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Related Experiment Video

Updated: Apr 30, 2026

Microfluidic On-chip Capture-cycloaddition Reaction to Reversibly Immobilize Small Molecules or Multi-component Structures for Biosensor Applications

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Published on: September 23, 2013

Equilibrium and dynamic design principles for binding molecules engineered for reagentless biosensors.

Seymour de Picciotto¹, Barbara Imperiali², Linda G Griffith³

¹Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Analytical Biochemistry

|May 13, 2014

Summary

Optimizing reagentless biosensors involves matching binding affinity to input signals. Maximizing association and dissociation rates, within practical limits, enhances biosensor performance and sets new quality standards.

Keywords:

Antibody immunoassay Detection Dynamic analysis Reagentless biosensor

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Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Bio-layer Interferometry for Measuring Kinetics of Protein-protein Interactions and Allosteric Ligand Effects

Published on: February 18, 2014

Area of Science:

Biomedical Engineering
Analytical Chemistry
Biophysics

Background:

Reagentless biosensors utilize binding partner-target interactions to generate fluorescent signals.
Binding affinity significantly impacts biosensor equilibrium and dynamic responses.
Existing models often overlook dynamic performance optimization.

Purpose of the Study:

To develop a kinetic model for reagentless biosensor dynamic performance.
To identify optimal binding moiety characteristics for improved biosensor response.
To provide design principles for enhanced biosensor development.

Main Methods:

Developed a kinetic model for reagentless biosensor dynamics.
Simulated biosensor response to sinusoidal ligand concentration signals.
Analyzed the influence of binding affinity, association, and dissociation rate constants.

Main Results:

Optimal equilibrium dissociation constant (KD) should match the average predicted input signal.
Maximizing both association rate constant (kon) and dissociation rate constant (koff) is crucial.
The ratio of kon to koff must be tuned to achieve the desired KD.

Conclusions:

Kinetic modeling provides guidance for reagentless biosensor design.
Matching KD to input signal characteristics and optimizing rate constants enhances performance.
Derived principles offer metrics for biosensor quality standards and future development.