tested to initialize the optimization. Cost, the best cost value (closest fit to all constraints).</p>
</caption>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
</colgroup>
<thead valign="bottom"><tr><th align="left" rowspan="1" colspan="1">model</th>
<th align="left" rowspan="1" colspan="1">PlaSMo ID</th>
<th align="left" rowspan="1" colspan="1">internal job ID</th>
<th align="left" rowspan="1" colspan="1">start</th>
<th align="left" rowspan="1" colspan="1">range</th>
<th align="left" rowspan="1" colspan="1">set-up trials</th>
<th align="left" rowspan="1" colspan="1">cost</th>
</tr>
</thead>
<tbody><tr><td rowspan="1" colspan="1">P2011.1.2</td>
<td rowspan="1" colspan="1">PLM_71 ver 1</td>
<td rowspan="1" colspan="1">—</td>
<td rowspan="1" colspan="1">—</td>
<td rowspan="1" colspan="1">—</td>
<td rowspan="1" colspan="1">—</td>
<td rowspan="1" colspan="1">171</td>
</tr>
<tr><td rowspan="1" colspan="1">P2011.2.1</td>
<td rowspan="1" colspan="1">PLM_71 ver 2</td>
<td rowspan="1" colspan="1">.599</td>
<td rowspan="1" colspan="1">P2011.1.2</td>
<td rowspan="1" colspan="1">2–3×</td>
<td rowspan="1" colspan="1">5000</td>
<td rowspan="1" colspan="1">77</td>
</tr>
<tr><td rowspan="1" colspan="1">P2011.3.1</td>
<td rowspan="1" colspan="1">PLM_1041 ver 1</td>
<td rowspan="1" colspan="1">t30</td>
<td rowspan="1" colspan="1">P2011.1.2</td>
<td rowspan="1" colspan="1">100×</td>
<td rowspan="1" colspan="1">2 097 152</td>
<td rowspan="1" colspan="1">175</td>
</tr>
<tr><td rowspan="1" colspan="1">P2011.4.1</td>
<td rowspan="1" colspan="1">PLM_1042 ver 1</td>
<td rowspan="1" colspan="1">t34</td>
<td rowspan="1" colspan="1">Nom</td>
<td rowspan="1" colspan="1">0.001–10</td>
<td rowspan="1" colspan="1">67 108 864</td>
<td rowspan="1" colspan="1">270</td>
</tr>
<tr><td rowspan="1" colspan="1">P2011.5.1</td>
<td rowspan="1" colspan="1">PLM_1043 ver 1</td>
<td rowspan="1" colspan="1">t37</td>
<td rowspan="1" colspan="1">Nom</td>
<td rowspan="1" colspan="1">0.001–10</td>
<td rowspan="1" colspan="1">67 108 864</td>
<td rowspan="1" colspan="1">190</td>
</tr>
<tr><td rowspan="1" colspan="1">P2011.6.1</td>
<td rowspan="1" colspan="1">PLM_1044 ver 1</td>
<td rowspan="1" colspan="1">t40</td>
<td rowspan="1" colspan="1">Nom</td>
<td rowspan="1" colspan="1">0.0005–20</td>
<td rowspan="1" colspan="1">134 217 728</td>
<td rowspan="1" colspan="1">185</td>
</tr>
</tbody>
</table>
</table-wrap>
</p>
</sec>
</sec>
<sec id="s3"><label>3.</label>
<title>Discussion</title>
<sec id="s3a"><label>3.1.</label>
<title>Robust regulation of clock gene expression</title>
<p>Quantitative timeseries data are crucial to understand the dynamics of any moderately complex regulatory system. As understanding advances, more precise questions can be formulated that demand both consistent and comprehensive datasets. We provide such data for the RNA profiles of genes associated with the <italic>Arabidopsis</italic>
circadian clock, with an online resource to facilitate comparisons within and across datasets. Our experiments were designed to test clock function under the distinct conditions required for separate studies, on light signalling (in the ROBuST project) and carbon metabolism (in the TiMet project), using different technical platforms. The results presumably include the variation previously observed among experiments designed to be replicated across laboratories [<xref rid="RSOB150042C57" ref-type="bibr">57</xref>
]. We compared two <italic>Arabidopsis</italic>
accessions. Significant differences in circadian timing have been demonstrated among <italic>Arabidopsis</italic>
accessions, albeit using long-term, imaging assays that integrate the effects of small timing changes over many cycles [<xref rid="RSOB150042C58" ref-type="bibr">58</xref>
–<xref rid="RSOB150042C60" ref-type="bibr">60</xref>
]. Importantly, the rhythmic RNA profiles tested here were remarkably consistent (<xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
). Progress in understanding the clock gene network must, in part, be attributed to this robustness of circadian regulation.</p>
<p>Several clock genes are regulated with high daily amplitude, more than 100-fold for <italic>LHY, CCA1, GI, ELF4</italic>
and <italic>PRR5</italic>
under LD (<xref ref-type="fig" rid="RSOB150042F4">figure 4</xref>
; electronic supplementary material, figures S3 and S4 [<xref rid="RSOB150042C21" ref-type="bibr">21</xref>
,<xref rid="RSOB150042C34" ref-type="bibr">34</xref>
]), falling to low RNA copy numbers per cell. Our data necessarily reflect the mean expression across cells in the rosette, greater than 80% of which are in the leaf mesophyll [<xref rid="RSOB150042C61" ref-type="bibr">61</xref>
]. Nonetheless, the absolute calibration of our RNA assays provides one approach for future estimation of the average copy number for the cognate proteins.</p>
<p>The most striking variations of RNA profiles among WT plants involved the acutely light-responsive genes <italic>GI</italic>
and <italic>PRR9</italic>
. The ROBuST dataset showed the highest levels of <italic>GI</italic>
and strong induction of <italic>PRR9</italic>
at ZT2 (figures <xref ref-type="fig" rid="RSOB150042F2">2</xref>
and <xref ref-type="fig" rid="RSOB150042F3">3</xref>
). This is consistent with strong light induction, which might be mediated by direct photoreceptor signalling and/or by indirect sugar signalling. The absence of exogenous sucrose in the ROBuST conditions was not the sole cause, as the TiMet sd2 data used the same, sucrose-free media but did not show such strong <italic>GI</italic>
induction (<xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
<italic>c</italic>
). The lower growth temperature in ROBuST conditions (17°C rather than 20–22°C in other datasets) might also increase light responsiveness. Consistent with this notion, both exogenous sucrose and higher ambient temperature limit other light responses [<xref rid="RSOB150042C30" ref-type="bibr">30</xref>
,<xref rid="RSOB150042C31" ref-type="bibr">31</xref>
].</p>
</sec>
<sec id="s3b"><label>3.2.</label>
<title>Regulation of the <italic>PRR</italic>
repressors</title>
<p>RNA profiles of the <italic>PRR</italic>
gene family varied among datasets in the WT under LD, as well as among conditions and genotypes. The variable expression of <italic>TOC1</italic>
around ZT18 (<xref ref-type="fig" rid="RSOB150042F3">figure 3</xref>
<italic>b</italic>
) awaits a mechanistic explanation, as do the de-repression of multiple genes in DD (for example, <xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>n</italic>
) and of <italic>PRR5</italic>
in the <italic>gi</italic>
mutant (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>e</italic>
; electronic supplementary material, figure S5<italic>b</italic>
). TOC1 is thought to be an active repressor at ZT18, so variable auto-repression is possible and might also explain variation in <italic>PRR5</italic>
expression at this phase (<xref ref-type="fig" rid="RSOB150042F2">figure 2</xref>
<italic>g</italic>
). Alternatively, <italic>TOC1</italic>
expression might rise during a transition between one repressor in the early night (such as the EC) and another in the late night (such as LHY and CCA1).</p>
<p>The tight interconnections among the clock components complicate the analysis of these data, though the resulting combination of direct and indirect effects is now interpretable. For example, removing EC regulation in the <italic>elf3</italic>
mutant de-repressed the direct EC targets <italic>PRR9</italic>
and <italic>PRR7</italic>
in the early night, when the EC is active in WT plants. <italic>PRR5</italic>
and <italic>TOC1</italic>
were noted as potential targets based on mutant RNA profiles [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
], but both genes were de-repressed around dawn in <italic>elf3</italic>
, suggesting that an indirect mechanism owing to lower LHY and CCA1 levels is more significant than the loss of direct regulation by the EC in the mutant. <italic>PRR9</italic>
and <italic>PRR7</italic>
are both proposed EC targets (along with <italic>ELF4</italic>
and <italic>LUX</italic>
), yet <italic>PRR9</italic>
(and <italic>ELF4</italic>
) retains rhythmic regulation in the <italic>elf3</italic>
mutant under LD, whereas <italic>PRR7</italic>
(and <italic>LUX</italic>
) is more severely affected (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
). To understand such differences in response, it will now be important to measure the affinity of regulators for their target genes, extending initial data [<xref rid="RSOB150042C62" ref-type="bibr">62</xref>
]. Previous modelling results indicated that the different daily profiles of the <italic>PRR</italic>
genes allow flexible responses to dawn and dusk [<xref rid="RSOB150042C14" ref-type="bibr">14</xref>
], so the mechanisms that generate the <italic>PRR</italic>
profiles will repay further analysis [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
,<xref rid="RSOB150042C11" ref-type="bibr">11</xref>
].</p>
<p>Several results suggested that regulation by the <italic>PRR</italic>
genes is light-dependent. First, in the <italic>prr7;prr9</italic>
double mutant, <italic>LHY</italic>
and <italic>CCA1</italic>
expression was de-repressed during the day but returned to match the WT profile at night in LD (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
). One explanation might be that PRR9 and PRR7 (directly or indirectly) antagonize the light activation of <italic>LHY</italic>
and <italic>CCA1</italic>
during the day in the WT [<xref rid="RSOB150042C14" ref-type="bibr">14</xref>
,<xref rid="RSOB150042C63" ref-type="bibr">63</xref>
], and the absence of these PRR proteins in the double mutant has little effect in darkness. Consistent with this notion, the <italic>prr9</italic>
single mutant also showed a day-time de-repression of <italic>CCA1</italic>
in the ROBuST dataset (electronic supplementary material, figure S5<italic>e</italic>
), albeit less than in the double mutant. However, the <italic>CCA1</italic>
profile in the <italic>prr7</italic>
single mutant was unaffected in the daytime, but de-repressed 2 h earlier in the night (electronic supplementary material, figure S5<italic>e</italic>
). Thus, inter-regulation of the early <italic>PRR</italic>
genes is important, in addition to regulation by <italic>TOC1</italic>
[<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
]. Second, in the <italic>lhy;cca1</italic>
double mutant, <italic>PRR</italic>
gene expression is repressed to low levels at the end of the day in LD, consistent with simultaneous, early expression of all the PRR repressors in these mutant plants. In DD, however, the falling phase of <italic>PRR</italic>
expression is the same in WT and double mutant plants (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
). The higher and earlier expression of the <italic>PRR</italic>
RNAs in the double mutant in DD does not appear to be effective in suppressing <italic>PRR</italic>
expression. The faster degradation of the PRR proteins in darkness presumably contributes to these effects; it will be interesting to determine whether the interaction of the photoreceptor PHYB with clock proteins (including TOC1 [<xref rid="RSOB150042C64" ref-type="bibr">64</xref>
]) also mediates the light sensitivity of this process.</p>
</sec>
<sec id="s3c"><label>3.3.</label>
<title>Effects of exogenous sucrose</title>
<p>Current models of the <italic>Arabidopsis</italic>
circadian clock are necessarily based on disparate data, much of it derived from seedlings grown on media containing high levels of sucrose. The presence or absence of exogenous sucrose under the conditions tested here affected the clock RNA profiles less, or at least no more, than other experimental parameters, despite the widespread regulation of plant genes by sucrose [<xref rid="RSOB150042C41" ref-type="bibr">41</xref>
,<xref rid="RSOB150042C65" ref-type="bibr">65</xref>
]. Consistent with this, effects of exogenous sucrose on clock gene expression in WT plants have previously been reported under very low light fluence rates or in the presence of photosynthetic inhibitors [<xref rid="RSOB150042C40" ref-type="bibr">40</xref>
], in DD, CO<sub>2</sub>
-free air or the starchless <italic>pgm</italic>
mutant [<xref rid="RSOB150042C39" ref-type="bibr">39</xref>
,<xref rid="RSOB150042C66" ref-type="bibr">66</xref>
,<xref rid="RSOB150042C67" ref-type="bibr">67</xref>
]. <italic>PRR7</italic>
was induced in sugar-starved conditions (extended DD and at night in <italic>pgm</italic>
) and was repressed by resupply of 3% exogenous sucrose (electronic supplementary material, figure S7<italic>a</italic>
). Only the TiMet rosette study tested <italic>PRR7</italic>
in DD, finding increased <italic>PRR7</italic>
levels (<xref ref-type="fig" rid="RSOB150042F5">figure 5</xref>
<italic>n</italic>
), especially in the trough of the profile (electronic supplementary material, figure S4<italic>c</italic>
). Trough levels of <italic>CCA1</italic>
and <italic>GI</italic>
were also raised in DD in the TiMet data, and in the Edwards experiment that included 3% exogenous sucrose (electronic supplementary material, figure S3 [<xref rid="RSOB150042C34" ref-type="bibr">34</xref>
]). De-repression of the trough levels in DD is neither specific to <italic>PRR7</italic>
nor to sugar limitation. Transcript levels of the TOC1- and PRR5-degrading F-box protein ZTL, and its homologues LKP2 and FKF1, also rose slightly in sugar-starved conditions (electronic supplementary material, figure S7<italic>b</italic>
[<xref rid="RSOB150042C67" ref-type="bibr">67</xref>
]), suggesting one possible mechanism for de-repression of <italic>PRR7</italic>
via faster degradation of PRR repressors.</p>
</sec>
<sec id="s3d"><label>3.4.</label>
<title>Open resources for small-scale results</title>
<p>Our results will be useful to generate and test many hypotheses beyond those reported here. The potential for such future value might, in principle, justify the additional effort in curating and disseminating our data. In practice, future value motivated little data sharing, compared with present value. We therefore outline the mathematical understanding of and empirical evidence for such present value, together with practical steps that increased both present and future value relative to the effort involved in sharing data.</p>
<p>No suitable community repository existed for our results. One reason was the relatively large effort required to describe accurately many small data files, which deters researchers and resource developers from sharing such data [<xref rid="RSOB150042C68" ref-type="bibr">68</xref>
]. The largest-scale omics and sequencing studies have different data structures, motivations, stakeholders and economics, which can facilitate data sharing [<xref rid="RSOB150042C69" ref-type="bibr">69</xref>
] including exemplary resources in the circadian field [<xref rid="RSOB150042C70" ref-type="bibr">70</xref>
–<xref rid="RSOB150042C72" ref-type="bibr">72</xref>
]. However, mathematical analysis explains why the results of small-scale experiments are often particularly valuable in understanding biological systems. Gutenkunst <italic>et al.</italic>
[<xref rid="RSOB150042C73" ref-type="bibr">73</xref>
] showed that parameters were ‘sloppy’ in dynamic models of a range of biological regulatory systems, meaning that a wide range of parameter values could generate the simple behaviours that they tested. Rand <italic>et al.</italic>
[<xref rid="RSOB150042C74" ref-type="bibr">74</xref>
–<xref rid="RSOB150042C76" ref-type="bibr">76</xref>
] tested how many parameter changes could affect the dynamic behaviour of such systems. All possible behaviours were tested and only a handful of behaviours could be readily achieved by changing parameters (these behaviours have also been termed the ‘dynatype’ of the system, by analogy to the phenotype of an organism [<xref rid="RSOB150042C77" ref-type="bibr">77</xref>
]). For circadian clocks, a change in period was the most accessible behaviour: many different parameter changes altered period under constant conditions [<xref rid="RSOB150042C74" ref-type="bibr">74</xref>
]. The related, empirical result is that genetic screens seeking mutants with altered circadian period have not only identified clock components, but also many genes that affect the clock less directly [<xref rid="RSOB150042C78" ref-type="bibr">78</xref>
,<xref rid="RSOB150042C79" ref-type="bibr">79</xref>
]. Observing a change in period gives little evidence for the role of the mutated gene in the plant and does not strongly constrain any particular parameter in the model, but rather has a small constraining effect upon a large number of parameters, in agreement with Gutenkunst <italic>et al.</italic>
[<xref rid="RSOB150042C73" ref-type="bibr">73</xref>
]. A measured period value can therefore easily be accommodated without fundamentally changing the model. In contrast, manipulating the system to test less accessible behaviours provides strong constraints, albeit potentially on fewer parameters [<xref rid="RSOB150042C76" ref-type="bibr">76</xref>
]. It is much more likely that such results would not be accommodated by any reasonable parameter values, falsifying the current model and leading to new understanding during the development of a better model. Thus, the number of manipulations tested is crucial; model analysis can prioritize the most informative manipulations [<xref rid="RSOB150042C80" ref-type="bibr">80</xref>
,<xref rid="RSOB150042C81" ref-type="bibr">81</xref>
].</p>
<p>One consequence for experimental design is that the number of manipulations is more important than the number of components tested. This concept is familiar from the statistical clustering of microarray timeseries. The behaviour of a single cluster mean can adequately represent hundreds of individual transcripts, even for genes with complex light and circadian regulation [<xref rid="RSOB150042C82" ref-type="bibr">82</xref>
]. The individual transcript data are more valuable in identifying coregulated, downstream genes than in understanding the clock system upstream. Thus, targeted qRT-PCR or reporter gene assays have been more widely used in understanding the clock gene circuit, although they lacked a data-sharing resource. Despite the limited justification for costly omic assays, targeted data-sharing resources [<xref rid="RSOB150042C67" ref-type="bibr">67</xref>
,<xref rid="RSOB150042C70" ref-type="bibr">70</xref>
] have ensured that a subset of transcriptomics data have been reused effectively in clock studies.</p>
<p>Empirical evidence for the value of multiple manipulations comes from 10 years of modelling the plant clock gene circuit and output pathways. Constraining the models with timeseries data from many conditions was a critical tool [<xref rid="RSOB150042C83" ref-type="bibr">83</xref>
], resulting in multiple, experimentally validated predictions. Gleaning the data from electronic supplementary material or by ‘scraping’ numerical values from published charts made this possible. In practice, aggregating the numerical data has often taken a major effort, after which the data were shared on author web sites [<xref rid="RSOB150042C25" ref-type="bibr">25</xref>
,<xref rid="RSOB150042C84" ref-type="bibr">84</xref>
] or on BioDare [<xref rid="RSOB150042C82" ref-type="bibr">82</xref>
,<xref rid="RSOB150042C85" ref-type="bibr">85</xref>
,<xref rid="RSOB150042C86" ref-type="bibr">86</xref>
].</p>
<p>BioDare [<xref rid="RSOB150042C51" ref-type="bibr">51</xref>
] was developed to share timeseries data from relatively small-scale experiments conducted within individual laboratories (such as the Edwards, Southern and McWatters datasets) or in collaborative projects with few partners (such as ROBuST and TiMet). A regular user might upload an experiment with several hundred timeseries each week [<xref rid="RSOB150042C87" ref-type="bibr">87</xref>
,<xref rid="RSOB150042C88" ref-type="bibr">88</xref>
]. However, the user must also provide experimental metadata that are sufficiently detailed to pinpoint the most relevant experiment among hundreds to thousands of similar studies (<xref ref-type="table" rid="RSOB150042TB1">table 1</xref>
). The resource must therefore streamline the process of writing the structured metadata to minimize the weekly effort involved, and then use the metadata to provide powerful search functions, for later users to discover relevant data that were previously unknown to them. <xref ref-type="fig" rid="RSOB150042F8">Figure 8</xref>
illustrates metadata capture using ‘wizard’ forms in BioDare, data aggregation based upon the resulting metadata, and visualization of the data from a small set of relevant experiments, whereas a naive text search returned an impractically large number of results.</p>
<p>The potential future value of shared data resulted in fewer than a dozen datasets being shared in the early phases of our projects. To provide immediate value from depositing data, BioDare therefore offers data processing (detrending, averaging) and visualization along with specialized circadian data analysis [<xref rid="RSOB150042C52" ref-type="bibr">52</xref>
,<xref rid="RSOB150042C53" ref-type="bibr">53</xref>
]. Stable identifier URLs conveniently direct collaborators to specific datasets and can be cited in publications [<xref rid="RSOB150042C88" ref-type="bibr">88</xref>
,<xref rid="RSOB150042C89" ref-type="bibr">89</xref>
]. The citations will be tracked by the Thomson Reuters Data Citation Index, giving a metric analogous to publication citations to recognize data-sharing contributions [<xref rid="RSOB150042C90" ref-type="bibr">90</xref>
]. BioDare is available as a community resource that could be linked to organism-specific databases [<xref rid="RSOB150042C91" ref-type="bibr">91</xref>
]. BioDare complements our repository of plant systems models (PlaSMo) [<xref rid="RSOB150042C92" ref-type="bibr">92</xref>
].</p>
</sec>
<sec id="s3e"><label>3.5.</label>
<title>From visualization to modelling</title>
<p>Our analysis here was model-assisted but manual, so data visualization was important. For example, phase-plane diagrams can reveal conditional pairwise interactions including subtle effects at low RNA levels, such as the correlation of <italic>PRR9</italic>
and <italic>ELF4</italic>
expression in the <italic>lhy cca1</italic>
double mutant under LL (<xref ref-type="fig" rid="RSOB150042F6">figure 6</xref>
<italic>d–f</italic>
). In contrast, <italic>PRR9</italic>
and <italic>ELF4</italic>
expression are uncorrelated or anticorrelated in the WT under LD. Such changes in dynamics are important in forming hypotheses during model development. Expert modelling has a subjective element. Objective machine-learning methods can also to contribute to hypothesis generation [<xref rid="RSOB150042C93" ref-type="bibr">93</xref>
], though understanding such a conditionally connected network (electronic supplementary material, figure S2) is challenging by any approach [<xref rid="RSOB150042C94" ref-type="bibr">94</xref>
].</p>
<p>Dense transcriptional regulatory interactions might be general for plant environmental response pathways [<xref rid="RSOB150042C95" ref-type="bibr">95</xref>
], justifying investment in infrastructure to support their analysis. Mathematical models can powerfully express hypotheses about such circuits, so long as the starting model adequately recapitulates most data. Qualitatively, the variation among our datasets was smaller than the departure of the model simulations from the data (figures <xref ref-type="fig" rid="RSOB150042F3">3</xref>
and <xref ref-type="fig" rid="RSOB150042F9">9</xref>
). The existing circadian clock models are therefore equally applicable to the several growth conditions tested, at least in leaf tissue.</p>
<p>The transition from LD to LL is one case where the model departed from the data, to which it had not previously been constrained (also noted in references [<xref rid="RSOB150042C54" ref-type="bibr">54</xref>
,<xref rid="RSOB150042C88" ref-type="bibr">88</xref>
]). The P2011.2.1 model's 2 h late phase in LL (<xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
) is caused by the slower degradation of PRR proteins in the light than in the dark [<xref rid="RSOB150042C16" ref-type="bibr">16</xref>
,<xref rid="RSOB150042C45" ref-type="bibr">45</xref>
]. Without a dark night to reduce PRR levels, their slow degradation delays the rise in <italic>LHY/CCA1</italic>
on the first cycle in LL in the model. <italic>PRR9, PRR7</italic>
and <italic>PRR5</italic>
RNA levels are reduced in the second cycle in LL in both model and data (figures <xref ref-type="fig" rid="RSOB150042F5">5</xref>
<italic>c–e</italic>
and 9<italic>e</italic>
), restoring an approximately 24 h period in subsequent cycles. It is reassuring but not surprising that re-optimization of the model could better match this behaviour, but the models' detailed behaviour is non-trivial. Reducing the levels of PRR proteins in the new parameter set advanced the phase of the first peak in LL. Simplified models that included only the PRR protein changes also reduced the effect of the PRRs on the period of the clock in constant light (data not shown), contradicting the data. The re-optimization allowed multiple parameter changes to advance the phase of the P2011.2.1 model under LL while retaining the observed effects of PRRs on clock period, such as the short period of the <italic>toc1</italic>
mutant (<xref ref-type="fig" rid="RSOB150042F9">figure 9</xref>
<italic>e</italic>
).</p>
<p>Most significantly, this result was obtained using tools designed to be accessible to biological researchers with no specialist computing or mathematical skills. Development of P2011.2.1 required no new programming, nor the hand-crafted cost functions that were used to optimize previous models [<xref rid="RSOB150042C25" ref-type="bibr">25</xref>
,<xref rid="RSOB150042C83" ref-type="bibr">83</xref>
–<xref rid="RSOB150042C85" ref-type="bibr">85</xref>
], nor the laborious, expert parameter exploration used to construct its parent models [<xref rid="RSOB150042C10" ref-type="bibr">10</xref>
,<xref rid="RSOB150042C14" ref-type="bibr">14</xref>
,<xref rid="RSOB150042C96" ref-type="bibr">96</xref>
]. Our intention was that the scarcity of these skills should no longer present an insuperable barrier, though of course they remain beneficial, not least to keep abreast of relevant method development [<xref rid="RSOB150042C80" ref-type="bibr">80</xref>
]. To test whether this approach could assist new model development, as well as adjustment of an existing model, we repeated the parameter search within a wide range of values and/or after setting P2011.1.2 model parameters to nominal values. Greater computational power is required when there are fewer constraints on the model's parameter values; however, viable solutions were identified (<xref ref-type="table" rid="RSOB150042TB2">table 2</xref>
) and suitable computing resources are increasingly accessible [<xref rid="RSOB150042C97" ref-type="bibr">97</xref>
]. The approach and infrastructure presented here allow a wider range of biologists to engage with complicated models, which will be essential tools to understand the mechanisms and physiological functions of complex biological networks.</p>
</sec>
</sec>
<sec sec-type="supplementary-material"><title>Supplementary Material</title>
<supplementary-material content-type="local-data" id="SD1"><caption><title>Supporting Information and Supplementary Figures</title>
</caption>
<media mimetype="application" mime-subtype="pdf" xlink:href="rsob150042supp1.pdf" orientation="portrait" id="d35e3786" position="anchor"></media>
</supplementary-material>
</sec>
</body>
<back><ack><title>Acknowledgements</title>
<p>We are grateful to Gavin Steel and Kelly Stewart for expert technical assistance, Martin Beaton and Richard Adams for support of BioDare and SBSI, Uriel Urquiza for preparing SBML files, David Rand for insightful discussion, and members of the A.J.M. and K.J.H. laboratories, who curated data for BioDare.</p>
</ack>
<sec id="s4"><title>Author contributions</title>
<p>Designed experiments: A.F., R.S., K.S., H.G.M., M.S., A.J.M., K.J.H. Performed experiments: A.F., A.P.F., V.M., M.M.S., H.G.M., K.S., A.J.M. Designed infrastructure: T.Z., A.J.M., K.J.H. Built infrastructure: T.Z., A.H.; Analysed data: D.D.S., A.P., M.M.S., H.G.M., A.J.M., M.S., K.J.H. A.F., H.G.M., M.S. and A.J.M. wrote the paper with input from all authors.</p>
</sec>
<sec id="s5"><title>Competing interests</title>
<p>We declare we have no competing interests.</p>
</sec>
<sec id="s6"><title>Funding</title>
<p>Supported by awards from UK BBSRC and EPSRC (ROBuST BB/F005237/1 and SynthSys BB/D019621/1) and from the European Commission (FP7 collaborative project TiMet, contract 245143). This work made use of the facilities of HECToR, the UK's national high-performance computing service, which was provided by UoE HPCx Ltd at the University of Edinburgh, Cray Inc. and NAG Ltd, and funded by the Office of Science and Technology through EPSRC's High End Computing Programme.</p>
</sec>
<app-group><app><title>Appendix A</title>
<p><bold>A.1. Experimental procedures</bold>
</p>
<p>Experimental methods were similar or identical to published protocols [<xref rid="RSOB150042C33" ref-type="bibr">33</xref>
,<xref rid="RSOB150042C88" ref-type="bibr">88</xref>
,<xref rid="RSOB150042C98" ref-type="bibr">98</xref>
], as detailed in the electronic supplementary material. Statistical significance of comparisons reported in the results is from two-tailed <italic>t</italic>
-tests compared with the cognate WT plants at the same timepoint, unless otherwise stated. Homoscedasticity is assumed, because all comparisons reported are within individual datasets for the same PCR primers. Significance is not corrected for multiple comparisons (reducing significance), nor for support from neighbouring timepoints or replication across cycles or studies (which can increase significance).</p>
<p><bold>A.2. Biodare and computational methods</bold>
</p>
<p>The BioDare online resource (<uri xlink:href="www.biodare.ed.ac.uk">www.biodare.ed.ac.uk</uri>
) uses a desktop application to prepare metadata (describe experiments). The XML editor P<sc>edro</sc>
[<xref rid="RSOB150042C99" ref-type="bibr">99</xref>
] was customized for each experimental protocol to speed up metadata entry, as each experiment can comprise several hundred samples. Numerical data are uploaded in a spreadsheet-compatible format, with the XML metadata, and stored in a relational database. Password-protected access allows controlled data sharing or public dissemination. Searching the metadata (by genotype, marker, etc.) allows aggregation of similar data from multiple sources, followed by secondary processing (detrending, normalization, averaging), visualization (<xref ref-type="fig" rid="RSOB150042F7">figure 7</xref>
<italic>a</italic>
) and download. Rhythm analysis in BioDare was recently described [<xref rid="RSOB150042C52" ref-type="bibr">52</xref>
,<xref rid="RSOB150042C53" ref-type="bibr">53</xref>
]. Model optimization used SBSI<sc>visual</sc>
v. 1.4.5 [<xref rid="RSOB150042C29" ref-type="bibr">29</xref>
] and SBSI<sc>numerics</sc>
v. 1.2 (see electronic supplementary material, Methods). Graphical network diagrams used SBGN-ED in VANTED [<xref rid="RSOB150042C38" ref-type="bibr">38</xref>
].</p>
<p><bold>A.3. Data, network diagram, model and code accessibility</bold>
</p>
<p>The accessibility of resources used in the publication is summarized at the University of Edinburgh's institutional repository, at <uri xlink:href="http://www.research.ed.ac.uk/portal/en/datasets/data-code-and-models-for-flis-et-al-rs-open-biology-2015(fd297498-7d0d-4d57-9040-769af9c65212).html">http://www.research.ed.ac.uk/portal/en/datasets/data-code-and-models-for-flis-et-al-rs-open-biology-2015(fd297498-7d0d-4d57-9040-769af9c65212).html</uri>
</p>
<p><bold>A.3.1. RNA expression profile data</bold>
</p>
<p>The RNA datasets reported here are publicly available from BioDare with the permanent data identifiers listed below, using login name ‘public’ with password ‘public’. Numbers below match <xref ref-type="fig" rid="RSOB150042F1">figure 1</xref>
.
<list list-type="simple"><list-item><p>(1) Pinas-Fernandez and K.J. Halliday (2015) ROBuST RNA timeseries data at 17°C for clock model parameterisation. BioDare accessions:</p>
</list-item>
</list>
ROBuST sd for <italic>CCA1</italic>
, BioDare accession 12820611467827, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611467827">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611467827</uri>
</p>
<p>ROBuST sd for <italic>LHY</italic>
, BioDare accession 3492, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=3492">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=3492</uri>
</p>
<p>ROBuST sd for <italic>PRR9</italic>
, BioDare accession 12820610743262, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820610743262">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820610743262</uri>
</p>
<p>ROBuST sd for <italic>PRR7</italic>
, BioDare accession 12820611319996, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611319996">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611319996</uri>
</p>
<p>ROBuST sd for <italic>PRR5</italic>
, BioDare accession 12820611188065, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611188065">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611188065</uri>
</p>
<p>ROBuST sd for <italic>TOC1</italic>
, BioDare accession 12820611587928, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611587928">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611587928</uri>
</p>
<p>ROBuST sd for <italic>GI</italic>
, BioDare accession 12820606741450, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820606741450">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820606741450</uri>
</p>
<p>ROBuST sd for <italic>LUX</italic>
, BioDare accession 12820610913763, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820610913763">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820610913763</uri>
</p>
<p>ROBuST sd for <italic>CAB2</italic>
, BioDare accession 13228354371807, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228354371807">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228354371807</uri>
</p>
<p>ROBuST sd for <italic>ELF4</italic>
, BioDare accession 12962296599986, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12962296599986">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12962296599986</uri>
</p>
<p>ROBuST sd for <italic>ELF3</italic>
, BioDare accession 12962294335805, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12962294335805">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12962294335805</uri>
<list list-type="simple"><list-item><p>(2) Flis, V. Mengin, R. Sulpice and M. Stitt (2015) TiMet RNA timeseries data from rosette plants for clock model parameterisation. TiMEt ros, BioDare accession 2841, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=2841">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=2841</uri>
</p>
</list-item>
<list-item><p>(3) Flis, V. Mengin, R. Sulpice and M. Stitt (2015) TiMet RNA timeseries data from <italic>elf3</italic>
mutant plants for clock model parameterisation. TiMet sd1, BioDare accession 2842, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=2842">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=2842</uri>
</p>
</list-item>
<list-item><p>(4) Flis, V. Mengin, R. Sulpice and M. Stitt (2015) TiMet RNA timeseries data from seedlings for clock model parameterisation. TiMet sd2, BioDare accession 2843, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=2843">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=2843</uri>
</p>
</list-item>
<list-item><p>(5) H. G. McWatters (2015) Clock RNA timeseries in light and temperature entrainment. McWatters sd, BioDare accession 3488, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=3488">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=3488</uri>
</p>
</list-item>
<list-item><p>(6) K.D. Edwards and A.J. Millar (2010). Clock RNA timeseries in multiple photoperiods. Edwards sd, BioDare accession 13227622661196, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13227622661196">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13227622661196</uri>
</p>
</list-item>
<list-item><p>(7) M.M. Southern and A.J. Millar (2015). Clock RNA timeseries in wild-type and mutant plants under red light.</p>
</list-item>
</list>
Independent biological replicates are presented for this experiment, with the same genotypes and markers tested in replicates (1 + 2) and (3 + 4):</p>
<p>Southern sd replicate 1, Biodare accession 13228298288040, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228298288040">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228298288040</uri>
</p>
<p>Southern sd replicate 2, Biodare accession 13227619871305, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13227619871305">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13227619871305</uri>
</p>
<p>Southern sd replicate 3, Biodare accession 13228357055348, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228357055348">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228357055348</uri>
</p>
<p>Southern sd replicate 4, Biodare accession 13228357183121, <uri xlink:href="https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228357183121">https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13228357183121</uri>
</p>
<p><bold>A.3.2. Code</bold>
</p>
<p>BioDare and SBSI are open-source and available from Sourceforge (<uri xlink:href="www.sourceforge.net">www.sourceforge.net</uri>
).</p>
<p>BioDare: Sourceforge project, <uri xlink:href="http://sourceforge.net/projects/biodare/">http://sourceforge.net/projects/biodare/</uri>
. The online resource is available at <uri xlink:href="www.biodare.ed.ac.uk">www.biodare.ed.ac.uk</uri>
.</p>
<p>SBSI: Sourceforge project, <uri xlink:href="http://sourceforge.net/projects/sbsi/">http://sourceforge.net/projects/sbsi/</uri>
. Related materials, plugins and tutorials are available at <uri xlink:href="www.sbsi.ed.ac.uk">www.sbsi.ed.ac.uk</uri>
.</p>
<p><bold>A.3.3. Graphical network diagram</bold>
</p>
<p>The diagram of the <italic>Arabidopsis</italic>
clock model (electronic supplementary material, figure S2) is available from the PlaSMo repository (<uri xlink:href="www.plasmo.ed.ac.uk">www.plasmo.ed.ac.uk</uri>
), which handles a variety of XML file formats.</p>
<p>D. D. Seaton and A. J. Millar (2015), Graphical network diagram of the <italic>Arabidopsis</italic>
clock model P2011.1 in SBGN PD: PlaSMo accession 1045 version 1, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1045&version=1">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1045&version=1</uri>
</p>
<p><bold>A.3.4. Models</bold>
</p>
<p>A. Pokhilko <italic>et al.</italic>
(2012), <italic>Arabidopsis</italic>
clock model P2011.1.1 (published in Molecular Systems Biology, 2012): BioModels identifier BIOMD0000000412 [<xref rid="RSOB150042C100" ref-type="bibr">100</xref>
].</p>
<p>The <italic>Arabidopsis</italic>
clock models below are available from the PlaSMo repository (<uri xlink:href="www.plasmo.ed.ac.uk">www.plasmo.ed.ac.uk</uri>
) and will also be submitted to Biomodels when the present publication has a digital identifier. The model versioning convention is described in the electronic supplementary material.</p>
<p>A. J. Millar and A. Hume (2015) <italic>Arabidopsis</italic>
clock model P2011.1.2: PlaSMo accession PLM_71 version 1, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_71&version=1">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_71&version=1</uri>
</p>
<p>A. J. Millar and A. Hume (2015) <italic>Arabidopsis</italic>
clock model P2011.2.1: PlaSMo accession PLM_71 version 2, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_71&version=2">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_71&version=2</uri>
</p>
<p>K. Stratford, A. Hume and A. J. Millar (2015) <italic>Arabidopsis</italic>
clock model P2011.3.1: PlaSMo accession PLM_1041 version 1, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1041&version=1">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1041&version=1</uri>
</p>
<p>K. Stratford, A. Hume and A. J. Millar (2015) <italic>Arabidopsis</italic>
clock model P2011.4.1: PlaSMo accession PLM_1042 version 1, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1042&version=1">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1042&version=1</uri>
</p>
<p>K. Stratford, A. Hume and A. J. Millar (2015) <italic>Arabidopsis</italic>
clock model P2011.5.1: PlaSMo accession PLM_1043 version 1, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1043&version=1">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1043&version=1</uri>
</p>
<p>K. Stratford, A. Hume and A. J. Millar (2015) <italic>Arabidopsis</italic>
clock model P2011.6.1: PlaSMo accession PLM_1044 version 1, <uri xlink:href="http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1044&version=1">http://www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_1044&version=1</uri>
</p>
</app>
</app-group>
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