Department of Chemical Engineering, École Polytechnique de Montréal, PO Box 6079, Centre-ville Station, Montréal, Québec, Canada H3C 3A7.
J Mol Recognit. 2012 Apr;25(4):208-15. doi: 10.1002/jmr.2172.
Surface plasmon resonance-based biosensors are now acknowledged as robust and reliable instruments to determine the kinetic parameters related to the interactions between biomolecules. These kinetic parameters are used in screening campaigns: there is a considerable interest in reducing the experimental time, thus improving the throughput of the surface plasmon resonance assays. Kinetic parameters are typically obtained by analyzing data from several injections of a given analyte at different concentrations over a surface where its binding partner has been immobilized. It has been already proven that an iterative optimization approach aiming at determining optimal analyte injections to be performed online can significantly reduce the experimentation time devoted to kinetic parameter determination, without any detrimental effect on their standard errors. In this study, we explore the potential of this iterative optimization approach to further reduce experiment duration by combining it with the simultaneous injection of two analytes.
基于表面等离子体共振的生物传感器现在被公认为是确定与生物分子相互作用相关的动力学参数的强大而可靠的仪器。这些动力学参数用于筛选实验：人们有很大的兴趣来减少实验时间，从而提高表面等离子体共振分析的通量。动力学参数通常是通过分析在固定有其结合配偶体的表面上以不同浓度多次注入给定分析物的数据来获得的。已经证明，旨在确定要在线执行的最佳分析物注入的迭代优化方法可以大大减少用于确定动力学参数的实验时间，而对其标准误差没有任何不利影响。在这项研究中，我们通过将其与同时注入两种分析物相结合，探索了这种迭代优化方法通过进一步减少实验时间的潜力。
