Abstract
In this review we discuss a number of topics related to fitting mechanistic mathematical models to experimental data. These can be divided broadly into issues to consider before beginning an experiment or fitting a model and the advantages of direct fitting of a model over other methods of analysis (e.g. null methods). We have sought to address some commonplace issues of receptor pharmacology with some real-life examples: How should dilutions be distributed along a concentration-response curve? How do problems with dilutions manifest in assay results? What assumptions are made when certain analysis models are applied? What is global data fitting and how does it work? How can it be applied to improve data analysis? How can models such as one-site and two-site fits be compared? What is the principal behind statistical comparison of data fits? It is our hope that after reading this review you will have a greater appreciation of assay planning and subsequent data analysis and interpretation which will improve the quality of the data that you generate. This article is part of a themed section on Analytical Receptor Pharmacology in Drug Discovery. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2010.161.issue-6.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
