Browsing Metabolic and Regulatory Networks with BioCyc

Citation

Latendresse M., Paley S., Karp P.D. (2012) Browsing Metabolic and Regulatory Networks with BioCyc. In: van Helden J., Toussaint A., Thieffry D. (eds) Bacterial Molecular Networks. Methods in Molecular Biology (Methods and Protocols), vol 804. Springer, New York, NY

Abstract

The BioCyc database collection at BioCyc.org integrates genome and cellular network information for more than 1,100 organisms. This method chapter describes Web-based tools for browsing metabolic and regulatory networks within BioCyc. These tools allow visualization of complete metabolic and regulatory networks, and allow the user to zoom-in on regions of the network of interest. The user can find objects of interest such as genes and metabolites within the networks, and can selectively examine the connectivity of the network. The EcoCyc database within the BioCyc collection has been extensively curated. The descriptions within EcoCyc of the Escherichia coli metabolic network and regulatory network were derived from thousands of publications. Other BioCyc databases received moderate levels of curation, or no curation at all. Those databases receiving no curation contain metabolic networks that were computationally inferred from the annotated genome sequences of each organism.

Keywords: Regulatory network, Metabolic network, Cellular network, Web interface, Highlighting, Regulatory subnetwork, Browsing, Genome database, Metabolic database.

 


Read more from SRI

  • A photo of Mary Wagner

    Recognizing the life and work of Mary Wagner 

    A cherished SRI colleague and globally respected leader in education research, Mary Wagner leaves behind an extraordinary legacy of groundbreaking work supporting children and youth with disabilities and their families.

  • Testing XRGo in a robotics laboratory

    Robots in the cleanroom

    A global health leader is exploring how SRI’s robotic telemanipulation technology can enhance pharmaceutical manufacturing.

  • SRI research aims to make generative AI more trustworthy

    Researchers have developed a new framework that reduces generative AI hallucinations by up to 32%.