About Us

Computational focus areas

  1. Biochemical network reconstruction. We reconstruct metabolic, protein synthesis, and regulatory networks for industrially-relevant microorganisms, pathogenic microbes, and model organisms using a combination of bioinformatic approaches and manual curation. The reconstructions are made publicly available through the BIGG database.

    • Chang, R.L., Ghamsari, L., Manichaikul, A., Home, E.F.Y., Balaji, S., Fu, W., Shen, Y., Hao, T., Palsson, B.O., Salehi-Ashtiani, K., and Papin, J.A. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism, Molecular Systems Biology, 7:518 (2011).

    • Feist, A.M., Herrgard, M.J., Thiele, I., Reed, J.L., Palsson, B.Ø., "Reconstruction of Biochemical Networks in Microbial Organisms" Nature Reviews Microbiology, 7(2)(2009).

  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 and host-pathogen interactions.

    • Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, König R, Palsson BO. Large-scale in silico modeling of metabolic interactions between cell types in the human brain, Nature Biotechnology, 28 (12):1279–1285 (2010).

    • Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Bio, 6:422 (2010).

  3. Development of constraint-based modeling, reconstruction methods, and omics data integration. We develop in silico methods for constraint-based modeling, network reconstruction, and integrated analysis of multiple types of omics data. We provide free implementations of these methods to the scientific community in the form of the Cobra Toolbox.

    • Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 Nature Protocols 6, 1290–1307 (2011).

    • Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D, Weitz KK, Eils R, König R, Smith RD, Palsson BØ. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Bio 6:390 (2010).

  4. Microbial Community Interaction. We study the interplay of different members of a microbial community by integrating top-down and bottom-up approaches. We utilize multiple omics and physiological data in the context of genome-scale network reconstruction and constraint-based modeling to provide a biological framework for unraveling the key interactions microbes engage in.

Experimental focus areas

  1. Model-driven elucidation of metabolic, protein, and regulatory networks. We combine metabolic, protein synthesis and regulatory network modeling and high- and low-throughput experimental approaches in order to discover new cellular functions and mechanisms. We utilize extensively growth screen characterization, ChIP-seq, transcription start site profiling (5’-RACE), and gene expression profiling techniques.

    • Cho, B.K., Zengler, K., Qiu, Y., Knight, E.M., Park, Y.S., Barrett, C.L., Gao,Y., and Palsson, B.O., Elucidation of the Transcription Unit Architecture of the Escherichia coli K-12 MG1655 Genome, Nature Biotechnology.27:1043-1049 (2009).

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

  2. Using adaptive laboratory evolution (ALE) for biological discovery. We use adaptive 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.

    • Conrad, TM, Lewis, NE, and Palsson, BO, Microbial laboratory evolution in the era of genome-scale science, Molecular Systems Biology, 7: 509 (2011).

    • 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)

  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 laboratory evolution is often used in conjunction with strain design to optimize strain behavior.

    • Feist, A.M., Zielinski, D.C., Orth, J.D., Schellenberger, J., Herrgard, M.J., Palsson, B.O. , Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli , Metabolic Engineering, 12:3 173-186 (2010).

    • Portnoy, V.A., Herrgård, M.J., Palsson, B.Ø. Aerobic Fermentation of D-Glucose by an Evolved Cytochrome Oxidase Deficient Escherichia coli Strain, Appl Environ Microbiol. 2008 Dec;74(24):7561-9.



417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

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Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns: sbrgit@ucsd.edu

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