Nika Vafadari Week 10

Electronic Lab Notebook Week 10

Preparation for jornal Club 2

Outline

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

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

• • represents target gene expression level z at time t + dt
• 
• g= regulatory effect for specific gene
• yj= expression levels of regulators at time t
• wj= regulatory weights of genes controlling target gene
• j= 1, 2,…,m ( m= number of regulators controlling gene)
• b= parameter that represents delay of transcription initiation/ unspecified bias from regulatory effects of gene expression

*Equation 2

• • rate of expression of a target gene (dz/dt)
• 
• p= regulatory effect g of regulators j transformed by sigmoidal transfer function (regulatory effects of other genes)
• x= effect of degradation (x=k.z.)

*Equation 3

• • models control of target gene expression z
• 
• k2= rate constant of degradation of product of target gene
• k1= maximal rate of expression

*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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