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The Map of Life Notes on Ch.13 Judith Molka-Danielsen Gene research • In 1987 Nature Magazine article stated it found the gene for manic depression. It located the gene responsible as chromosome 11. Later reports refuted this discovery, and laid claim to other chromosomes responsible ie: 6, 13, 15, and 18. • Barabasi says this is not conflicting results. • These discoveries demonstrate that most illnesses are not caused by a single malfunctioning gene. Rather, "several genes interacting through a complex network hidden within our cells are simultaneously responsible." New Research Field: Bioinformatics • Scientists were originally not aware of the fact that not one gene, but actually a system of genes, encode for particular traits, or characteristics; as they are developmentally regulated in their gene expression. • Unknown are which, genes work together, in developmentally regulating the expression, and systematic development, of proteins themselves. • This has led to the explosion of the new field known as bioinformatics. • Scientists, and biologists, are taking a more computational approach to molecular biology, and genomic data/DNA itself. Genome project - of parts • • • On June 26, 2000, Bill Cllinton announced the decoding of the 3 billion letters of the human genome, saying we have been handed the "book of life." Barabasi says," We are repeatedly told that everything from our personality to future medical history is encoded in this book. Can you read it? Let me share a secret with you: Neither can biologists or doctors." Barabasi agrees that the genome project is a triumph in reducing complex living systems to their smallest parts. But he stresses in order to really understand how illnesses work, we need to look at how the cell functions as a network. On June 26, 2000 President Clinton, with J. Craig Venter, left, and Francis Collins, announces completion of "the first survey of the entire human genome. The Human Genome Project Complex systems Made of many non-identical elements connected by diverse interactions. NETWORK Networks of Cells • Cells function as a network, because they are instructed to do so by a network of genes themselves, which encode the function of the cells. • Protein interactions, and developmental pathways, ultimately determine the function of a particular trait, or characteristic, which is ultimately determined by the underlying genetic pathways, or networks, themselves. Networks of Cells • Networks in the cell appear at many levels. • They include protein-protein interaction networks (redlines), proteingene interactions (green-lines) and metabolic networks (bottom). • They together form what is often called the "cellular network." GENOME protein-gene interactions PROTEOME protein-protein interactions METABOLISM Bio-chemical reactions Citrate Cycle METABOLISM Bio-chemical reactions Citrate Cycle A few resources: http://www.hos.ufl.edu/meteng/HOS62312002/DATABASESANDPATHWAYS2004.htm • BASIC WWW RESOURCES • NCBI http://www.ncbi.nlm.nih.gov/ Entrez nucleotide and protein data bases; Blast similarity search programs. • TIGR http://www.TIGR.org The Institute for Genomic Research. Annotated Arabidopsis and rice genomes. TIGR Gene Indices are an analysis of the transcribed sequences represented in the world's public EST data (contig assembly, analysis of expression patterns). • ExPASy Translate Tool http://www.expasy.ch/tools/dna.html Translates a DNA sequence in all 6 frames • METABOLIC PATHWAY RESOURCES • Swiss-Prot Enzyme http://www.expasy.ch/enzyme/ Enzyme nomenclature data base (linked to SWISS-PROT protein database, BRENDA, WIT, etc) • BRENDA http://brenda.bc.uni-koeln.de/ Comprehensive enzyme database. • KEGG http://www.genome.ad.jp/kegg/ The Kyoto Encyclopedia of Genes and Genomes • BioCyc, EcoCyc & MetaCyc http://BioCyc.org/ EcoCyc Encyclopedia of E. coli Genes and Metabolism; MetaCyc Metabolic Encyclopedia. Also computationally-derived Study of the topology of the metabolic network of the yeast cell. Paper sources: http://www.nd.edu/~networks/publications.htm#anchor-bio0002 Andreas Wagner and David Fell, independently concluded that the metabolic network of E. Coli is scale-free.(from http://bmsmudshark.brookes.ac.uk/fell.ht ml) The 2000 Nature paper demonstrating that the metabolism of 43 organisms is scale-free. (from: “Metabolic Networks” ) The protein-protein interaction network of yeast also has a scale-free topology: a few proteins interact with a large number of other proteins, while most proteins have only one or two links. (See: “Network Biology: Understanding the Cell’s Functional Organization”, Albert-László Barabási & Zoltán N. Oltvai, Nature Reviews, Vol 5, Feb 2004.) ”Lethality and centrality in protein networks”, Nature, 411, 41-42- (2001) H. Jeong, S. Mason. A.-L. Barabasi, and Zoltan N. Oltvai Metabolic Network Nodes: chemicals (substrates) Links: bio-chemical reactions Metabolic network Archaea Bacteria Eukaryotes Organisms from all three domains of life are scale-free networks! H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, 407 651 (2000) Yeast protein network Nodes: proteins Links: physical interactions (binding) P. Uetz, et al. Nature 403, 623-7 (2000). Topology of the protein network Nodes: proteins Links: physical interactions-binding P(k ) ~ (k k0 ) exp( k k0 ) k H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001) Preferential attachment in protein Interaction networks ki ki ( ki ) ~ t t k vs. k : increase in the No. of links in a unit time No PA: k is independent of k PA: k ~k Eisenberg E, Levanon EY, Phys. Rev. Lett. 2003 Jeong, Neda, A.-L.B, Europhys. Lett. 2003 Origin of the scale-free topology in the cell: Gene Duplication Proteins with more interactions are more likely to obtain new links: Π(k)~k (preferential attachment) Wagner 2001; Vazquez et al. 2003; Sole et al. 2001; Rzhetsky & Gomez 2001; Qian et al. 2001; Bhan et al. 2002. C. Elegans Li et al. Science 2004 Drosophila M. Giot et al. Science 2003 Studying Diseases • In cancer, scientists found that the p53 gene is responsible (when it does not function to stop cell growth). • Barabasi writes that instead of obsessing over the p53 molecule, we should focus instead on the p53 network: a sum of all molecules interacting with the p53 molecule. • Diseases are usually the result of defects in proteins, which interact with other molecules. Other defects in protein folding are the result of diseases themselves. • Some researchers try to study protein pathways and interactions. Nature 408 307 (2000) …“One way to understand the p53 network is to compare it to the Internet. The cell, like the Internet, appears to be a ‘scale-free network’.” P53 Networks, Vogelsteing, Lane and Levine suggested that the role of the p53 molecule is a hub in the p53 network. (Nature 408, 307 (2000)). p53 network (mammals) •Activation of the network (by stresses such as DNA damage, ultraviolet light and oncogenes) stimulates enzymatic activities that modify p53 and its negative regulator,MDM2. This results in increased levels of activated p53 protein. The expression of several target genes is then activated by binding of the activated p53 to their regulatory regions. These genes are involved in processes that slow down the development of tumors. For example, some genes inhibit cell-cycle progression or the development of blood vessels to feed a growing tumor; others increase cell death (apoptosis). A negative feedback loop between MDM2 and p53 restrains this network. Many other components of this network, not shown here, have been identified. Similarly, p53 activation results in a variety of other effects, including the maintenance of genetic stability, induction of cellular differentiation, and production of extra cellular matrix, cytoskeleton and secreted proteins. (Contributed by Dr. Koji Nakade) Tools for studying genes A micro-array chip can record which of the genes are active in a cell-offering unprecedented opportunities as a future diagnostic tool. For a description of how the DNA chip works, see http://www.devicelink.com/ivdt/a rchive/98/09/009.html RT² Real-Time™ Gene Expression Assay Kit • • • • • • • • • Compare gene expression levels for any human, mouse or rat gene with our RT² Real-Time™ Gene Expression Assay Kits. RT² Real-Time™ Gene Expression Assay Kits are compatible with the universally accepted SYBR Green dye, making them compatible with virtually every real-time PCR system. Universal: Any Gene in the Human, Mouse, or Rat GenomeReliable: Specially Designed, Validated Primer PairFlexible: SYBR® Green CompatibleGreat Value: Only $87 for 24 reactions* Each RT² kit includes PCR primers designed with a proprietary, experimentally verified algorithm. The PCR primers for each gene-specific kit are carefully designed for SYBR Green compatible real-time PCR. RT² Real-Time™ Gene Expression Assay Kits include specially formulated and optimized master mix for real-time PCR. The master mix is lyophilized for easy storage, and contains everything need for PCR...just add water, template, primers and SYBR Green. Our RT² HotStart enzyme provides superior amplification performance to that of competing enzymes. Kit Contents: Enough of the following reagents for 24 reactions: One pair of validated, gene-specific PCR primers specifically designed for real-time PCR Two vials of specially formulated real-time PCR master mix, compatible with SYBR Green detection http://www.superarray.com/product.php Personalized medicine • Barabasi says that once we understand DNA in terms of a network, scientists will be able to deliver prescription medicines catered specifically to an individual's DNA. • Second, in producing antibiotics, scientists will be able to develop drugs which can kill a particular strain of bacteria, rather than wiping out all the bacteria in the body (good and bad bacteria) as current medicines do. • He also says that this can be accomplished within the next 20 years. • Researchers claim this is possible because, Recombinant DNA technology will enable us to target, and sequence, individual bacteria, and hence to invent Antibiotics which are specific to certain strands of the bacterial genome itself. Business ties in US biotech-industry Nodes: companies: investment pharma research labs public biotechnology Links: financial R&D collaborations http://ecclectic.ss.uci.edu/~drwhite/Movie