pathways - Computational Inference of Biological Pathways...

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Unformatted text preview: Computational Inference of Biological Pathways and Networks Pathways Ying Xu (徐鹰) Biological Pathways and Networks Biological • Biological pathways and networks provide a framework Biological for studying complex biological systems for Biological Pathways and Networks Biological • Metabolic pathway: a series of enzymatic reactions that series produce a specific product produce • Regulatory networks: pathways that regulate a cell’s behaviors, including transcription, translation, degradation, motility, ….. • Signal transduction pathway and networks: cellular cellular processes that recognize extra- or intra-cellular signals processes cellular and induce appropriate cellular responses and Biological Pathways and Networks Biological • Metabolic pathways & networks – – – – – – – – – – Carbohydrate Metabolism Energy Metabolism Lipid Metabolism Nucleotide Metabolism Amino Acid Metabolism Metabolism of Other Amino Acids Glycan Biosynthesis and Metabolism Metabolism of Cofactors Biosynthesis of Secondary Metabolites Biodegradation of Xenobiotics Biodegradation Xenobiotics Biological Pathways and Networks Biological • Metabolic pathways & networks (from KEGG) Biological Pathways and Networks Biological • Regulatory and signaling pathways and networks – Genetic Information Processing • • • • Transcription Transcription Translation Translation Sorting and Degradation Sorting Replication and Repair Replication • • • • Membrane Transport Membrane Signal Transduction Signal Ligand-Receptor Interaction Receptor Immune System Immune • • • • • Cell Motility Cell Cell Growth and Death Cell Cell Communication Cell Development Development Behavior Behavior – Environmental Information Processing – Cellular Processes Biological Pathways and Networks Biological • Regulatory/signaling pathways and networks (from KEGG) (from Biological Pathways and Networks Biological • Regulatory/signaling pathways and networks (from KEGG) (from Stimulus -> G proteins -> MAPKKK -> MAPKK -> MAPK -> response Biological Pathways and Networks Biological • Regulatory/signaling pathways and networks (from KEGG) (from Regulatory/signaling GAP1 -> Synthesis -> GAP2 -> Mitosis Pathway Prediction Pathway • Put all the information together and more …. Put – Component elements (genes, proteins, etc) – Functions, derived based on sequence, structure and motif Functions, information – Interactions, protein-DNA, protein-protein, … protein, – Kinetics information such as microarray expression data – ….. Conceptual Framework Conceptual Literature and database search – to infer initial “conceptual” models for a target pathway – to collect information about which genes are involved in a specific pathway and their interaction relationships Pathways to utilize phosphonates: 2-AEP pathway (dominate in Gram-positive microbes): NH2CH2CH2PO3H transaminase 2-aminoethylphosphonate How does a microbe implement these biochemistry processes under what conditions (signaling and regulation)? COHCH2PO3H2 phosphoacetaldehyde phosphonatase CHOCH3 + Pi acetaldehyde Component Genes Component Information derived from microarray gene expression data – Differentially expressed genes under designed conditions may indicate genes relevant to a particular biological process – Co-expressed genes may work in the same biological process – Microarray data provide causality information { initial gene list } Component Genes Component Information derived from protein sequence, structure, motifs and association – a protein’s biological function determines its role in a pathway – protein structures can provide detailed functional information – computer programs for predicting protein functions, based on • Sequence information – homology prediction • Structural information – remote homology prediction; and detailed functional inference based on protein structures • Identified motifs Component Genes through Association Component Information derived from operons – prokaryotic organisms often use operons as an efficient way to organize genes that encode proteins working in the same pathway – there are computer programs for operon predictions Protein-DNA Interactions Protein Information derived from protein-DNA interactions – Regulatory networks generally consist of protein-DNA interactions – Prediction of regulatory elements (binding sites) Genes regulated by the same regulatory protein (e.g., transcription factor) have similar binding motifs in their promoter regions – by identifying conserved sequence motifs in the promoter regions, one can predict regulatory binding sites, and – and derive “co-regulation” relationships TGTGAAAGACTGTTTTTTTGATCGTTTTGACAAAAATGGAAGTCCACA AAGTCCACATTGATTATTTGCACGGCGTCACACTTTGCTATCCCATAG TGATGTACTGCATGTATGCAAAGGACGTCAGATTACCGTGCAGTACAG TAAACGATTCCACTAATTTATTCCATGTCACTCTTTTCGCATCTTTGT ACATTACCGCCAATTCTGTAACAGAGATCACACAAAGCGACGGTGGGG ACTTTTTTTTCATATGCCTGACGGAGTTGACACTTGTAAGTTTTCAAC Protein-DNA Interactions Protein • Co-expression data analysis • Identification of transcription factors • Protein-DNA arrays (ChIP-chip) – for identification of transcription factor binding to cis regulatory for cis regulatory motifs motifs Protein-Protein Interactions Protein Information derived from protein-protein interaction data – a significant portion of a pathway often is made of protein-protein interactions – protein-protein interactions can be inferred experimentally or computationally Yeast two hybrid systems Protein arrays Mass spec … Protein-Protein Interactions Protein H. pylori Interaction map Synechococcus sp. homology search homology search DIP Database Synechococcus sp. ORF1071 ORF1076 159 4 153 298 Protein-Protein Interactions Protein ORFs 89 microbe genomes orf1034:1110110110010111110100010100000000111100011111110110111010101 orf1036:1011110001000001010000010010000000010111101110011011010000101 orf1037:1101100110000001110010000111111001101111101011101111000010100 orf1038:1110100110010010110010011100000101110101101111111111110000101 orf1039:1111111111111111111111111111111111111111101111111111111111101 orf104: 1000101000000000000000101000000000110000000000000100101000100 orf1040:1110111111111101111101111100000111111100111111110110111111101 orf1041:1111111111111111110111111111111101111111101111111111111111101 orf1042:1110100101010010010110000100001001111110111110101101100010101 orf1043:1110100110010000010100111100100001111110101111011101000010101 orf1044:1111100111110010010111010111111001111111111111101101100010101 orf1045:1111110110110011111111111111111101111111101111111111110010101 orf1046:0101100000010001011000000111110000010100000001010010100000000 orf1047:0000000000000001000010000001000100000000000000010000000000000 orf105: 0110110110100010111101101010111001101100101111100010000010001 orf1054:0100100110000001100001000100000000100100100001000100100000000 genes unique to WH8102 genes existing in all 89 genomes Phylogenetic profiling analysis A subset forms a cluster if and only they form a valley genes related to “phosphorus” Partial Networks Partial Parts-list Interactions Partial networks “complete” network Techniques for Network Inference Techniques • Network work inference based on time-course data Collectively, how genes are wired together (representing two modes: activation or inhibition) to achieve the observed gene expression patterns? -Bayesian networks -Petri nets -Different equations Take-Home Message Take • Inference of biological pathways and networks Inference represents one of the most challenging problems represents • It is possibly doable for inference of microbial pathways It & networks • Data mining is the key! • Many challenges are ahead Homework Homework • Suppose that you are asked to carry out a project to Suppose decipher the metabolic pathway of sugar metabolism in E. coli, please write a bioinformatic research plan of how E. please bioinformatic research you might want to accomplish your goal. In you research plan, you should indicate what experiments you will do and what data you will collect; and also you should be specific about what tools you will use in your bioinformatics study (try to apply all the knowledge you have learned from this section of the course, plus Internet search). Internet ...
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