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Genome-scale Metabolic Reconstruction and Modeling of Microbial Life

Genome-scale Metabolic Reconstruction and Modeling of Microbial Life. Aaron Best, Biology Matthew DeJongh, Computer Science Nathan Tintle, Mathematics Hope College, Holland, Michigan. Timeline of Collaboration. Fall 2004/Spring 2005 Best, DeJongh brainstorming

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Genome-scale Metabolic Reconstruction and Modeling of Microbial Life

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  1. Genome-scale Metabolic Reconstruction and Modeling of Microbial Life Aaron Best, Biology Matthew DeJongh, Computer Science Nathan Tintle, Mathematics Hope College, Holland, Michigan

  2. Timeline of Collaboration • Fall 2004/Spring 2005 • Best, DeJongh brainstorming • Sabbatical planning for DeJongh • Summer 2005 • HHMI Faculty Development Grant to Best, DeJongh • Cultivate collaboration with Argonne National Lab • Student research support (NSF REU) • Fall 2005/Spring 2006 • DeJongh on 1 year sabbatical • Project-based bioinformatics course (CS/Bio/Chem students) • Summer 2006 • HHMI Faculty Development Grant to Best, DeJongh, Tintle • Student research support (NSF REU, HHMI) • Fall 2006 • Bioinformatics course runs a second time • Microbiology - Wet-lab projects to test bioinformatics hypotheses

  3. And so it begins… • Introduce the Big Picture -- Aaron • Bioinformatics Tools to Implement Reconstruction and Modeling -- Matt • Statistical Methods to Integrate Reconstructions in data analyses -- Nathan • Incorporate into the curriculum • Reflections on Interdisciplinary experience

  4. The Genomics Era • Why Microbial Life? • Diversity: majority of life on earth • Tractable: • ~400 complete genomes • Genome size range: 1 million to 10 million bases • Explore, Enrich, Exploit • Why Metabolic Modeling? • Links genotype with phenotype understanding • Allows rational engineering of organisms • Amino acid production in Corynebacterium • Bioremediation of toxic wastes from environment • Alternative energy sources -- Bioenergy You are here

  5. Genome Sequence Annotation Genome-scale Metabolic Reconstruction (Qualitative Framework) Genome-scale Metabolic Modeling (Quantitative Analyses) Metabolic Modeling Covert et al. (2001) Trends Biochem. Sciences 25:179-186.

  6. Research Method • Reverse-engineer existing metabolic models that have been created by hand • Develop software for automating genome-scale metabolic reconstructions • Verify that our software regenerates the existing metabolic models accurately • Generate metabolic reconstructions for new organisms • Use metabolic reconstructions for quantitative analysis of phenotypic data

  7. Mapping Metabolic Pathways

  8. Finding Paths through Networks

  9. Linking Metabolic Subsystems

  10. Reconstructing Networks for Other Organisms

  11. To this point… • Created process to automate generation of metabolic networks from genome annotations • Currently extending tools to create metabolic networks for new organisms • Metabolic networks as resources • Interpretation of gene expression data • Interpretation of other “omics” data (large-scale data sets)

  12. Gene Expression Data • Gene expression data from microarrays can give insight into biological processes at work in specific organisms • Each location (probe) on the microarray corresponds to a particular gene. • A typical microarray will produce data for tens of thousands of genes under defined environmental conditions

  13. Gene Expression Data • Typical analysis: • Examine all probes (locations) on the microarray for over- and under-expressed (differentially expressed) genes • Use statistical methods (e.g. Fisher’s exact test) to see which biological processes are statistically over-represented among the differentially expressed genes • This assumes we know which gene is involved in which biological process

  14. Problems • Gene Ontology (GO) terms for biological processes • Attempt to standardize terminology for gene annotations • Use of GO terms is not consistent • Dimensionality • Microarray data have few replicates • Many standard statistical methods fail because of small sample size problems

  15. Loss of Statistical Power • Statistical power (the ability to find genes that are truly differentially expressed) is lost as a result of these problems

  16. One solution • First, impose a biological structure (e.g., metabolic reconstruction) on the microarray data • Then, look for over- and under-represented groups of genes • Result, gain statistical power by grouping

  17. Where we go from here… • Step 1. Validation of metabolic reconstruction using gene expression data • Step 2. Implementation of currently available statistical methods that capitalize on an imposed data structure • Step 3. Refinement of statistical methods

  18. Bioinformatics Genome Annotation Microbiology Predicted Function Standard genetics, biochemistry and molecular biology Created automated pipeline that uses the SEED Tool generation and curation by students Experimentation by students in classroom lab Genome-scale Metabolic Network Validation of Function Incorporation of Research into Curriculum Address open scientific questions in systems biology using bioinformatics and targeted experimentation, while training undergraduates for careers in the sciences, mathematics, engineering and technology fields.

  19. The Projects thus far: Bioinformatics Toward the automatic reconstruction of genome-scale metabolic networks in the SEED. BMC Bioinformatics (2007), in review 4 undergraduate co-authors Microbiology Examination of a predicted L-threonine kinase required for coenzyme B-12 biosynthesis in Streptomyces coelicolor and Salmonella typhimurium. Validation of missing gene functions in the rhamnose metabolic pathway of Bacillus, Streptomyces, and Salmonella. Predicted alternative N-formylglutamate deformylase in histidine catabolism. Collaboration with Dr. Andrei Osterman, The Burnham Institute, San Diego

  20. Preliminary hypotheses (network analysis) Ranking via tools (e.g., functional variants, phylogenetic distribution, which parts of pathways present) Bioinformatics students Identification of candidates for missing genes Validation of networks in gene expression data Leverage networks to interpret gene expression data Microbiology/Statistics Students Linking the Bioinformatics and Experimental Pieces: Annotation Prediction/Validation Network Generation Modeling

  21. Future directions… • Spring 2008 • First offering of revamped statistics course • Research Program • Publications • Continued incorporation into curriculum • Funding Opportunities • DOE, NSF, NIH

  22. Acknowledgements • HHMI Faculty Research Development Grants • NSF REU to Computer Science Department • Argonne National Laboratories • Fellowship for the Interpretation of Genomes (FIG) • The Burnham Institute • Hope College Students: • Bioinformatics classes 2005-2006 • Microbiology class Fall 2006

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