Computational MIC Estimation from Flow Cytometry Data
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Solution Overview
Problem
Current methods for estimating the minimum inhibitory concentration of antibiotics for bacterial species using cytometric data are subjective and time-consuming, often relying on manual gating strategies and subjective determinations, which can be inconsistent and labor-intensive.
Innovation Solution
The method involves obtaining cytometric data for test and control samples, computing distance values using probability binning or statistical metrics, and fitting curves to determine the minimum inhibitory concentration objectively, allowing for automatic and reproducible estimation of antibiotic effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gating strategies and subjective determinations are used to estimate minimum inhibitory concentration, then the process allows for flexible analysis of cytometric data, but the method becomes subjective and time-consuming with inconsistent results
Solution Approach 1:
The patent replaces manual gating strategies and subjective visual analysis with automated computational algorithms. Specifically, it uses unsupervised machine learning algorithms (such as clustering algorithms) to automatically identify bacterial populations and calculate minimum inhibitory concentrations from flow cytometry data, eliminating the need for manual intervention and subjective determination while improving consistency and reducing time requirements
Solution Approach 2:
The system enables self-service by allowing the computational algorithm to autonomously perform the entire MIC estimation process without human intervention. The algorithm automatically processes the cytometric data, identifies relevant bacterial populations, calculates distance metrics, determines inhibition thresholds, and outputs MIC values independently, making the system self-sufficient and eliminating dependency on manual analysis
2Extent of automation
If manual gating strategies are used for analyzing cytometric data, then the method can handle complex data patterns, but it becomes labor-intensive and inconsistent
Solution Approach 1:
The patent replaces complex manual gating operations with automated computational algorithms that process cytometric data. The system uses machine learning algorithms to automatically identify bacterial populations, calculate distance metrics between treated and control samples, and determine minimum inhibitory concentrations without requiring manual gating expertise, thereby increasing automation while managing complexity through algorithmic standardization
3Adaptability or versatility
If subjective determinations are used to estimate minimum inhibitory concentration, then the method can adapt to different data patterns, but it reduces reliability and reproducibility
Solution Approach 1:
The patent maintains adaptability by dynamically adjusting computational parameters such as distance thresholds and inhibition criteria based on the specific antibiotic-bacterial combination being analyzed. The algorithm modifies these parameters automatically to optimize performance for different data patterns while maintaining consistent methodology, thereby achieving both adaptability to various biological systems and reliability through standardized computational procedures
Solution Approach 2:
The system replaces subjective human judgment with objective computational algorithms that consistently apply the same mathematical rules across all analyses. This substitution ensures that the same data will always produce the same results, dramatically improving reproducibility while the algorithm's ability to process various data types maintains adaptability across different antibiotic-bacterial combinations
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
This approach enables objective and automated quantification of antibiotic effects on bacterial species, reducing subjectivity and time, and providing consistent and reproducible estimates of minimum inhibitory concentrations and susceptibility or resistance.
Implementation Method 1
the light scattering and fluorescence properties of the particles are measured
Implementation Method 2
the light emitted from fluorescent molecules in one or more detectors
Data Source
AI summary
Methods for estimating a minimum inhibitory concentration of an antibiotic for a bacterial species. Methods according to certain embodiments include obtaining cytometric data (e.g., flow cytometer data) for a plurality of test samples and a control sample for the antibiotic and bacterial species, computing distance values that reflect a measure of variation between one or more pairs of samples, and assigning a minimum inhibitory concentration based on the computed distance values. Systems for practicing the subject methods are also provided. Non-transitory computer readable storage media are also described.