Analysis of exponential data using a noniterative technique: application to surface plasmon experiments
The analysis of experimental data of exponential type plays a central role in many
biophysical applications. We introduce a novel noniterative algorithm to analyze the
association phase and dissociation phase of surface plasmon resonance experiments. It is
shown that this algorithm can determine kinetic constants with a high level of accuracy in the
presence of significant levels of noise. This algorithm should provide a valuable alternative
to existing data analysis techniques.
biophysical applications. We introduce a novel noniterative algorithm to analyze the
association phase and dissociation phase of surface plasmon resonance experiments. It is
shown that this algorithm can determine kinetic constants with a high level of accuracy in the
presence of significant levels of noise. This algorithm should provide a valuable alternative
to existing data analysis techniques.
The analysis of experimental data of exponential type plays a central role in many biophysical applications. We introduce a novel noniterative algorithm to analyze the association phase and dissociation phase of surface plasmon resonance experiments. It is shown that this algorithm can determine kinetic constants with a high level of accuracy in the presence of significant levels of noise. This algorithm should provide a valuable alternative to existing data analysis techniques.
Elsevier