Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data

Kevin A. Janes, Jason R. Kelly, Suzanne Gaudet, John Albeck, Peter K. Sorger, Douglas A. Lauffenburger

Research output: Contribution to journalArticle

92 Citations (Scopus)

Abstract

Biological signaling networks process extracellular cues to control important cell decisions such as death-survival, growth-quiescence, and proliferation-differentiation. After receptor activation, intracellular signaling proteins change in abundance, modification state, and enzymatic activity. Many of the proteins in signaling networks have been identified, but it is not known how signaling molecules work together to control cell decisions. To begin to address this issue, we report the use of partial least squares regression as an analytical method to glean signal-response relationships from heterogeneous multivariate signaling data collected from HT-29 human colon carcinoma cells stimulated to undergo programmed cell death. By partial least squares modeling, we relate dynamic and quantitative measurements of 20-30 intracellular signals to cell survival after treatment with tumor necrosis factor alpha (a death factor) and insulin (a survival factor). We find that partial least squares models can distinguish highly informative signals from redundant uninformative signals to generate a reduced model that retains key signaling features and signal-response relationships. In these models, measurements of biochemical characteristics, based on very different techniques (Western blots, kinase assays, etc.), are grouped together as covariates, showing that heterogenous data have been effectively fused. Importantly, informative protein predictors of cell responses are always multivariate, demonstrating the multicomponent nature of the decision process.

Original languageEnglish (US)
Pages (from-to)544-561
Number of pages18
JournalJournal of Computational Biology
Volume11
Issue number4
DOIs
StatePublished - Sep 28 2004
Externally publishedYes

Fingerprint

Tumor Necrosis Factor
Partial Least Squares Regression
Apoptosis
Multivariate Data
Cell death
Least-Squares Analysis
Cues
Cell
Intracellular Signaling Peptides and Proteins
Partial Least Squares
Proteins
Protein
Survival
Cells
Cell Survival
Colon
Cell Death
Phosphotransferases
Insulin
Tumor Necrosis Factor-alpha

Keywords

  • Apoptosis
  • Insulin
  • Partial least squares (PLS)
  • Principal component analysis (PCA)
  • Tumor necrosis factor-alpha (TNF-α)

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

Cite this

Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data. / Janes, Kevin A.; Kelly, Jason R.; Gaudet, Suzanne; Albeck, John; Sorger, Peter K.; Lauffenburger, Douglas A.

In: Journal of Computational Biology, Vol. 11, No. 4, 28.09.2004, p. 544-561.

Research output: Contribution to journalArticle

Janes, Kevin A. ; Kelly, Jason R. ; Gaudet, Suzanne ; Albeck, John ; Sorger, Peter K. ; Lauffenburger, Douglas A. / Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data. In: Journal of Computational Biology. 2004 ; Vol. 11, No. 4. pp. 544-561.
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