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                  <mods:namePart>De Luis Balaguer, María Angels</mods:namePart>
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                  <mods:dateAccessioned encoding="iso8601">2016-03-17T13:05:59Z</mods:dateAccessioned>
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               <mods:abstract>Gene Regulatory Networks controlling Arabidopsis Root Stem Cells&#xd;
Identifying the transcription factors (TFs) and associated regulatory processes involved in stem&#xd;
cell regulation is key for understanding the initiation and growth of tissues and organs. Although&#xd;
many TFs have been described in the Arabidopsis root stem cells, a comprehensive view of the&#xd;
transcriptional signature of the stem cells is lacking. We used a systems biology approach to&#xd;
predict interactions among the genes involved in stem cell identity and maintenance.&#xd;
We first transcriptionally profiled four stem cell populations and developed a gene regulatory&#xd;
network (GRN) inference algorithm, GENIST, which combines spatial and temporal transcriptomic&#xd;
datasets to identify important TFs and infer gene-to-gene interactions. Our approach resulted in a&#xd;
map of gene interactions that orchestrates the transcriptional regulation of stem cells. In addition&#xd;
to linking known stem cell factors, our resulting GRNs predicted additional TFs involved in stem&#xd;
cell identity and maintenance. We mathematically modeled and experimentally validated some of&#xd;
our predicted transcription factors, which confirmed the robustness of our algorithm and our&#xd;
resulting networks. Our approach resulted in the finding of a factor, PERIANTHIA (PAN), which&#xd;
may play an important role in stem cell maintenance and QC function.&#xd;
We then developed an imaging system to perform in vivo, long-term imaging experiments that will&#xd;
be used to understand the dynamics of the regulatory interactions between PAN and its&#xd;
downstream TFs in a cell-specific manner. For this, we designed and 3-D printed a Multi-sample&#xd;
Arabidopsis Growth and Imaging Chamber (MAGIC) that provides near-physiological imaging&#xd;
conditions and allows high-throughput time-course imaging experiments in the ZEISS Lightsheet&#xd;
Z.1. We showed MAGIC’s imaging capabilities by following cell divisions, as an indicator of plant&#xd;
growth and development, over prolonged time periods, and demonstrated that plants imaged with&#xd;
our chamber undergo cell divisions for >16 times longer than those with the glass capillary&#xd;
system supplied by the ZEISS Z1. Future in vivo observations of the expression of PAN and its&#xd;
predicted downstream factors will be key to refine our model predictions and obtain information&#xd;
about the dynamics of the regulatory processes.&#xd;
Our systems biology approach illustrates the strength of integrating computational and&#xd;
technological tools into the experimental approaches to solve key biological questions. We&#xd;
anticipate that our algorithm and our approach can be applied to solve similar problems in a&#xd;
diverse number of systems, which can result in unsupervised predictions of gene functions and&#xd;
gene candidates.</mods:abstract>
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               <mods:accessCondition type="useAndReproduction">by-nc-nd</mods:accessCondition>
               <mods:subject>
                  <mods:topic>Transcripción genética - Regulación</mods:topic>
               </mods:subject>
               <mods:titleInfo>
                  <mods:title>Gene Regulatory Networks controlling Arabidopsis Root Stem Cells</mods:title>
               </mods:titleInfo>
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