Sources of cell-to-cell variability in canonical nuclear factor-κB (NF-κB) signaling pathway inferred from single cell dynamic images.

The canonical nuclear factor-κB (NF-κB) signaling pathway controls a gene network important in the cellular inflammatory response. Upon activation, NF-κB/RelA is released from cytoplasmic inhibitors, from where it translocates into the nucleus, subsequently activating negative feedback loops producing either monophasic or damped oscillatory nucleo-cytoplasmic dynamics. Although the population behavior of the NF-κB pathway has been extensively modeled, the sources of cell-to-cell variability are not well understood. We describe an integrated experimental-computational analysis of NF-κB/RelA translocation in a validated cell model exhibiting monophasic dynamics. Quantitative measures of cellular geometry and total cytoplasmic concentration and translocated RelA amounts were used as priors in Bayesian inference to estimate biophysically realistic parameter values based on dynamic live cell imaging studies of enhanced GFP-tagged RelA in stable transfectants. Bayesian inference was performed on multiple cells simultaneously, assuming identical reaction rate parameters, whereas cellular geometry and initial and total NF-κB concentration-related parameters were cell-specific. A subpopulation of cells exhibiting distinct kinetic profiles was identified that corresponded to differences in the IκBα translation rate. We conclude that cellular geometry, initial and total NF-κB concentration, IκBα translation, and IκBα degradation rates account for distinct cell-to-cell differences in canonical NF-κB translocation dynamics.

Identification of viruses using microfluidic protein profiling and Bayesian classification.

We present a rapid method for the identification of viruses using microfluidic chip gel electrophoresis (CGE) of high-copy number proteins to generate unique protein profiles. Viral proteins are solubilized by heating at 95 degrees C in borate buffer containing detergent (5 min), then labeled with fluorescamine dye (10 s), and analyzed using the microChemLab CGE system (5 min). Analyses of closely related T2 and T4 bacteriophage demonstrate sufficient assay sensitivity and peak resolution to distinguish the two phage. CGE analyses of four additional viruses--MS2 bacteriophage, Epstein-Barr, respiratory syncytial, and vaccinia viruses--demonstrate reproducible and visually distinct protein profiles. To evaluate the suitability of the method for unique identification of viruses, we employed a Bayesian classification approach. Using a subset of 126 replicate electropherograms of the six viruses and phage for training purposes, successful classification with non-training data was 66/69 or 95% with no false positives. The classification method is based on a single attribute (elution time), although other attributes such as peak width, peak amplitude, or peak shape could be incorporated and may improve performance further. The encouraging results suggest a rapid and simple way to identify viruses without requiring specialty reagents such as PCR probes and antibodies.