Automated Host Phage Response Data Analysis
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Solution Overview
Problem
Current methods for analyzing host-phage response data are subjective and prone to variability, relying on manual interpretation of growth curves which can be influenced by biological and experimental factors, leading to inconsistent results and reduced precision.
Innovation Solution
A computer-implemented method that analyzes host-phage response data by fitting candidate functions such as Gompertz, Logistic, and Richards functions to estimate lag time and goodness of fit, with normalization and quality assurance steps to classify datasets and predict phage efficacy, using a random forest classifier for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual visual inspection methods are used to interpret growth curves, then flexibility and adaptability in analysis are maintained, but measurement precision and reliability deteriorate due to subjectivity and human variability
Solution Approach 1:
The patent replaces the manual mechanical process of visual inspection with an automated computer-based image analysis system. The system uses software algorithms to objectively analyze growth curves, eliminating human subjectivity while maintaining the ability to handle diverse experimental conditions through programmable analysis parameters.
Solution Approach 2:
The analysis system performs self-correction by automatically identifying and compensating for common errors in growth curve interpretation. The software independently processes multiple curves using consistent criteria, eliminating the need for human intervention and ensuring uniform application of analysis standards across all datasets.
2Productivity
If automated high throughput systems are implemented, then productivity and speed of analysis improve, but device complexity and initial resource requirements worsen
Solution Approach 1:
The patent implements a universal analysis platform that can process multiple types of growth curve data from different experimental conditions using the same software system. The platform handles various plate formats, time points, and bacterial strains through configurable parameters, eliminating the need for separate specialized systems for each experimental variation.
3Loss of information
If subjective human interpretation is used, then contextual understanding and biological insight are maintained, but reliability and consistency of results worsen due to variability in user skill and attentiveness
Solution Approach 1:
The system incorporates feedback mechanisms where the software continuously refines its analysis based on the characteristics of the growth curves being analyzed. The system adjusts its interpretation algorithms based on patterns recognized across multiple datasets, improving reliability while maintaining biological relevance through iterative optimization.
Data Source
AI summary
A computer implemented method for analyzing host phage response data comprises fitting a sequence of sigmoidal functions at each time point from a start time to an end time and selecting the best fit at each point. The best fit over all the time points is then selected, and a search performed for a time point with a similar coefficient of determination, but closer in time to a minimum time point indicative of the end of the lag phase. The lag time for the best fit time point is then obtained from the fitted model. If the best fit fails a threshold test then the dataset is considered to be flat and the lag time is set to the end time.