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

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

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

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):

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

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

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

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

Bussemaker Algorithm Computational Problem Too many motifs for each promoter (up to 20000 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

Questions?