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Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate
Kevin A Janes1, Jason R Kelly, Suzanne Gaudet
1Biological Engineering Division, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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.
Insights
This study uses partial least squares regression to analyze biological signaling data from colon cancer cells. The method identifies key signaling pathways that control cell death and survival decisions.
Area of Science:
- Cell Biology
- Systems Biology
- Bioinformatics
Background:
- Biological signaling networks integrate external signals to regulate critical cell fate decisions, including survival, death, growth, and differentiation.
- While many signaling proteins are known, their collective function in controlling cell decisions remains largely unelucidated.
- Understanding these complex interactions is crucial for deciphering cellular responses to stimuli.
Purpose of the Study:
- To develop and apply an analytical method for understanding signal-response relationships in biological networks.
- To investigate how intracellular signaling molecules cooperate to control cell fate decisions, specifically cell death and survival.
- To analyze heterogeneous multivariate signaling data from HT-29 human colon carcinoma cells.
Main Methods:
- Utilized partial least squares (PLS) regression, a multivariate statistical technique, to model signal-response relationships.
- Collected dynamic and quantitative measurements of 20-30 intracellular signals in HT-29 cells.
- Fused heterogeneous data from various biochemical assays (e.g., Western blots, kinase assays) into a cohesive analytical framework.
Main Results:
- PLS modeling successfully distinguished informative signals from redundant ones, enabling the creation of reduced models that preserved key signaling features.
- The models effectively integrated diverse biochemical measurements, demonstrating successful fusion of heterogeneous data.
- Identified that critical protein predictors of cell response are consistently multivariate, highlighting the multicomponent nature of cell decision-making.
Conclusions:
- Partial least squares regression is a powerful tool for dissecting complex biological signaling networks and identifying key signal-response relationships.
- Cell fate decisions, such as survival and death, are governed by the concerted action of multiple signaling components.
- This approach facilitates the integration of diverse data types to reveal the intricate mechanisms underlying cellular responses.
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