MW 11:00-12:15 in Beckman B302 Prof: Gill Bejerano TAs: Jim Notwell & Harendra Guturu

1. CS173 Lecture 8: Transcriptional regulation II MW  11:00-12:15 in Beckman B302 Prof: Gill Bejerano TAs: Jim Notwell & Harendra Guturu http://cs173.stanford.edu [BejeranoWinter12/13]

2. Announcements • HW1 due today. Thoughts and comments? • HW2 will be out by midnight • Halfway feedback today http://cs173.stanford.edu [BejeranoWinter12/13]

3. Announcements http://cs173.stanford.edu [BejeranoWinter12/13]

4. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAGTTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG Genome Content http://cs173.stanford.edu [BejeranoWinter12/13]

5. Gene Regulation Some proteins and non coding RNAs go “back” to bind DNA near genes, turning these genes on and off. Gene activation: Gene DNA Proteins http://cs173.stanford.edu [BejeranoWinter12/13]

6. Transcription Activation contd. http://cs173.stanford.edu [BejeranoWinter12/13]

7. Transcription Activation Terminology: • RNA polymerase • Transcription Factor • Transcription Factor Binding Site • Promoter • Enhancer • Gene Regulatory Domain TF DNA http://cs173.stanford.edu [BejeranoWinter12/13]

8. Transcription activation “loop” Transcription factors bind DNA, turn on or off different promoters and enhancers, which in-turn turn on or off different genes, some of which may themselves be transcription factors, which again changes the presence of TFs in the cell, the state of active promoters/enhancers etc. Proteins DNA transcription factorbinding site Gene DNA http://cs173.stanford.edu [BejeranoWinter12/13]

9. IFN beta enhancer http://cs173.stanford.edu [BejeranoWinter12/13]

10. Transcription Measurements Some measurement techniques: • Chromatin Immunoprecipitation • Transcription output: • Transfection • Transgenics • Genome Engineering • Chromosome Conformation Capture http://cs173.stanford.edu [BejeranoWinter12/13]

11. Transcription Activation Properties Observed Properties: • Most TF binding site basepair preferences are independent of each other. • TFs can synergize to turn gene activity on. • Behavior can change in different conditions. • TFs bind to hundreds and thousands of different targets in a single condition. • Enhancers complement in different tissues. http://cs173.stanford.edu [BejeranoWinter12/13]

12. Gene Regulation is HOT • Gene regulation is currently one of the hottest topics in the study of the human genome. • Large projects are pouring lots of money to generate large descriptive datasets. • The challenge now is to glean logic from these piles. Measured >100 TFsin >70 cellular conditions. • How does TF binding determine its output: gene expression? http://cs173.stanford.edu [BejeranoWinter12/13]

13. System output measurements • Measure non/coding gene expression! • 1. First generation mRNA (cDNA) and EST sequencing: In UCSC Browser: http://cs173.stanford.edu [BejeranoWinter12/13]

14. 2. Gene Expression Microarrays (“chips”) http://cs173.stanford.edu [BejeranoWinter12/13]

15. 3. RNA-seq • “Next” (2nd) generation sequencing. http://cs173.stanford.edu [BejeranoWinter12/13]

16. Gene Finding II: technology dependence Challenge: “Find the genes, the whole genes, and nothing but the genes” We started out trying to predict genes directly from the genome. When you measure gene expression, the challenge changes:Now you want to build gene models from your observations. These are both technology dependent challenges. The hybrid: what we measure is a tiny fraction of the space-time state space for cells in our body. We want to generalize from measured states and improve our predictions for the full compendium of states. http://cs173.stanford.edu [BejeranoWinter12/13]

17. 4. Spatial-temporal maps generation http://cs173.stanford.edu [BejeranoWinter12/13]

18. Gene Expression Causality • Measuring gene expression over time provides sets of genes that change their expression in synchrony. • But who regulates whom? • Some of the necessary regulators may not change their expression level when measured, and yet be essential. • “Reading” enhancers can provide gene regulatory logic: • If present(TF A, TF B, TF C) then turn on nearby gene X http://cs173.stanford.edu [BejeranoWinter12/13]

19. Some Computational Challenges in Gene Regulation • Transcription factor binding site discovery • Technology-dependent challenge in constructing the correct binding site model (e.g. motif) from the measurements. • Eg, ChIP produces sequences of 100-200bp.Your motif of length 4-20 is there somewhere. • Find the most enriched model in the set of sequences you obtained. • Methods range between full enumeration, heuristic/probabilistic searches, and hybrids. http://cs173.stanford.edu [BejeranoWinter12/13]

20. Transcription factor motif discovery: different technologies • SELEX = Systematic Evolution of Ligands by Exponential Enrichment • PBM = Protein Binding Microarrays http://cs173.stanford.edu [BejeranoWinter12/13]

21. Transcription factor binding site prediction • Given the genome, and possibly some cell measurements predict (all and nothing but) the binding sites of a given transcription factor (in a/all context/s). http://cs173.stanford.edu [BejeranoWinter12/13]

22. Enhancer Prediction • How do TFs “sum” together to provide the activity of an enhancer? • A network of genes? http://cs173.stanford.edu [BejeranoWinter12/13]

23. Enhancer Prediction • Given a sequence of DNA predict: • Is it an enhancer? Ie, can it drive gene expression? • If so, in which cells? At which times? • Driven by which transcription factor binding sites? • Given a set of different enhancers driving expression in the same population of cells: • Do they share any logic? If so what is it? • Can you generalize this logic to find new enhancers? http://cs173.stanford.edu [BejeranoWinter12/13]

24. Biology is empirical: you predict, and you measure! • Measuring is great. It allows you to check your assumptions and improve your models until you get it. • Some difficulties associated with gene regulation: • Single cell measurements are rare. You most often measure some “average” over a population of cells. • The population of cells is seldom in sync (same state). • The closer a population of cells is to its in vivo state the less homogeneous it is. • The closer a population of cells is to its in vivo state the harder (time, effort, money) it is to measure it. http://cs173.stanford.edu [BejeranoWinter12/13]

25. Biology is empirical, you predict, and you measure! • Some more difficulties associated with gene regulation: • A family of TFs often has very similar binding motifs • Expression pattern may be different (but unknown to you). • Family members may have different protein-protein interaction (PPI) domains which are also important. • The genome is pleiotropic ( = good for all contexts). • If an enhancer you are studying is in fact good for multiple contexts they will be overlaid on each other in sequence and make prediction (and disentanglement) harder. http://cs173.stanford.edu [BejeranoWinter12/13]

26. Transcription Factors Large “fan outs” revisited • TFs reproducibly bind to thousands of genomic locations almost anywhere we’ve looked. • Gene regulation forms a dense network. • However, when such a TF is perturbed (over expressed or silenced) only a fraction of the genes it binds next to change their expression levels. http://cs173.stanford.edu [BejeranoWinter12/13]

27. Genomics vs. Genetics • Last but not least – genomics is descriptive. • It can show you “everything”. • Eg: all the location a given transcription factor is bound to the genome (reproducibly) in a given cell state. • Which of these bindings actually matters? no or near no effect adverse effect on cell frequency adverse effectobservable in experiments effect on cell http://cs173.stanford.edu [BejeranoWinter12/13]

28. Transcription factors “rule” We have learned (in a dish) to: 1 control differentiation 2 reverse differentiation 3 hop between different states Cellular reprogramming is done by adding to the cell large quantities of a small number of the “right” TFs. These somehow “reset” cell state. http://cs173.stanford.edu [BejeranoWinter12/13]

29. Transcription Regulation is not just about activation http://cs173.stanford.edu [BejeranoWinter12/13]

30. Transcriptional Repression An equally important but less visible part of transcription (tx) regulation is transcriptional repression (that lowers/ablates tx output). • Transcription factors can bind key genomic sites, preventing/repelling the binding of • The RNA polymerase machinery • Activating transcription factors(including via competitive binding) • Some transcription factors have stereotypical roles as activators or repressors. Likely many can do both (in different contexts). • DNA can be bent into 3D shape preventing enhancer – promoter interactions. • Activator and co-activator proteins can be modified into inactive states. Note: repressor thus can relate to specificDNA sequences or proteins. http://cs173.stanford.edu [BejeranoWinter12/13]

31. Transcriptional Output Prediction All these can increase or decrease tx output: • Adding/repressing different proteins • Modifying DNA bases • Adding genomic context • Changing cellular context Repression logic is harder to tease out.(need positive controls) http://cs173.stanford.edu [BejeranoWinter12/13]

32. Transcription can only happen in open Chromatin Chromatin / Proteins Genome packaging in fact provides a critical layer of gene regulation. DNA / Proteins http://cs173.stanford.edu [BejeranoWinter12/13]

33. Gene Activation / Repression via Chromatin Remodeling • A dedicated machinery opens and closes chromatin. • Interactions with this machinery turns genes and/or gene regulatory regions like enhancers and repressors on or off(by making the genomic DNA in/accessible) http://cs173.stanford.edu [BejeranoWinter12/13]

34. Insulators Insulators are DNA sequences that when placed between target gene and enhancer prevent enhancer from acting on the gene. • Known insulators contain binding sites for a specific DNA binding protein (CTCF) that is involved in DNA 3D conformation. • However, CTCF fulfills additional roles besides insulation. I.e, the presence of a CTCF site does not ensure that a genomic region acts as an insulator. TSS2 TSS1 Insulator http://cs173.stanford.edu [BejeranoWinter12/13]

35. Cis-Regulatory Components • Low level (“atoms”): • Promoter motifs (TATA box, etc) • Transcription factor binding sites (TFBS) • Mid Level: • Promoter • Enhancers • Repressors/silencers • Insulators/boundary elements • Cis-regulatory modules (CRM) • Locus control regions (LCR) • High Level: • Epigenetic domains / signatures • Gene expression domains • Gene regulatory networks (GRN) http://cs173.stanford.edu [BejeranoWinter12/13]

36. Signal Transduction • Everything we discussed so far happens within the cell. • But cells talk to each other, copiously. http://cs173.stanford.edu [BejeranoWinter12/13]

37. Gene Regulation II Chromatin / Proteins To be continued… Extracellular signals DNA / Proteins http://cs173.stanford.edu [BejeranoWinter12/13]

38. (On Mondays) ask students to stack the chairs without wheels at the back of the room at the end of class. http://cs173.stanford.edu [BejeranoWinter12/13]

39. http://cs173.stanford.edu [BejeranoWinter12/13]