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Gene Network Modeling

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  1. Gene Network Modeling Prof. Yasser Kadah Eng. Fadhl Al-Akwaa

  2. OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Next work • Reference

  3. From The Last Lecture • DNA sequence {A,T,C,G} ATCGAATCGA • Protein sequence { except B, J, O, U, X, Z} KMLSLLMARTYW

  4. Transcription Translation :The Central DogmaProtein Synthesis Cell Function Transcriptome Proteome Genome

  5. Transcription Translation Bioinformatics Important Challenges • Protein Function • Protein 3D Structure • Gene • Predication • Gene Function

  6. Transcription Translation Public Data Base • Protein sequence • KMLSLLMARTYW • DNA • sequence • {A,T,C,G} • Microarray Gene Expression Level

  7. Gene Expression 9

  8. Microarray Technology

  9. + + GENE A + - Translation Rate Protein Level Gene Expression Level Transcription Rate

  10. Translation Rate + + Protein Level Gene Expression Level + Transcription Rate GENE A GENE B - ? ? Translation Rate + + Protein Level Gene Expression Level + Transcription Rate -

  11. Translation Rate + + Protein Level Gene Expression Level + Transcription Rate GENE A GENE B - ? Translation Rate + + Protein Level Gene Expression Level + Transcription Rate - ? ? ? ?

  12. OUTLINES • What is the Gene Regulatory Network? • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Future work • Reference

  13. What is Gene Regulatory Network? (GRN) ? ? ? ? ? Gene A Gene C Gene D Gene B

  14. GRN An example:Fission yeast Lackner DH ,2007 http://www.sanger.ac.uk/Info/News-releases/2007/070413.shtml

  15. http://en.wikipedia.org/wiki/Metabolic_network_modelling

  16. http://www.enm.bris.ac.uk/anm/summerschools/complexity/imagery/191.htmlhttp://www.enm.bris.ac.uk/anm/summerschools/complexity/imagery/191.html

  17. OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Next work • Reference

  18. Why build a Gene Network? Functional Genomics • Allow researchers to make predictions about gene function that can then be tested at the bench. • The Focus is gradually shifting to Functional Genomics.

  19. Application of GRN Translational Genomics • we can study the effects of a compound (such as a drug) on the level of expression of many genes. • Translational Genomics The mission of the Translational Genomics is to translate genomic discoveries into advances in human health.

  20. Application of GRN UnderstandingExperimental data • Biologists are expecting powerful computational tools to extract functional • information from the Experimental data.

  21. GRN Model Objective Construct a gene network model that: • Describes known genes interactions well • Predicts interactions not known so far • Allows for Drug effect simulation • Understand the otology of the Disease

  22. OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Next work • Reference

  23. GRN Construction Methodology • Forward Engineering • Inverse Engineering “Traditional methodology”

  24. Forward Engineering Hard

  25. Reverse Engineering Model Gene Network very difficultinverse problem Possible forward problem Microaary Data

  26. Reverse Engineering Boolean networks easy Boolean data easy

  27. assay Data Required: DNA Microarray gene 1 gene 2 gene 3

  28. Data Required: Gene Expression Matrix

  29. Data Required: Gene Expression Matrix Time serious Snap Shot

  30. OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN Modeling Steps • GRN Models • GRS Software • Next work • Reference

  31. Overview of steps in modeling and control of Probabilistic Boolean networks Ranadip Pal,2007 Microarray Image Data Extraction Discretization A3 A1 A2 C Gene Expression Extraction B Discretization Grid Alignment Segmentation Hypothesis testing Upregulated 99% 1 t1 0 t2 -1 Down regulated t1 t2 Application of Stationary Policy Design of Optimal Control Policy Gene Selection BN generation (I) Penalty Assignment PBN steady state matched Seed Algorithm Y (II) Formulation of Optimal Control Problem Original Steady State Dynamic Programming Optimal Control Policy F E Prior Biological Knowledge D Gene Selection Steady State using Control Network Generation G H Control of Network

  32. GRN modeling steppes: Discretization gene 1 gene 2 gene 3 assume that genes exist in two states: on and off if expression of gene i is above level ti consider it on, otherwise, consider it off

  33. GRN modeling steppes: Discretization t1

  34. GRN modeling steppes: Discretization on on on on on on on t1 off off off off off off off off

  35. GRN modeling steppes: Discretization gene 1 gene 2 gene 3 t1 t2 t3

  36. GRN modeling steppes: Discretization gene 1 gene 2 gene 3 on on on on on on on on on t1 on on on t2 off on on t3 off off off off off off off off off off off off off off off off off off

  37. GRN modeling steppes: Discretization • we obtain the following discretized gene expression data: • the gene expression data is now in the form of bit streams

  38. GRN modeling steppes: Discretization Up-regulated 1 Unchanged 0 Down-regulated -1 assume that genes exist in three states

  39. GRN modeling steppes: Gene SelectionClustring

  40. Clustering Steps: Correlation Choose a similarity metric to compare the transcriptional response or the expression profiles: Pearson Correlation Spearman Correlation Euclidean Distance …

  41. Clustering Steps: Correlation Algorithm • Correlation coefficients are values from –1 to 1, with 1 indicating a similar behavior, –1 indicating an opposite behavior and 0 indicating no direct relation.

  42. Clustering Steps: Clustering Algorithm • Choose a clustering algorithm: • Hierarchical • K-means • …

  43. Hierarchical Clustering g1 g4 • Find largest value in similarity matrix. • Join clusters together. • Recompute matrix and iterate.

  44. Hierarchical Clustering g2 g3 g1 g4 • Find largest value is similarity matrix. • Join clusters together. • Recompute matrix and iterate.

  45. Hierarchical Clustering g5 g2 g3 g1 g4 • Find largest value is similarity matrix. • Join clusters together. • Recompute similarity matrix and iterate.

  46. Clustering Example Eisen et al. (1998), PNAS, 95(25): 14863-14868

  47. _ g1 _ + ? _ g2 g3 + + _ g4 _ Gene network GRN Modeling Steppes: GRN Generation Statistical Signal Processing Technique

  48. OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Next work • Reference