We take a systems biology approach towards understanding the energy metabolism of microbial communities. This includes integrating biochemistry, thermodynamics, metabolite transport and utilization, meta-genomic sequencing, regulatory and metabolic network analysis, and comparative and evolutionary genomics. We aim to predict and rationalize the dominant microbially mediated metabolic activities that develop in a given type of environment, and to predict a microbe's behavior and lifestyle directly from its genomic sequence.
We are dedicated to studying the bewildering diversity of microbial life, by looking for patterns across hundreds of sequenced genomes, and attempting to extrapolate outwards from the few well-studied model organisms.
Given the glut of sequence data, comparative genomics methods are essential
to efficiently leverage existing knowledge. However, most current approaches
are limited to comparisons between closely related species. We intend to study
a large collection of bacterial genomes at the level of gene content rather
than precise sequence similarity, allowing us to take advantage of sequence
data from even remotely related species. By integrating data on gene function
and species phenotype, we intend to elucidate genotype-to-phenotype mapping,
with particular emphasis on metabolic processes and regulatory mechanisms.
(See our publications.)
During the last few years, network approaches have shown great promise as a new tool to analyze and understand complex systems. In biology, networks appear in many disparate situations ranging from food webs in ecology to biochemical interactions in molecular biology. In particular in the cell, the variety of interactions between genes, proteins and metabolites are well captured by complex networks. To one day understand the system level behavior and properties of biological systems, it is thus necessary to first understand the connections and connectivity patterns between their individual building blocks.
The complex network representation of biological systems, like protein interaction networks and metabolic networks, has revealed surprising similarities with systems far removed from biology. Consequently, the methods developed for the study of e.g. social networks will often offer important insights into the organization of biological networks. Conversely, methods devised to study biological networks can fruitfully be applied to a multitude of other problems. Our group is interested in developing both general and special methods to understand the principles behind the design and organization of biological systems. Most of the work in our group is theoretical and computational, but we are also directly involved in smaller experimental efforts.
- Nguyen DH, and D'haeseleer P. (2006) Deciphering principles of transcription regulation in eukaryotic genomes. Molecular Systems Biology, doi:10.1038/msb4100055 .
- Bussemaker, HJ. (2006) Modeling gene expression control using Omes Law. Molecular Systems Biology doi:10.1038/msb4100055 .
- Slonim N, et al. (2006) Ab initio genotype-phenotype association reveals intrinsic modularity in genetic networks. Molecular Systems Biology doi:10.1038/msb4100047 .
- Pace NR.(1997) A molecular view of microbial diversity and the biosphere. Science 276(5313):734-40.
- Tyson G.W., et al. (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature, 428:37-43.
- Rhee SK, et al. (2004) Detection of genes involved in biodegradation and biotransformation in microbial communities by using 50-mer oligonucleotide microarrays. Appl Environ Microbiol. 70(7):4303-17.
- Korbel JO, et al. (2005) Systematic association of genes to phenotypes by genome and literature mining. PLoS Biol. 3(5): e134.
- Beer M. A., Tavazoie S. (2004) Predicting gene expression from sequence. Cell 117, 185-98.
- Bochner BR, et al. (2001) Phenotype MicroArrays for High-Throughput Phenotypic Testing and Assay of Gene Function. Genome Research, 11:1246-1255.
- Doerks T., et al. (2004) Global analysis of bacterial transcription factors to predict cellular target processes. Trends in Genetics, 20(3):126-131.
- Tan K., et al. (2005) Making connections between novel transcription factors and their DNA motifs. Genome Research, 15:312-320.