Johnson and O'Neil LMU Symposium 2016: Difference between revisions
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'''Modeling the dynamics of a 21-gene, 50-edge gene regulatory network controlling the transcriptional response to cold shock in ''Saccharomyces cerevisiae'' using GRNmap''' | '''Modeling the dynamics of a 21-gene, 50-edge gene regulatory network controlling the transcriptional response to cold shock in ''Saccharomyces cerevisiae'' using GRNmap''' | ||
Gene expression is regulated by proteins called transcription factors which can either repress or activate a gene’s transcriptional output. A gene regulatory network (GRN) consists of a set of transcription factors that regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. A MATLAB software package called GRNmap uses ordinary differential equations to model the dynamics of medium-scale GRNs from budding yeast, ''Saccharomyces cerevisiae''. The program estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on DNA microarray data using a penalized least squares function. DNA microarray data for 6189 yeast genes was obtained from the Dahlquist lab where they subjected yeast to cold shock at 13°C and measured gene expression at three time points (after 15, 30, and 60 minutes of cold shock). | Gene expression is regulated by proteins called transcription factors which can either repress or activate a gene’s transcriptional output. A gene regulatory network (GRN) consists of a set of transcription factors that regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. A MATLAB software package called GRNmap uses ordinary differential equations to model the dynamics of medium-scale GRNs from budding yeast, ''Saccharomyces cerevisiae''. The program estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on DNA microarray data using a penalized least squares function. DNA microarray data for 6189 yeast genes was obtained from the Dahlquist lab where they subjected a yeast strain deleted for the Hap4 transcription factor to cold shock at 13°C and measured gene expression at three time points (after 15, 30, and 60 minutes of cold shock). A modified ANOVA showed that xxx genes had a log<sub>2</sub> fold change significantly different than zero at any of the timepoints studied. These genes were then submitted to the YEASTRACT database to determine which transcription factors regulated them. From this we generated xxx candidate GRNs that ranged in size from 35 genes and 104 edges to 15 genes and xx edges. From this analysis we expect to gain insight into potential missing genes in the gene regulatory network that controls the cold shock response in yeast. Our working code is available on the GRNmap page (http://kdahlquist.github.io/GRNmap/), and visualization of the network is available on GRNsight (http://dondi.github.io/GRNsight/). | ||
Word count: 330 | Word count: 330 | ||
Required word count: 250 | Required word count: 250 | ||
Revision as of 16:18, 11 February 2016
Abstract Draft
Modeling the dynamics of a 21-gene, 50-edge gene regulatory network controlling the transcriptional response to cold shock in Saccharomyces cerevisiae using GRNmap
Gene expression is regulated by proteins called transcription factors which can either repress or activate a gene’s transcriptional output. A gene regulatory network (GRN) consists of a set of transcription factors that regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. A MATLAB software package called GRNmap uses ordinary differential equations to model the dynamics of medium-scale GRNs from budding yeast, Saccharomyces cerevisiae. The program estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on DNA microarray data using a penalized least squares function. DNA microarray data for 6189 yeast genes was obtained from the Dahlquist lab where they subjected a yeast strain deleted for the Hap4 transcription factor to cold shock at 13°C and measured gene expression at three time points (after 15, 30, and 60 minutes of cold shock). A modified ANOVA showed that xxx genes had a log2 fold change significantly different than zero at any of the timepoints studied. These genes were then submitted to the YEASTRACT database to determine which transcription factors regulated them. From this we generated xxx candidate GRNs that ranged in size from 35 genes and 104 edges to 15 genes and xx edges. From this analysis we expect to gain insight into potential missing genes in the gene regulatory network that controls the cold shock response in yeast. Our working code is available on the GRNmap page (http://kdahlquist.github.io/GRNmap/), and visualization of the network is available on GRNsight (http://dondi.github.io/GRNsight/).
Word count: 330
Required word count: 250
