title: Biosensor architectures for high-fidelity reporting of cellular signaling.
abstract: Understanding mechanisms of information processing in cellular signaling networks requires quantitative measurements of protein activities in living cells. Biosensors are molecular probes that have been developed to directly track the activity of specific signaling proteins and their use is revolutionizing our understanding of signal transduction. The use of biosensors relies on the assumption that their activity is linearly proportional to the activity of the signaling protein they have been engineered to track. We use mechanistic mathematical models of common biosensor architectures (single-chain FRET-based biosensors), which include both intramolecular and intermolecular reactions, to study the validity of the linearity assumption. As a result of the classic mechanism of zero-order ultrasensitivity, we find that biosensor activity can be highly nonlinear so that small changes in signaling protein activity can give rise to large changes in biosensor activity and vice versa. This nonlinearity is abolished in architectures that favor the formation of biosensor oligomers, but oligomeric biosensors produce complicated FRET states. Based on this finding, we show that high-fidelity reporting is possible when a single-chain intermolecular biosensor is used that cannot undergo intramolecular reactions and is restricted to forming dimers. We provide phase diagrams that compare various trade-offs, including observer effects, which further highlight the utility of biosensor architectures that favor intermolecular over intramolecular binding. We discuss challenges in calibrating and constructing biosensors and highlight the utility of mathematical models in designing novel probes for cellular signaling.
authors.affiliation: Sir William Dunn School of Pathology, University of Oxford, United Kingdom; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, United Kingdom. Electronic address: omer.dushek@path.ox.ac.uk.
authors.forename: Omer
authors.initials: O
authors.lastname: Dushek
authors.affiliation: Aix Marseille Université, Laboratoire d'Adhésion et Inflammation, Marseille, France; Institut National de la Santé et de la Recherche Médicale U1067, Marseille, France; Centre National de la Recherche Scientifique UMR 7333, Marseille, France.
authors.forename: Annemarie C
authors.initials: AC
authors.lastname: Lellouch
authors.affiliation: Sir William Dunn School of Pathology, University of Oxford, United Kingdom.
authors.forename: David J
authors.initials: DJ
authors.lastname: Vaux
authors.affiliation: Department of Mathematics, Imperial College London, United Kingdom. Electronic address: v.shahrezaei@imperial.ac.uk.
authors.forename: Vahid
authors.initials: V
authors.lastname: Shahrezaei
mesh_terms: D015374:Biosensing Techniques / Q000379:methods* / Q000592:standards; D003627:Data Interpretation, Statistical; D031541:Fluorescence Resonance Energy Transfer / Q000379:methods* / Q000592:standards; D008954:Models, Biological; D015588:Observer Variation; D010749:Phosphoprotein Phosphatases / Q000378:metabolism; D011485:Protein Binding; D011494:Protein Kinases / Q000378:metabolism; D012680:Sensitivity and Specificity; D015398:Signal Transduction
chemical_list: D011494:Protein Kinases; D010749:Phosphoprotein Phosphatases
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