Experimental focus areas

1. Model-driven elucidation of metabolic and regulatory networks. We combine metabolic and regulatory network modeling and high- and low-throughput experimental approaches in order to discover new metabolic functions and regulatory mechanisms. We utilize extensively ChIP-chip and gene expression profiling techniques to characterize the structure and function of regulatory networks in bacteria.

   • Reed, J. L. et al. "Systems Approach to Refining Genome Annotation", Proc Natl Acad Sci U S A, 103(46):17480-4 (2006)

   • Covert, M.W. et al. "Integrating high-throughput and computational data elucidates bacterial networks", Nature, 429:92-96 (2004).

2. Using adaptive evolution for biological discovery. We use laboratory evolution in defined conditions to study the dynamics of bacterial adaptation in response to environmental and genetic challenges. The adaptive trajectories are characterized both at the physiological level and by whole-genome resequencing in order to decipher the genetic basis of adaptation.

   • Herring, C. D. et al."Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale", Nature Genet, 38:1406-1412 (2006)

   • Ibarra, R.U. et al. "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth", Nature, 420:186-189 (2002).

3. Genome-scale metabolic engineering. We utilize genome-scale metabolic models to predict genetic modifications that lead to overproduction of desirable metabolic by-products. The strain designs are then implemented in vivo and adaptive evolution is used to optimize strain behavior.

   • Hua, Q. et al."Metabolic Analysis of Adaptive Evolution for In Silico-Designed Lactate-Producing Strains", Biotechnol Bioeng 95:992-1002 (2006)

   • Fong, S.S. et al. "In Silico Design and Adaptive Evolution of ''Escherichia coli'' for Production of Lactic Acid", Biotechnol Bioeng, 91:643-648 (2005).

Computational focus areas

1. Biochemical network reconstruction. We reconstruct metabolic and regulatory networks for microbial species using a combination of bioinformatic approaches and manual curation. The reconstructions are made publicly available through the BIGG database.

   • Reed, J.L. et al. "Towards multidimensional genome annotation",Nat Rev Genet, 7:130-41 (2006).

   • Feist, A.M. et al. "A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information" Mol Sys Biol 3:121 (2007)

2. Human metabolic models and their applications. We have reconstructed the global human metabolic network and are using this network as the basis for building cell- and organelle-specific metabolic models. These models are then applied to addressing a variety of questions related to metabolic disease states.

   • Duarte, N.D. et al."Global reconstruction of the human metabolic network based on genomic and bibliomic data", Proc Natl Acad. Sci U S A 104:1777-82 (2007).

   • Thiele, I. et al. "Candidate metabolic network states in human mitochondria: Impact of diabetes, ischemia, and diet", J Biol Chem, 280:11683-11695 (2005)

3. Development of constraint-based modeling and reconstruction methods. We develop in silico methods for constraint-based modeling and reconstruction and provide free implementations of these methods to the scientific community in the form of the Cobra Toolbox .

   • Becker, S.A. et al."Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox ", Nat. Protocols 2"727-738 (2007).

   • Gianchandani E. P. et al."Matrix formalism to describe functional States of transcriptional regulatory systems", PLoS Comput Biol, 2:e101 (2006).'