1. Reverse-engineering transcription control networks timothy s
Reverse-engineering transcription control networks timothy s. Gardener & jeremiah j. faith
A paper pitch by Samar Tareen and Gianluca Mazzoni
Keywords:
Mechanism and kinetic model of transcription
Reverse engineering
The physical approach
The Bussemaker algorithm

2. Theme
Classification and discussion of the various methods of reverse engineering transcriptional control networks
Reverse Engineering: Reconstruction of transcriptional control networks
Inferring the microarray data
Principal focus: mechanism of transcription control

3. Mechanism of Transcription
RNA polymerase (RNAP; P) binds to the promoter region on the DNA (N)
RNAP unwinds the DNA and starts polymerising the RNA transcript (S)
RNAP detaches at the stop sequence after completing transcription
(3)
(1)
(2)
Figure 3.a; Gardener and Faith, "Reverse-engineering transcription control networks", Physics of Life Reviews 2 (2005) 65–88

4. Kinetic Model Using the rate law kinetic modelling:
Assuming k+2 is small, or conc.(N) << conc.(P):
Imperfect as molecular concentration is small and cell contents inhomogeneous
But still captures qualitative and quantitative features of gene expression
Incorporating regulatory control (via transcription factors):
Incorporating competitive inhibition (via repressors):

5. Physical Approach Objective: to identify,
Proteins that regulate transcription
DNA motifs where factors bind
Physical interaction between regulatory proteins and promoters but not any causal relationships
Advantages:
Reduction of dimensionality problem in reverse-engineering while increasing sensitivity and specificity
Limitations
Restricted to Transcription Factors
Regulatory Protein
RNA Polymerase

6. Bussemaker Algorithm Combines RNA expression with genome sequence data
Basic assumption
Rate of transcription of a promoter reflects the number of binding motifs for a TF in that promoter
 Look for binding motifs in which:
Copy number of the motifs correlates linearly with logarithm of the RNA expression
Other assumptions
Under steady state
[TFs] no at saturation level

7. Bussemaker Algorithm Under these conditions we get
Considering that both repressor and activation are simultaneously possible and considering
We obtain the general expression:

8. Bussemaker Algorithm Steps:
Generation all possible binding motifs of n nucleotides length
Counting how many copy number of each motifs in each promoter (Mi)
Regression
Estimation the best combination of motifs looking at the best fit of the regression
promoter

9. Bussemaker Algorithm Computational Problem
Too many motifs for each promoter (up to of 7nt)
Solution: The algorithm uses an iterative gready search
An iteration for each promoter region
Fits model for each motif and select the best
Removes the influence of the motif from the expression
Until the fit does not improve further
Disadvantages
Highly constrained models, prone to errors
Does not determine which TF binds to which motifs

10. Questions?