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 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

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

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

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.

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

Mapping Metabolic Pathways

Finding Paths through Networks

Linking Metabolic Subsystems

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

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)

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

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

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

Loss of Statistical Power

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

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

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

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.

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

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

Future directions…

Spring 2008 First offering of revamped statistics course

Research Program Publications Continued incorporation into curriculum Funding Opportunities

DOE, NSF, NIH

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