Receptor bias

Figure 1 Bias in the chemokine system and other 7TMRs. Biased signaling describes the ability of a receptor to induce different signaling pathways or cellular events. Thereby, different ligands may activate different pathways via the same receptor (ligand bias, A), or the same ligand induces different outcomes at different receptors (receptor bias, B). Also the cell or tissue that "hosts" the ligand receptor interaction can modulate the induced signaling pathway (tissue bias, C).

Two-state model, a model of proteins that coexists in two states controlled by an equilibrium constant. Molecules with selective affinity for one of the states will produce a bias in that state upon binding to the system. Two-state theory was conceived to describe the function of ion channels but also has relevance to receptors (see Chapter 3.7). 

Crestani F, Lorez M, Baer K et al (1999) Decreased GABAA-receptor clustering results in enhanced anxiety and a bias for threat cues. Nat Neurosci 2 833-839... 

Screening the molecular heterogeneity of receptor expression in endothelial cell surfaces is required for the development of vascular-targeted therapies. First, as opposed to targeting purified proteins as discussed above, membrane-bound receptors are more likely to preserve their functional conformation, which can be lost upon purification and immobilization outside the context of intact cells. Moreover, many cell surface receptors require the cell membrane microenvironment to function so that protein-protein interaction may occur. Finally, combinatorial approaches may allow the selection of cell membrane ligands in a functional assay and without any bias about the cellular surface receptor. Therefore, even as yet unidentified receptors may be targeted. 

One can identify two major categories of uncertainty in EIA data (scientific) uncertainty inherited in input data (e.g., incomplete or irrelevant baseline information, project characteristics, the misidentification of sources of impacts, as well as secondary, and cumulative impacts) and in impact prediction based on these data (lack of scientific evidence on the nature of affected objects and impacts, the misidentification of source-pathway-receptor relationships, model errors, misuse of proxy data from the analogous contexts) and decision (societal) uncertainty resulting from, e.g., inadequate scoping of impacts, imperfection of impact evaluation (e.g., insufficient provisions for public participation), human factor in formal decision-making (e.g., subjectivity, bias, any kind of pressure on a decision-maker), lack of strategic plans and policies and possible implications of nearby developments (Demidova, 2002). 

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