BioInformatic Tools and Genomes To Life By Karan Sundlass Goals Identify and characterize the molecular machines of life the multiprotein complexes that execute cellular functions and govern cell form Characterize gene regulatory networks Characterize the functional repertoire of complex microbial communities in their natural environments at the molecular level Develop the computational methods and capabilities to advance understanding of complex biological systems and predict their behavior What computational methods are needed Refining raw data from lab Analyzing data for useful information Incorporating data from many different sources to see overall patterns Refining Raw Data DNA Genome Sequence RNA ESTs Protein Sequence and Structure DNA and RNA Assemble contigs and mRNAs into whole sequences Finding sequencing errors alternative splicing Reading microarrays to determine expression patterns Proteins Original data comes from sequencing proteins through digestion and chemical analysis Proteins Mass Spec Proteins are run on 2D PAGE Spot is digested peptide fragments run through a mass spectrometer Data is run through a database of mass spec data like Protein Prospector and compared Confirmation accomplished through further digestion and mass spec Proteins X Ray Crystallography Proteins must be grown and crystallized Useful to determine protein structure Analyzing Data Genome Sequence ESTs Protein Sequence GRAIL GenScan Genes Regulatory Regions CAT ArrayViewer Expression Pattern Blast Motif tools Function and form Analyzing Data Incorporation of Related Data Right now this information is being generated at a very fast pace and is being stored in many different databases Annotated genome databases EST cluster databases Protein homology databases Incorporation of Related Data Using current tools the following information can be obtained for a gene Sequence When and where it is expressed What it codes for Genomes to Life Function Expression Pattern Gene Regulatory Networks Gene Ontology Metabolic Pathways Complete Response Map Gene Regulatory Networks GRN The determination of the relationships between genes requires the use of all the tools presented A study that mapped part of the cell cycle regulatory network shows the the use of bioinformatic tools to elucidate the network Global Analysis of the Genetic Network Controlling a Bacterial Cell Cycle Laub M et al Science December 15 2000 V 290 2144 2148 Gene Regulatory Networks GRN Using DNA and RNA microarrays made expression profiles at different times Identified genes that were expressed in cycles with a transform algorithm Clustered genes that expressed together Noted changes in phenotypes at same times Mutated CtrA a master regulator that inhibits the expression of G1 S phase factors Gene Regulatory Networks GRN Results CtrA regulates 26 of cell cycle regulated genes Constructed a GRN for CtrA Metabolic Pathways This study constructed a computer model of the metabolic pathway of glucose and tested the accuracy of the model against experimental observations The Escherichia coli MG1655 in silico metabolic genotype Its definition characteristics and capabilities J S Edwards and B O Palsson Proc Natl Acad Sci USA Vol 97 Issue 10 55285533 May 9 2000v Metabolic Pathways Used biochemical properties of reactions between metabolic enzymes and metabolites to create a matrix of interactions Matrix was 436 metabolites by 720 enzyme catalyzed reactions Found best model with flux balance analysis of matrix and an optimizing algorithm very computationally intensive Compared predictions of deleted intermediaries to experimental observations Metabolic Pathways Results 86 of predictions were experimentally observed to be true Essential central metabolic enzymes of glucose pathway determined So What With the continuing discovery of new GRNs and metabolic pathways the rules that shape them are being more clearly understood So What Predictive methods are improving and it should be possible to recognize systems that have not been studied from sequence expression and functional data Applications As the rules regulating organisms are understood the ability to manipulate them will grow Uses Bioremediation Carbon Sequestration Defense from Biological and Chemical Attacks Tests for worker susceptibility to hazardous materials Not So Fast Just as the highly automated technology used to sequence DNA allowed genomic projects to proceed automated techniques need to be developed to find GRNs and metabolic pathways The algorithms to interpret and store this data efficiently must also be implemented Genomes to Life The fulfillment of the Genomes to Life project s goals will be greatly facilitated by the development of these predictive models and tools to quickly find homologies in these networks just as the determination of protein function was accelerated by tools that found protein homology The Future While the DOE plans to mainly focus on microbial organisms the extension of these models will help the discovery of human analogs The Future The GRN of a human would allow immediate diagnosis and identification of drug targets for almost any genetic disease The Future Deciphering the complex relation between the genotype and the phenotype will involve the biological sciences computer science and quantitative analysis all of which must be included in the bioengineering of the 21st 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 Network Controlling a Bacterial Cell Cycle Laub M et al Science December 15 2000 V 290 2144 2148 The Escherichia coli MG1655 in silico metabolic genotype 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 2000v
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