User:Jeremy Zucker

From Proteopedia

Jeremy Zucker has devoted his research to the representation, integration, modeling, and simulation of metabolic pathways since 2002. In the area of representation, he was an early contributor to the SBML and BioPAX communities, releasing Biocyc2SBML to represent genome-scale models in Systems Biology Markup Language, and BioPAX level 1 to represent metabolic pathways in the web ontology language (OWL).

In the area of pathway integration, Jeremy initially proposed and led the effort to integrate the reactions, and metabolites in the Palsson group's FBA E. coli model with EcoCyc, and the results of the effort were described at http://bio.freelogy.org/wiki/Debugging_the_bug and published as supplementary material in Nature Molecular Systems Biology paper\cite{Feist2008Genome}.

In the area of modeling, Jeremy is an active developer for Pathway-tools, which automatically reconstructs metabolic networks from annotated genomes. Building on top of this platform, Jeremy has subsequently curated genome-scale metabolic models for Buchnera, Prochlorococcus\cite{Kettler2007Patterns}, Synechococcus, Rhodococcus, Mesoplasma, Ralstonia, Streptococcus pneumonia, Clostridium phytofermentans, and M. tuberculosis.

In the area of simulation, Jeremy was a lead developer for the fluxor BioSPICE project\cite{Segre2003From} and used this software to analyze genome-scale metabolic models such as M. tb, Buchnera, E. coli, Streptococcus pneumonia, Synechocystis, and Streptomyces coelicolor. In collaboration with Aaron Brandes, James Galagan, and Desmond Lun, Jeremy developed the E-flux method to integrate expression data with metabolic models to constrain the feasible space of possible steady-state solutions.

Jeremy also frequently teaches the representation, integration, modeling and simulation of metabolic pathways to the computational biology community. Some highlights include ISMB tutorial presenter for "Semantic aggregation, integration, and inference of pathway data", guest lecturer in Systems Biology at the Massachusetts College of Pharmacy, and Teaching Assistant at Harvard University for the three courses: Genomics, Computing, Economics and Society; A Systems Approach to Biology; and Computational Biology for Infectious Disease Researchers

@article{Feist2008Genome,

  author = {Feist, Adam  M.  and Henry, Christopher  S.  and Reed, Jennifer  L.  and Krummenacker, Markus   and Joyce, Andrew  R.  and Karp, Peter  D.  and Broadbelt, Linda  J.  and Hatzimanikatis, Vassily   and Palsson, Bernhard  O. },
  citeulike-article-id = {1524073},
  doi = {10.1038/msb4100155},
  journal = {Mol Syst Biol},
  keywords = {biocyc, ecocyc, ecoli, fba},
  month = {June},
  posted-at = {2008-07-17 18:51:37},
  priority = {2},
  title = {A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information},
  url = {http://dx.doi.org/10.1038/msb4100155},
  volume = {3},
  year = {2007}

}

@article{Segre2003From,

  abstract = {Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steady-state models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.},
  address = {Lipper Center for Computational Genetics, Harvard Medical School, Boston, Massachusetts, USA.},
  author = {Segr\`{e}, D.  and Zucker, J.  and Katz, J.  and Lin, X.  and D'haeseleer, P.  and Rindone, W. P.  and Kharchenko, P.  and Nguyen, D. H.  and Wright, M. A.  and Church, G. M. },
  citeulike-article-id = {822475},
  doi = {10.1089/153623103322452413},
  issn = {1536-2310},
  journal = {OMICS},
  keywords = {biocyc, fba, kinetics, metabolic-reconstruction},
  number = {3},
  pages = {301--316},
  posted-at = {2008-07-21 23:20:44},
  priority = {2},
  title = {From annotated genomes to metabolic flux models and kinetic parameter fitting.},
  url = {http://dx.doi.org/10.1089/153623103322452413},
  volume = {7},
  year = {2003}

}

@article{kettler2007patterns,

  abstract = {Prochlorococcus is a marine cyanobacterium that numerically dominates the mid-latitude oceans and is the smallest known oxygenic phototroph. Numerous isolates from diverse areas of the world's oceans have been studied and shown to be physiologically and genetically distinct. All isolates described thus far can be assigned to either a tightly clustered high-light (HL)-adapted clade, or a more divergent low-light (LL)-adapted group. The 16S rRNA sequences of the entire Prochlorococcus group differ by at most 3\%, and the four initially published genomes revealed patterns of genetic differentiation that help explain physiological differences among the isolates. Here we describe the genomes of eight newly sequenced isolates and combine them with the first four genomes for a comprehensive analysis of the core (shared by all isolates) and flexible genes of the Prochlorococcus group, and the patterns of loss and gain of the flexible genes over the course of evolution. There are 1,273 genes that represent the core shared by all 12 genomes. They are apparently sufficient, according to metabolic reconstruction, to encode a functional cell. We describe a phylogeny for all 12 isolates by subjecting their complete proteomes to three different phylogenetic analyses. For each non-core gene, we used a maximum parsimony method to estimate which ancestor likely first acquired or lost each gene. Many of the genetic differences among isolates, especially for genes involved in outer membrane synthesis and nutrient transport, are found within the same clade. Nevertheless, we identified some genes defining HL and LL ecotypes, and clades within these broad ecotypes, helping to demonstrate the basis of HL and LL adaptations in Prochlorococcus. Furthermore, our estimates of gene gain events allow us to identify highly variable genomic islands that are not apparent through simple pairwise comparisons. These results emphasize the functional roles, especially those connected to outer membrane synthesis and transport that dominate the flexible genome and set it apart from the core. Besides identifying islands and demonstrating their role throughout the history of Prochlorococcus, reconstruction of past gene gains and losses shows that much of the variability exists at the "leaves of the tree," between the most closely related strains. Finally, the identification of core and flexible genes from this 12-genome comparison is largely consistent with the relative frequency of Prochlorococcus genes found in global ocean metagenomic databases, further closing the gap between our understanding of these organisms in the lab and the wild.},
  author = {Kettler, Gregory C  C.  and Martiny, Adam C  C.  and Huang, Katherine   and Zucker, Jeremy   and Coleman, Maureen L  L.  and Rodrigue, Sebastien   and Chen, Feng   and Lapidus, Alla   and Ferriera, Steven   and Johnson, Justin   and Steglich, Claudia   and Church, George M  M.  and Richardson, Paul   and Chisholm, Sallie W  W. },
  citeulike-article-id = {2400157},
  doi = {10.1371/journal.pgen.0030231},
  issn = {1553-7404},
  journal = {PLoS Genet},
  keywords = {chlorophyll, debugging-the-bug, metabolic-reconstruction, prochlorococcus},
  month = {December},
  number = {12},
  posted-at = {2008-07-21 23:24:50},
  priority = {2},
  title = {Patterns and Implications of Gene Gain and Loss in the Evolution of Prochlorococcus.},
  url = {http://dx.doi.org/10.1371/journal.pgen.0030231},
  volume = {3},
  year = {2007}

}