BioInformatic Tools andGenomes To LifeBy Karan SundlassGoals• Identify and characterize the molecular machines of life-- the multiprotein complexes that execute cellularfunctions and govern cell form.• Characterize gene regulatory networks.• Characterize the functional repertoire of complexmicrobial communities in their natural environments atthe molecular level.• Develop the computational methods and capabilities toadvance understanding of complex biological systemsand predict their behavior.What computational methods areneeded?• Refining raw data from lab• Analyzing data for useful information• Incorporating data from many differentsources to see overall patterns.Refining Raw DataDNADNAGenomeSequenceGenomeSequenceRNARNA ESTsProteinSequence andStructureDNA and RNA• Assemble contigs and mRNAs into wholesequences• Finding sequencing errors, alternativesplicing• Reading microarrays to determineexpression patternsProteins• Original data comes from sequencingproteins through digestion and chemicalanalysisProteins (Mass Spec)• Proteins are run on 2D-PAGE• Spot is digested, peptide fragments runthrough a mass spectrometer• Data is run through a database of mass specdata, like Protein Prospector and compared• Confirmation accomplished through furtherdigestion and mass specProteins (X-Ray Crystallography)• Proteins must be grown and crystallized• Useful to determine protein structureAnalyzing DataESTsESTsCAT, ArrayViewerCAT, ArrayViewerExpression PatternExpression PatternGenomeSequenceGenomeSequenceGRAIL, GenScanGRAIL, GenScanGenes,Regulatory RegionsGenes,Regulatory RegionsProteinSequenceProteinSequenceBlast, Motif toolsBlast, Motif toolsFunction and formFunction and formAnalyzing DataIncorporation of Related DataRight now, this information is beinggenerated at a very fast pace, and is beingstored in many different databases:• Annotated genome databases• EST cluster databases• Protein homology databasesIncorporation of Related DataUsing current tools, the followinginformation can be obtained for a gene:• Sequence• When and where it is expressed• What it codes forGenomes to LifeFunctionFunctionExpression PatternExpression PatternGene OntologyGene OntologyGene RegulatoryNetworksGene RegulatoryNetworksMetabolicPathwaysMetabolicPathwaysComplete Response MapComplete Response MapGene Regulatory Networks(GRN)• The determination of the relationships betweengenes requires the use of all the tools presented.•A study that mapped part of the cell cycleregulatory network shows the the use ofbioinformatic tools to elucidate the network.Global Analysis of the Genetic Network Controlling aBacterial Cell Cycle. Laub, M. et alScience, December 15 2000. V. 290 2144-2148.Gene Regulatory Networks(GRN)• Using DNA and RNA microarrays, madeexpression profiles at different times.• Identified genes that were expressed in cycles witha transform algorithm.• Clustered genes that expressed together• Noted changes in phenotypes at same times• Mutated CtrA, a master regulator that inhibits theexpression of G1- S phase factors.Gene Regulatory Networks(GRN)• Results:– CtrA regulates 26%of cell cycleregulated genes.– Constructed aGRN for CtrAMetabolic Pathways This study constructed a computer model of themetabolic pathway of glucose, and tested theaccuracy of the model against experimentalobservations.•The Escherichia coli MG1655 in silico metabolicgenotype: Its definition, characteristics, and capabilities.J. S. Edwards and B. O. Palsson• Proc. Natl. Acad. Sci. USA, Vol. 97, Issue 10, 5528-5533, May 9, 2000vMetabolic Pathways• Used biochemical properties of reactions betweenmetabolic enzymes and metabolites to create amatrix of interactions.• Matrix was 436 metabolites by 720 enzymecatalyzed reactions.• Found best model with flux balance analysis ofmatrix and an optimizing algorithm, verycomputationally intensive.•Compared predictions of deleted intermediaries toexperimental observations.Metabolic Pathways• Results:– 86% of predictionswere experimentallyobserved to be true– Essential centralmetabolic enzymes ofglucose pathwaydeterminedSo What?• With the continuing discovery of newGRNs and metabolic pathways, the rulesthat shape them are being more clearlyunderstood.So What?• Predictive methods are improving, and itshould be possible to recognize systems thathave not been studied from sequence,expression, and functional data.Applications• As the rules regulating organisms are understood, theability to manipulate them will grow.• Uses:• Bioremediation• Carbon Sequestration• Defense from Biological and Chemical Attacks• Tests for worker susceptibility to hazardous materialsNot So Fast• Just as the highly automated technologyused to sequence DNA allowed genomicprojects to proceed, automated techniquesneed to be developed to find GRNs andmetabolic pathways.• The algorithms to interpret and store thisdata efficiently must also be implemented.Genomes to Life• The fulfillment of the Genomes to Lifeproject’s goals will be greatly facilitated bythe development of these predictive modelsand tools to quickly find homologies inthese networks, just as the determination ofprotein function was accelerated by toolsthat found protein homology.The Future• While the DOE plans to mainly focus onmicrobial organisms, the extension of thesemodels will help the discovery of humananalogs.The Future• The GRN of a human would allowimmediate diagnosis and identification ofdrug targets for almost any genetic diseaseThe Future “Deciphering the complex relation betweenthe genotype and the phenotype will involvethe biological sciences, computer science,and quantitative analysis, all of which mustbe included in the bioengineering of the21st century.”References• Dr. Brutlag• www.doegenomestolife.org• “Computing the Genome”, Ed Uberbacher,www.ornl.gov/ornlreview/v30n3-4/genome.htm• Global Analysis of the Genetic NetworkControlling a Bacterial Cell Cycle. Laub, M. et alScience, December 15 2000. V. 290 2144-2148.• The Escherichia coli MG1655 in silico metabolicgenotype: Its definition, characteristics, andcapabilities. J. S. Edwards and B. O. Palsson.Proc. Natl. Acad. Sci. USA, Vol. 97, Issue 10,5528-5533, May 9,
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