title: Non-linear dimensionality reduction of signaling networks.
abstract: BACKGROUND
  Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling.
  
  
  RESULTS
  Here we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares - Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin. Prediction accuracy was highest when early activation time points in the apoptosis signaling network were used to predict apoptosis rates at later time points. Extended Isomap also outperformed PCA on the AfCS double ligand screen data. Isomap identified more functionally coherent clusters than PCA and captured more information in the first two-components. The Isomap projection performs slightly worse when more signaling networks are analyzed; suggesting that the mapping function between cues and responses becomes increasingly non-linear when large signaling pathways are considered.
  
  
  CONCLUSIONS
  We developed and applied extended Isomap approach for the analysis of cell signaling networks. Potential biological applications of this method include characterization, visualization and clustering of different treatment conditions (i.e. low and high doses of TNF) in terms of changes in intracellular signaling they induce.
authors.affiliation: Biological Engineering Division, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. s0567096@sms.ed.ac.uk
authors.forename: Sergii
authors.initials: S
authors.lastname: Ivakhno
authors.forename: J Douglas
authors.initials: JD
authors.lastname: Armstrong
mesh_terms: D000465:Algorithms; D017209:Apoptosis; D016000:Cluster Analysis; D016207:Cytokines / Q000378:metabolism; D016002:Discriminant Analysis; D004815:Epidermal Growth Factor / Q000494:pharmacology; D006801:Humans; D007328:Insulin / Q000494:pharmacology; D016018:Least-Squares Analysis; D053858:Metabolic Networks and Pathways / Q000187:drug effects; D008954:Models, Biological; D009369:Neoplasms / Q000378:metabolism; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015398:Signal Transduction / Q000187:drug effects; D014409:Tumor Necrosis Factor-alpha / Q000494:pharmacology
chemical_list: D016207:Cytokines; D007328:Insulin; D014409:Tumor Necrosis Factor-alpha; D004815:Epidermal Growth Factor