Fluctuation analysis of activity biosensor images for the study of information flow in signaling pathways.

作者: Marco Vilela , Nadia Halidi , Sebastien Besson , Hunter Elliott , Klaus Hahn

DOI: 10.1016/B978-0-12-405539-1.00009-9

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摘要: Abstract Comprehensive understanding of cellular signal transduction requires accurate measurement the information flow in molecular pathways. In past, has been inferred primarily from genetic or protein–protein interactions. Although useful for overall signaling, these approaches are limited that they typically average over populations cells. Single-cell data signaling states emerging, but usually snapshots a particular time point to averaging whole cell. However, many pathways activated only transiently specific subcellular regions. Protein activity biosensors allow spatiotemporal activation molecules living These contain highly complex, dynamic can be parsed out and space compared with other events as well changes cell structure morphology. We describe this chapter use computational tools correct, extract, process time-lapse images biosensors. one explore biosensor signals multiplexed approach order reconstruct sequence consequently topology underlying pathway. The extraction information, dynamics topology, provides insight into how inputs network translated its biochemical mechanical outputs.

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