Gene Regulatory Network InferenceProgress in Disease TreatmentBiological NetworksBackground KnowledgeWhat is Gene Regulatory Network?Slide 6Why Study GRN?Learning Causal RelationshipsKeggPathgenMicroarray dataLearning from microarray dataAIRnet: Asynchronous Inference of Regulatory networksInfluence VectorsSlide 15Slide 16Graph showing differences between Euploid and TrisomicSlide 18DREAM in-silico challengeUsing phylogenetic profiles to predict protein functionPhylogenetic ProfileSlide 22Slide 23Gene Regulatory Network InferenceProgress in Disease TreatmentPersonalized medicine is becoming more prevalent for several kinds of cancer treatment10-Feb-2009 – Breast Bioclassifier developed at the Huntsman Cancer Institute1/8 women will be diagnosed with breast cancerMicroarray analysis can separate large group who need no treatmentSavings in cost and lifestyleWith $100 human genomes, doctors can determine which drugs will be effective for your genotypeBiological NetworksGene regulatory network: two genes are connected if the expression of one gene modulates expression of another one by either activation or inhibitionProtein 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;Background KnowledgeCell 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) ;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 inhibitionAn example.Sources: http://www.ornl.gov/sci/techresources/Human_Genome/graphics/slides/images/REGNET.jpgWhy 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;Learning Causal RelationshipsHigh-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 onWe have to have a lot of carefully controlled time series data to infer thisKegghttp://www.genome.jp/kegg/pathway.htmlPathgenMicroarray dataGene up-regulate, down-regulate;GenesSamplesLearning from microarray dataRecurrent Neural NetworksBayesian learning approachesAIRnet: Asynchronous Inference of Regulatory networks1. Classify gene levels using k-means clustering2. Compute influence vectors (i.v.)3. Convert i.v.'s into a sorted list of edges4. Use Kruskal's algorithm to find the minimum-cost spanning treeInfluence Vectors1. 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 comparisonsClockwise 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.Euploid network →← Trisomic networkGraph showing differences between Euploid and TrisomicGraph highlighting differences between Euploid and Trisomic using multiple datasetsDREAM in-silico challengeUsing phylogenetic profiles to predict protein functionBasic 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 proteinPhylogenetic ProfileThe 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 0Protein P2: 0 0 1 0 1 1 0 0Protein P3: 1 0 0 1 0 1 0 0For 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.Phylogenetic ProfileProteinsSpeciesPellegrini 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,.
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