Biosensor Binding Curve Analysis Without Model-Based Fitting
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Solution Overview
Problem
Existing biosensor systems rely on specific interaction models for evaluating molecular interactions, which limits their applicability to interactions fitting these models, and methods using report points discard valuable data, leading to unreliable results for non-categorized interactions.
Innovation Solution
A method and biosensor system that evaluates analyte-ligand interactions independently of theoretical models by comparing sample binding curves to reference curves, considering all registered data points and using deviation criteria to classify interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based evaluation methods are used to derive interaction parameters from binding curves, then reliable results are obtained for interactions fitting specific models, but the method becomes limited to categorized interactions and cannot provide reliable results for non-categorized interactions
Solution Approach 1:
The patent applies universality by creating an evaluation method that works across all interaction types without requiring specific model assumptions. The system compares sample binding curves to reference binding curves obtained from actual experiments, rather than fitting to theoretical models. This universal approach allows reliable evaluation of both categorized and non-categorized interactions, making the system adaptable to diverse analyte-ligand interaction types while maintaining reliability through empirical reference data
2Ease of operation
If report points at predetermined points in the binding curve are used for evaluation, then the evaluation process is simplified, but a majority of the information in the binding curves is discarded leading to loss of interaction characterization accuracy
Solution Approach 1:
The patent applies continuity by utilizing all data points from the binding curve throughout the entire evaluation process. Instead of sampling only at predetermined report points, the system continuously compares the complete sample binding curve to reference binding curves. This continuous use of all available data maintains information integrity and characterization accuracy while the automated curve comparison keeps the process simple and efficient
3Measurement precision
If complex mathematical descriptions and interaction models are used to fit kinetic data, then interaction parameters can be derived, but the computational complexity increases and the method becomes less efficient
Solution Approach 1:
The patent applies copying by using empirically obtained reference binding curves as templates for comparison. Instead of performing complex mathematical fitting of theoretical models to sample data, the system creates a reference curve from actual experimental data and directly compares sample curves to this reference. This copying approach maintains measurement precision through empirical validation while dramatically reducing computational complexity by replacing complex fitting algorithms with straightforward curve comparison
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides reliable evaluation of interactions without requiring complex computations, utilizing all data points and reducing computational power, while maintaining accuracy and efficiency.
Implementation Method 1
A representative such biosensor system is the BIACORE® instrumentation sold by GE Healthcare, which uses surface plasmon resonance (SPR) for detecting interactions between molecules in a sample and molecular structures immobilized on a sensing surface.
Data Source
AI summary
A method and system for interaction analysis are disclosed. An example method for evaluation of an interaction between an analyte in a fluid sample and a ligand immobilized on a sensor surface of a biosensor includes providing a reference binding curve, representing a reference interaction for a predetermined acquisition cycle, by acquiring, using the biosensor, one or more binding curves for a reference-analyte ligand interaction at the predetermined acquisition conditions, acquiring, using the biosensor, a sample binding curve for the analyte ligand interaction for the predetermined acquisition cycle including at least one association phase wherein the sensor surface is put into contact with a fluid sample including analyte at a predetermined concentration, and generating a graphical user interface, including an upper threshold curve and a lower threshold curve defined with respect to the reference binding curve.