Nika Vafadari Week 10
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Electronic Lab Notebook Week 10
Purpose
Preparation for jornal Club 2
Biological Terms
Outline
Main Result Presented in Paper
Significance of Work
Introduction
Regulation of gene expression
- DNA (gene) —> RNA molecules and functional proteins
- For regulation to occur—> transcriptional regulatory proteins must recognize promotor sequence in order to bind RNA polymerase and start transcription reaction to environmental stress & cell development can lead to modification/change in gene expression
DNA microarrays
- visualize and record change in gene expression over the time (duration of cell cycle/developmental processes) by looking at the change in mRNA levels
- to understand relationship between regulators/ target genes and the network interactions that result
- these networks are what lead to changes in the amount of RNA/ changes in gene expression
Cell cycle control
- well studied in Saccharomyces cerevisiae leading to the establishment of large transcriptomic databases that include the changes in RNA synthesis throughout the cell cycle
- Goal: collect microarray gene expression data (genome wide) pertaining to cell cycle in yeast—> analyze through clustering methods —> identify cell cycle controlled genes
Methods
Previous methods
- grouped genes based on which promoter the transcriptional regulators bind to instead of similarity in pattern of gene expression
- identified potential networks through the use of differential equations in order to develop a generalized linear model to predict the pattern of transcription of a specific gene
- Wolf and Wang: used fuzzy logic
- Nachman et al.: used dynamic Bayesian networks with a kinematic model
- Bar-Joseph: used gene expression analysis and genomic info alongside one another
- Wang et al. and Makita et al.: extending the work of Bar-Joseph, incorporated promoter sequence analysis into gene expression analysis
Alternative method presented in paper
- replaces linear model with model using nonlinear differential equation
- Procedure applied
- starts by selecting set of potential regulators —> 184 chosen
- select set of specific target genes within S. cerevisiae —> 40 genes selected
- select genes from within the set of potential regulators to apply model to in order to see if it fits the gene expression profile of the specified gene correctly
- repeat for selected target genes and potential regulators
- determine true regulators by identifying regulators that model the profile of the target gene correctly
- Procedure applied
Results
Dynamic model of transcriptional control
- Model Assumptions
- the relationship/ interaction between regulators and target genes is repeated over time
- combinatorial control by regulators exists/causes change in gene expression of a target gene
- Equation 1
- Equation 2
- Equation 3
- Equation 4
- Equation 5
- Equation 6
- Equation 7
Acknowledgments
- Various sites referenced linked and referenced next to each term were used to define the biological terms.
- The equations, figures and key info pertaining to the construction of the model and execution of the experiment were copied and pasted from the article referenced below.
- Except for what is noted above, this individual journal entry was completed by me and not copied from another source.
- Nika Vafadari 05:24, 27 March 2017 (EDT):
References
- Dahlquist, Kam D. (2017) BIOL398-05/S17:Week 10. Retrieved from http://www.openwetware.org/wiki/BIOL398-05/S17:Week_10 on 27 March 2017.
- Vu, T. T., & Vohradsky, J. (2007). Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae. Nucleic acids research, 35(1), 279-287. doi: 10.1093/nar/gkl1001
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