Genome-scale Metabolic Reconstruction and Modeling of Microbial Life Aaron Best, Biology Matthew...
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![Page 1: Genome-scale Metabolic Reconstruction and Modeling of Microbial Life Aaron Best, Biology Matthew DeJongh, Computer Science Nathan Tintle, Mathematics Hope.](https://reader036.fdocuments.net/reader036/viewer/2022070407/56649e2d5503460f94b1d2cf/html5/thumbnails/1.jpg)
Genome-scale Metabolic Reconstruction and Modeling of Microbial Life
Aaron Best, Biology
Matthew DeJongh, Computer Science
Nathan Tintle, Mathematics
Hope College, Holland, Michigan
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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
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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
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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
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Metabolic Modeling
Genome Sequence Annotation
Genome-scale Metabolic Reconstruction
(Qualitative Framework)
Genome-scale Metabolic Modeling
(Quantitative Analyses)
Covert et al. (2001) Trends Biochem. Sciences 25:179-186.
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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
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Mapping Metabolic Pathways
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Finding Paths through Networks
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Linking Metabolic Subsystems
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Capitalizing on Common Aspects of Metabolism: Reuse of ScenariosCategory Subsystems Scenarios E. coli H. pylori L. lactis
Amino Acids 23 34 25 10 15Carbohydrates 15 39 35 6 23Cell Wall 3 8 6 4 7Lipids 3 9 9 2 1NitrogenMetabolism
1 1 1 0 0
NucleotideMetabolism
6 22 21 14 19
One Carbon 2 5 3 1 3Redox 5 3 3 1 1Sulfur 1 1 1 0 0Vitamins andCofactors
6 11 7 1 5
Totals 65 133 111 40 74
Reconstructing Networks for Other Organisms
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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)
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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
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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
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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
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Loss of Statistical Power
Statistical power (the ability to find genes that are truly differentially expressed) is lost as a result of these problems
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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
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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
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Incorporation of Research into Curriculum
Created automated pipeline that uses the SEED
Standard genetics, biochemistry and molecular biology
Tool generation and curation by students
Experimentation by students in classroom lab
Genome Annotation
Genome-scale Metabolic Network
Bioinformatics
Predicted Function
Validation of Function
Microbiology
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.
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The Projects thus far:
Bioinformatics
Microbiology
Toward the automatic reconstruction of genome-scale metabolic networks in the SEED. BMC Bioinformatics (2007), in review
4 undergraduate co-authors
1. Examination of a predicted L-threonine kinase required for coenzyme B-12 biosynthesis in Streptomyces coelicolor and Salmonella typhimurium.
2. Validation of missing gene functions in the rhamnose metabolic pathway of Bacillus, Streptomyces, and Salmonella.
3. Predicted alternative N-formylglutamate deformylase in histidine catabolism.
Collaboration with Dr. Andrei Osterman, The Burnham Institute, San Diego
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Annotation
Network Generation
Modeling
Prediction/Validation
Linking the Bioinformatics and Experimental Pieces:
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
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Future directions…
Spring 2008 First offering of revamped statistics course
Research Program Publications Continued incorporation into curriculum Funding Opportunities
DOE, NSF, NIH
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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