1. Gene Regulatory Network Inference

2. Progress in Disease Treatment  Personalized medicine is becoming more prevalent for several kinds of cancer treatment  10-Feb-2009 – Breast Bioclassifier developed at the Huntsman Cancer Institute 1/8 women will be diagnosed with breast cancer Microarray analysis can separate large group who need no treatment Savings in cost and lifestyle With $100 human genomes, doctors can determine which drugs will be effective for your genotype

3. Biological Networks  Gene regulatory network: two genes are connected if the expression of one gene modulates expression of another one by either activation or inhibition  Protein interaction network: proteins that are connected in physical interactions or metabolic and signaling pathways of the cell;  Metabolic network: metabolic products and substrates that participate in one reaction;

4. Background Knowledge  Cell reproduction, metabolism, and responses to the environment are all controlled by proteins;  Each gene is responsible for constructing a single protein;  Some genes manufacture proteins which control the rate at which other genes manufacture proteins (either promoting or suppressing);  Hence some genes regulate other genes (via the proteins they create) ;

5. What is Gene Regulatory Network?  Gene regulatory networks (GRNs) are the on-off switches of a cell operating at the gene level.  Two genes are connected if the expression of one gene modulates expression of another one by either activation or inhibition  An example.

6. Sources: http://www.ornl.gov/sci/techresources/Human_Genome/graphics/slides/images/REGNET.jpg

7. Why Study GRN?  Genes are not independent; They regulate each other and act collectively; This collective behavior can be observed using microarray;  Some genes control the response of the cell to changes in the environment by regulating other genes;  Potential discovery of triggering mechanism and treatments for disease;

8. Learning Causal Relationships  High-throughput genetic technologies empowers to study how genes interact with each other;  If gene A consistently turns on after Gene C, then gene C may be causing gene A to turn on  We have to have a lot of carefully controlled time series data to infer this

9. Kegg  http://www.genome.jp/kegg/pathway.html

10. Pathgen

11. Microarray data  Gene up-regulate, down-regulate; Genes Samples

12. Learning from microarray data  Recurrent Neural Networks  Bayesian learning approaches

13. AIRnet: Asynchronous Inference of Regulatory networks 1.Classify gene levels using k-means clustering 2.Compute influence vectors (i.v.) 3.Convert i.v.'s into a sorted list of edges 4.Use Kruskal's algorithm to find the minimum-cost spanning tree

14. Influence Vectors 1.Perform pairwise- comparisons of change in gene levels between samples, adding or subtracting from i.v. 2.Divide i.v. by the total number of comparisons

15. Clockwise from top left: simulated E.coli 1 network; E.coli 1 inferred correlations above 50%; simulated E.coli 2 network; E.coli 2 inferred correlations above 50%; inferred networks made using 2 bins for each gene.

16. Euploid network → ← Trisomic network

17. Graph showing differences between Euploid and Trisomic

18. Graph highlighting differences between Euploid and Trisomic using multiple datasets

19. DREAM in-silico challenge

20. Using phylogenetic profiles to predict protein function  Basic Idea: Sequence alignment is a good way to infer protein function, when two proteins do the exact same thing in two different organisms.  But can we decide if two proteins function in the same pathway?  Assume that if the two proteins function together they must evolve in a correlated fashion: every organism that has a homolog of one of the proteins must also have a homolog of the other protein

21. Phylogenetic Profile  The phylogenetic profile of a protein is a string consisting of 0s and 1s, which represent the absence or presence of the protein in the corresponding sequenced genome; Protein P1: 0 0 1 0 1 1 0 0 Protein P2: 0 0 1 0 1 1 0 0 Protein P3: 1 0 0 1 0 1 0 0 For a given protein, BLAST against N sequenced genomes. If protein has a homolog in the organism n, set coordinate n to 1. Otherwise set it to 0.

22. Phylogenetic Profile Proteins Species

23. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO, Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci U S A. 96(8):4285-8,. 1999