Jennifer Reed

Associate Professor

3639 Engineering Hall
1415 Engineering Drive
Madison, WI 53706

Ph: (608) 262-0188
Fax: (608) 262-5434
reed@engr.wisc.edu

Primary Affiliation:
Chemical and Biological Engineering

Additional Affiliations:
Biomedical Engineering,


Profile Summary

Systems biology utilizes both experimental and computational approaches to study biological networks at a systems- or network-level, in order to understand and predict cellular behavior. Our group studies metabolism and regulation by combining computational and experimental approaches. Overall, we use computational models to study biological systems, engineer cells, and expand our knowledge of the underlying mechanisms behind observed cellular behaviors. We are interested in studying microbes (and microbial interactions) involved in metabolic engineering, bioremediation and health applications. Our models enable the integration of diverse sets of experimental data to predict the structure and activity of cellular networks. With regard to structure, we are interested in identifying novel enzymes and/or reactions, transcriptional regulatory interactions, and inter-species interactions to further elucidate genotype-phenotype relationships. We are also interested in quantifying network activities using a variety of experimental (e.g., 13C metabolic flux analysis) and computational tools (e.g., constraint-based and kinetic models). Developed models allow us to systematically evaluate the capabilities of different organisms from a network-based perspective and to identify ways in which genetic manipulations could enhance desired activities, such as chemical production.

Education

  • BS, University of California, San Diego
  • MS, University of California, San Diego
  • PhD, University of California, San Diego

Research Interests

  • Systems biology
  • Metabolic model development & analysis
  • Metabolic engineering
  • Biofuels, bioremediation, & biotechnology

Awards, Honors and Societies

  • Irwin and Joan Jacobs Fellow (2000-2001)
  • University of California Faculty Fellow (2005-2007)
  • NSF Career Awardee (2011)
  • DOE Early Career Awardee (2012)
  • Presidential Early Career Award for Scientists and Engineers (2013)
  • Named as “Scientist to Watch” by The Scientist (2013)
  • Kavli Fellow (2014)
  • Vilas Faculty Early Career Investigator Award (2014)
  • Vilas Research Investigator Award (2014)

Publications

  • Long MR, Ong WK, and JL Reed. Computational Methods in Metabolic Engineering for Strain Design. Current Opinion in Biotechnology. 34:135-141 (2015).
  • Kim J and JL Reed. Refining Metabolic Models and Accounting for Regulatory Effects. Current Opinion in Biotechnology. 29:34-38 (2014)
  • Tervo CJ and JL Reed. Experimental Design Tool for Systematizing Metabolic Discoveries and Model Development. Genome Biology, 13(12):R116 (2012).
  • Kim J and JL Reed. RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations. Genome Biology, 13(9):R78 (2012).
  • Barua D*, Kim J*, and JL Reed. An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Computational Biology, 6(10):e1000970 (2010).
  • Kim J, and JL Reed. OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Systems Biology, 4:53 (2010). (Listed as one of the Journal’s Highly Accessed Articles).
  • Reed JL, Patel TR, Chen KH, et al. Systems Approach to Refining Genome Annotation: Prediction and Validation of Gene Functions. Proc Natl Acad Sci U S A. 103(46):17480-17484 (2006).
  • Covert MW, Knight EM, Reed JL, Herrgard MJ, and Palsson BO. Integrating high-throughput data and computational models leads to E. coli network elucidation. Nature 429: 92-96 (2004).


*authors contributed equally

Courses

Fall 2015-2016

  • CBE 255 - Introduction to Chemical Process Modeling

  • CBE 990 - Thesis-Research
  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 790 - Master\'s Research or Thesis
  • CBE 599 - Special Problems
  • CBE 255 - Introduction to Chemical Process Modeling
  • CBE 990 - Thesis-Research
  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 790 - Master\'s Research or Thesis
  • CBE 699 - Advanced Independent Studies
  • CBE 599 - Special Problems
  • Profile Summary

    Systems biology utilizes both experimental and computational approaches to study biological networks at a systems- or network-level, in order to understand and predict cellular behavior. Our group studies metabolism and regulation by combining computational and experimental approaches. Overall, we use computational models to study biological systems, engineer cells, and expand our knowledge of the underlying mechanisms behind observed cellular behaviors. We are interested in studying microbes (and microbial interactions) involved in metabolic engineering, bioremediation and health applications. Our models enable the integration of diverse sets of experimental data to predict the structure and activity of cellular networks. With regard to structure, we are interested in identifying novel enzymes and/or reactions, transcriptional regulatory interactions, and inter-species interactions to further elucidate genotype-phenotype relationships. We are also interested in quantifying network activities using a variety of experimental (e.g., 13C metabolic flux analysis) and computational tools (e.g., constraint-based and kinetic models). Developed models allow us to systematically evaluate the capabilities of different organisms from a network-based perspective and to identify ways in which genetic manipulations could enhance desired activities, such as chemical production.


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