1. A Systems Approach to Measuring the Binding Energy Landscapes of Transcription Factors By Sebastian J. Maerkl Stephen R. Quake

2. Background Measuring affinities was a difficult task: The traditional method cannot be used to measure interactions with small affinities. The traditional method cannot be used to measure interactions with small affinities. Results were often “1 or 0”. Results were often “1 or 0”. The new method provides solution by combining the microarray and microfluidic technology

3. Background How is a specific protein produced inside a cell? DNA RNA Polymerase RNA

4. Transcription Factor Where should I bind to? RNA Polymerase Transcription Factor DNA

5. Transcription Factor A protein that mediates the binding of RNA polymerase and the initiation of transcription. (Wiki) Without transcription factor, RNA synthesis cannot occur, thus protein production. A transcription factor can actually bind to a number of different initialization zone of gene—how does it know where to go for a specific RNA/protein synthesis?

6. Revisit With the traditional methods, we were unable to produce… This With the ability to quantify the absolute affinities of interactions, we are given the ability to produce a detailed graph such as the one above. This provides a lot more useful information.

7. What can we achieve? Predict in vivo function for 2 yeast TFs. Make a comprehensive test of the base additivity assumption. Test the hypothesis that the base region alone determines binding specificity of bHLH TFs.

8. MITOMI The new method, mechanically induced trapping of molecular interactions, provides solution by fusing the microarray and microfluidic technology together.

9. MITOMI 1.The DNA Chamber, connecting with the channels which will be filled with other components. 2. The valves controlling flow of the channels. 3. The “button” which creates a circular area when pressure is applied; able to stretch to the bottom of the slide. The enclosed cell will be used for TF binding energy topography to measure/quantify the affinities.

10. Process 1. Button was pressed to protect the area. 2. The surface other than the protected area is filled by biotinylated bovine serum albumin. 3. The button is released, leaving the protected area ready to be connected to antibodies.

11. Process Continued 4. The channel is filled with DNA, transcription factors, as well as antibodies. 5. The button is again pressed to trap the DNA bound on the bottom of the slides. ouch

12. No pocket; zero dead volume.

13. The Results The results showed that optimal binding sequence is CACGTG. Similar forms also proven to be relatively effective, such as CATGTG, CTCGTG, CAGGTG. The structures were found in low- affinity range, which couldn’t be detected with traditional methods before.

14. Additivity Assumption States that each base inside a codon behaves independently, without having to work with its neighbor bases to perform a task. That is to say, if you substitute bases around, you will get results which shall be different from the original, but will not be completely off.

15. Additivity Assumption Ctd… 2.5 Substituting 1 base seems to be perfectly fine as the data in blue boxes lies perfectly on the prediction line. Substitution of 2 bases seems to be ok, but then about half of the data points lie distinctively far away from the line.

16. Additivity Assumption Ctd… So…is the data for or against the argument that additivity assumption is true? We don’t know! PWM fails to predict properly for everything above 2.5 kcal/mol, because of non-specific interactions.

17. Basic Region is Enough? First looking at Pho4p and Cbf1p, which seem to have the same motifs with CACGTG. By adding flanking bases at the beginning or end of the sequence. Pho4p  CCCACGTGGG Cbf1p  [A/G]GTCACGTGAC[T/C]

18. Basic Region is Enough. The reason they couldn’t do it before: could not detect low- affinities. By cloning the basic regions of Pho4p and Cbflp into the MAX isoform B backbone, it was shown that the basic region alone is enough for recognition.

19. Comparing with other Methods Trying to list the number of genes regulated by Pho4p and Cbf1p. Pho4p was assumed to work on Phosphate metabolism. Cbf1p on Chromosome Segregation and Methionine Metabolism.

20. Comparing with other Methods -The data seems to be fairly consistent for Pho4p, as for all three different methods, there’s a considerable large region of overlap. -Data seems to be way off for Cbf1p. -Where did Microarray go?? No Funding

21. Comparing with other Methods Does this mean the MITOMI approach is not practical  answer is no. The data collected through different approach can be used as reference to each other for possible final confidential result.

22. Critique Clear and distinctive graphical representations. Number of experiments provided here The MITOMI method is clearly presented with details (no hiding techi details) Shadow area for Additivity Assumption Some more reference needed, e.g. for ITT

23. ChIP-Chip

24. ChIP-Chip ChIP-on-chip, also known as genome- wide location analysis, is a technique for isolation and identification of the DNA sequences occupied by specific DNA binding proteins in cells. These binding sites may indicate functions of various transcriptional regulators and help identify their target genes during animal development and disease progression.