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Transport and kinetic processes underlying biomolecular interactions in the BIACORE optical biosensor
V Sikavitsas1, J M Nitsche, T J Mountziaris
1Department of Chemical Engineering, State University of New York, Buffalo, New York 14260, USA.
Biotechnology Progress
|August 3, 2002
Summary
A new mathematical model for BIACORE optical biosensors couples flow channel transport with hydrogel reactions. This model enhances kinetic parameter accuracy, especially when transport limitations are significant, improving biomolecular interaction analysis.
Area of Science:
- Biophysical Chemistry
- Chemical Engineering
- Biotechnology
Background:
- BIACORE optical biosensors are crucial for studying biomolecular interactions.
- Existing models often simplify transport phenomena, potentially limiting accuracy.
- Accurate kinetic parameter estimation is vital for understanding binding affinities and reaction rates.
Purpose of the Study:
- To develop and validate a comprehensive mathematical model for BIACORE biosensors.
- To couple transport phenomena in the flow channel with reaction kinetics within the hydrogel.
- To determine conditions where this detailed model significantly improves kinetic parameter prediction.
Main Methods:
- Development of a novel mathematical model integrating fluid dynamics and hydrogel reaction-diffusion.
- Simulation of experiments using the detailed model and comparison with simpler existing models.
- Analysis of instrument response curves to assess model accuracy under varying transport limitations.
Main Results:
- The coupled model provides more accurate kinetic parameter estimation than simpler models when transport limitations are present.
- Significant influence of flow channel and hydrogel transport on instrument response is identified.
- The model extends the applicability of BIACORE to higher immobilized species concentrations.
Conclusions:
- The presented model offers enhanced accuracy for kinetic analysis in BIACORE biosensors.
- It is essential for studies where transport phenomena significantly impact binding kinetics.
- The model aids in optimizing experimental design to minimize parameter estimation errors.