Natalie Williams Week 10
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Outline of Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae
Introduction
- Gene regulation makes a working copy of the genetic information of DNA sequences into proteins and/or functional RNAs.
- Promoting regions must be recognized by transcription regulatory proteins which bind RNA polymerase to the DNA strand.
- Microarray developments have made it easier to follow the changes of the cell's gene expression over time.
- Analyzing this microarray data, we could better understand the relationships between genes and their transcription factor regulators.
- Because these relationships collectively form a network among the genes, it should be possible to construct networks by studying the results of microarray data.
- Budding yeast, Saccharomyces cerevisiae, has been studied extensively in the lab.
- There is a lot of knowledge about its genome.
- Expression data was collected and analyzed to figure out what genes were being used at a specific stage of the cell cycle.
- Genes were grouped based on where their regulators bound to promoter regions.
- Methods in which networks were produced previously:
- A generalized linear model was going to be created to described regulators and guess the pattern of regulators and their target genes.
- A kinetic model with Bayesian networks was used to predict gene regulatory networks as well as the proteins that regulate genes expression.
- Including both information from the genome and gene expression data named another method to predicting networks.
- Another research furthered this method by using promoter regions or the sigma factor.
- An alternative method used in this paper:
- A model based on nonlinear differential equation model was used.
- It called for all potential regulators
- Genes from a group of potential regulators are picked and the model is applied to try to fit the gene expression results of the target genes.
- This is done for all potential regulators
- A model based on nonlinear differential equation model was used.
- In this model:
- There were 40 target genes;
- 184 possible regulators were identified;
- The data were analyzed using a linear model; and,
- Results from the linear model were compared to that of the nonlinear differential equation system to see how well it predicted the target genes' profiles.
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