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Sci. STKE, 31 August 2004
Vol. 2004, Issue 248, p. pl11
[DOI: 10.1126/stke.2482004pl11]


Quantitative Information Management for the Biochemical Computation of Cellular Networks

Fabien Campagne1*, Susana Neves2, Chiung-wen Chang2, Lucy Skrabanek1, Prahlad T. Ram2, Ravi Iyengar2, and Harel Weinstein1,3,4

1Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, NY, 10021, USA.
2Department of Pharmacology and Biological Chemistry; Weill Medical College of Cornell University, New York, NY, 10021, USA.
3Institute for Computational Biomedicine; Weill Medical College of Cornell University, New York, NY, 10021, USA.
4Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.

Abstract: Understanding complex protein networks within cells requires the ability to develop quantitative models and to numerically compute the properties and behavior of the networks. To carry out such computational analysis, it is necessary to use modeling tools and information management systems (IMSs) where the quantitative data, associated to its biological context, can be stored, curated, and reliably retrieved. We have focused on the biochemical computation of cellular interactions and developed an IMS that stores both quantitative information on the cellular components and their interactions, and the basic reactions governing those interactions. This information can be used to construct pathways and eventually large-scale networks. This system, SigPath, is available on the Internet ( Key features of the approach include (i) the use of background information (for example, names of molecules, aliases, and accession codes) to ease data submission and link this quantitative database with other qualitative databases, (ii) a strategy to allow refinement of information over time by multiple users, (iii) the development of a data representation that stores both qualitative and quantitative information, and (iv) features to assist contributors and users in assembling custom quantitative models from the information stored in the IMS. Currently, models assembled in SigPath can be automatically exported to several computing environments, such as Kinetikit/Genesis, Virtual Cell, Jarnac/JDesigner, and JSim. We anticipate that, when appropriately populated, such a system will be useful for large-scale quantitative studies of cell-signaling networks and other cellular networks. SigPath is distributed under the GNU General Public License.

*Corresponding author. Weill Medical College of Cornell University, c/o Rockefeller University, Smith Hall 410, Box 270, 1230 York Avenue, New York, NY 10021, USA; e-mail, fac2003{at}

Citation: F. Campagne, S. Neves, C.-w. Chang, L. Skrabanek, P. T. Ram, R. Iyengar, H. Weinstein, Quantitative Information Management for the Biochemical Computation of Cellular Networks. Sci. STKE 2004, pl11 (2004).

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Electronic Data Sources for Kinetic Models of Cell Signaling.
G. V. HarshaRani, S. J. Vayttaden, and U. S. Bhalla (2005)
J. Biochem. 137, 653-657
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