Inferring conict-sensitive phosphorylation dynamics

Qiong Cheng, Mitsunori Ogihara, Vineet Gupta

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

1 Citation (Scopus)

Abstract

Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. It acts as switches in cell communications. One of the most important factors contributing to the dynamics is the binding competition, which refers to the existence of more than one protein that physically binds to the same or an overlapping residue on a protein. By integrating data from different sources on the HeLa cancer cells and all available Homo sapiens (human) cells and iteratively examining the phosphorylation interfaces, we found a number of conicting interaction pairs. We extended the search into indirect conicts over direct upstream cascades and further into the whole network and calculated a min-max conict-sensitive decomposition of phosphorylation network by graph-theoretical methods. Further we used EGF-stimulation phosphoproteome data and obtained activation patterns of phosphorated proteins by soft clustering. By combining these two groupings, we calculated an optimal conict-free activation patterns using maximum bipartite matching. Sorting the average peak time of the activation patterns brought forth an activation order of min-max conict-sensitive decomposition subnetworks. We evaluated conict-sensitive phosophorylation dynamics by analyzing the importance of the conicting interactions in the whole networks, the distribution of serine/threonine/tyrosine phosphorylation, and the direct or indirect activation order of phosphorylated proteins. Compared with a previously published approach [15], our solution discovered conict-sensitive dynamics that resolved conicts and it inferred more practical causal effects consistent with EGFR signaling pathways.

Original languageEnglish (US)
Title of host publication2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
Pages430-434
Number of pages5
DOIs
StatePublished - 2011
Event2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011 - Chicago, IL, United States
Duration: Aug 1 2011Aug 3 2011

Other

Other2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011
CountryUnited States
CityChicago, IL
Period8/1/118/3/11

Fingerprint

Phosphorylation
Chemical activation
Proteins
Cells
Information Storage and Retrieval
Decomposition
Threonine
Signal transduction
HeLa Cells
Epidermal Growth Factor
Cell Communication
Serine
Cluster Analysis
Tyrosine
Signal Transduction
Sorting
Switches
Communication
Neoplasms

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Cheng, Q., Ogihara, M., & Gupta, V. (2011). Inferring conict-sensitive phosphorylation dynamics. In 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011 (pp. 430-434) https://doi.org/10.1145/2147805.2147864

Inferring conict-sensitive phosphorylation dynamics. / Cheng, Qiong; Ogihara, Mitsunori; Gupta, Vineet.

2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011. 2011. p. 430-434.

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

Cheng, Q, Ogihara, M & Gupta, V 2011, Inferring conict-sensitive phosphorylation dynamics. in 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011. pp. 430-434, 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011, Chicago, IL, United States, 8/1/11. https://doi.org/10.1145/2147805.2147864
Cheng Q, Ogihara M, Gupta V. Inferring conict-sensitive phosphorylation dynamics. In 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011. 2011. p. 430-434 https://doi.org/10.1145/2147805.2147864
Cheng, Qiong ; Ogihara, Mitsunori ; Gupta, Vineet. / Inferring conict-sensitive phosphorylation dynamics. 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011. 2011. pp. 430-434
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