Spectral decomposition of signaling networks

B. Parvin, N. Ghosh, Laura Heiser, M. Knapp, C. Talcott, K. Laderoute, Joe Gray, Paul Spellman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many dynamical processes can be represented as directed attributed graphs or Petri nets where relationships between various entities are explicitly expressed. Signaling networks modeled as Petri nets are one class of such graphical modeling and representations. These networks encode how different protein in specific compartments, interact to create new protein products. Initially, the proteins and rules governing their interactions are curated from literature and then refined with experimental data. Variation in these networks occurs in topological structure, size, and weights associated on edges. Collectively, these variations are quite significant for manual and interactive analysis. Furthermore, as new information is added to these networks, the emergence of new computational models becomes paramount. From this perspective, hierarchical spectral methods are proposed and applied for inferring similarities and dissimilarities from an ensemble of graphs that corresponds to reaction networks. The technique has been implemented and tested on curated signaling networks that are derived for breast cancer cell lines.

Original languageEnglish (US)
Title of host publication2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007
Pages76-81
Number of pages6
StatePublished - 2007
Externally publishedYes
Event2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007 - Honolulu, HI, United States
Duration: Apr 1 2007Apr 5 2007

Other

Other2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007
CountryUnited States
CityHonolulu, HI
Period4/1/074/5/07

Fingerprint

Decomposition
Proteins
Petri nets
Directed graphs
Cells
Breast Neoplasms
Weights and Measures
Cell Line

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Health Informatics

Cite this

Parvin, B., Ghosh, N., Heiser, L., Knapp, M., Talcott, C., Laderoute, K., ... Spellman, P. (2007). Spectral decomposition of signaling networks. In 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007 (pp. 76-81). [4221207]

Spectral decomposition of signaling networks. / Parvin, B.; Ghosh, N.; Heiser, Laura; Knapp, M.; Talcott, C.; Laderoute, K.; Gray, Joe; Spellman, Paul.

2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007. 2007. p. 76-81 4221207.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Parvin, B, Ghosh, N, Heiser, L, Knapp, M, Talcott, C, Laderoute, K, Gray, J & Spellman, P 2007, Spectral decomposition of signaling networks. in 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007., 4221207, pp. 76-81, 2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007, Honolulu, HI, United States, 4/1/07.
Parvin B, Ghosh N, Heiser L, Knapp M, Talcott C, Laderoute K et al. Spectral decomposition of signaling networks. In 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007. 2007. p. 76-81. 4221207
Parvin, B. ; Ghosh, N. ; Heiser, Laura ; Knapp, M. ; Talcott, C. ; Laderoute, K. ; Gray, Joe ; Spellman, Paul. / Spectral decomposition of signaling networks. 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007. 2007. pp. 76-81
@inproceedings{4b0e85aa92c14aa6a68e9a5d7ff2aca0,
title = "Spectral decomposition of signaling networks",
abstract = "Many dynamical processes can be represented as directed attributed graphs or Petri nets where relationships between various entities are explicitly expressed. Signaling networks modeled as Petri nets are one class of such graphical modeling and representations. These networks encode how different protein in specific compartments, interact to create new protein products. Initially, the proteins and rules governing their interactions are curated from literature and then refined with experimental data. Variation in these networks occurs in topological structure, size, and weights associated on edges. Collectively, these variations are quite significant for manual and interactive analysis. Furthermore, as new information is added to these networks, the emergence of new computational models becomes paramount. From this perspective, hierarchical spectral methods are proposed and applied for inferring similarities and dissimilarities from an ensemble of graphs that corresponds to reaction networks. The technique has been implemented and tested on curated signaling networks that are derived for breast cancer cell lines.",
author = "B. Parvin and N. Ghosh and Laura Heiser and M. Knapp and C. Talcott and K. Laderoute and Joe Gray and Paul Spellman",
year = "2007",
language = "English (US)",
isbn = "1424407109",
pages = "76--81",
booktitle = "2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007",

}

TY - GEN

T1 - Spectral decomposition of signaling networks

AU - Parvin, B.

AU - Ghosh, N.

AU - Heiser, Laura

AU - Knapp, M.

AU - Talcott, C.

AU - Laderoute, K.

AU - Gray, Joe

AU - Spellman, Paul

PY - 2007

Y1 - 2007

N2 - Many dynamical processes can be represented as directed attributed graphs or Petri nets where relationships between various entities are explicitly expressed. Signaling networks modeled as Petri nets are one class of such graphical modeling and representations. These networks encode how different protein in specific compartments, interact to create new protein products. Initially, the proteins and rules governing their interactions are curated from literature and then refined with experimental data. Variation in these networks occurs in topological structure, size, and weights associated on edges. Collectively, these variations are quite significant for manual and interactive analysis. Furthermore, as new information is added to these networks, the emergence of new computational models becomes paramount. From this perspective, hierarchical spectral methods are proposed and applied for inferring similarities and dissimilarities from an ensemble of graphs that corresponds to reaction networks. The technique has been implemented and tested on curated signaling networks that are derived for breast cancer cell lines.

AB - Many dynamical processes can be represented as directed attributed graphs or Petri nets where relationships between various entities are explicitly expressed. Signaling networks modeled as Petri nets are one class of such graphical modeling and representations. These networks encode how different protein in specific compartments, interact to create new protein products. Initially, the proteins and rules governing their interactions are curated from literature and then refined with experimental data. Variation in these networks occurs in topological structure, size, and weights associated on edges. Collectively, these variations are quite significant for manual and interactive analysis. Furthermore, as new information is added to these networks, the emergence of new computational models becomes paramount. From this perspective, hierarchical spectral methods are proposed and applied for inferring similarities and dissimilarities from an ensemble of graphs that corresponds to reaction networks. The technique has been implemented and tested on curated signaling networks that are derived for breast cancer cell lines.

UR - http://www.scopus.com/inward/record.url?scp=84885979094&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885979094&partnerID=8YFLogxK

M3 - Conference contribution

SN - 1424407109

SN - 9781424407101

SP - 76

EP - 81

BT - 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007

ER -