Microbiota Mix Prediction Using Interaction Model Correction
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Solution Overview
Problem
Current methods for mixing complex microbiota samples for transplantation or treatment are inefficient, often relying on empirical approaches that fail to ensure the diversity and viability of microorganisms, leading to inaccurate predictions of mix compositions and treatment efficacy.
Innovation Solution
A computer-aided method using a linear approach to predict intermediary mix profiles, followed by correction with an interaction model learned from reference profiles, to accurately control the mixing of microbiota samples, ensuring precise composition and treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If random mixing of microbiota samples is performed to increase diversity, then the diversity of microorganisms in the sample is improved, but the time required to obtain accurate mix profiles increases significantly due to sequencing analysis time
Solution Approach 1:
The patent performs preliminary sequencing and profiling of individual donor samples before mixing. This allows the system to predict the composition of mixed samples computationally rather than performing actual sequencing on every mixed sample, dramatically reducing the time required while maintaining accuracy in assessing microbial diversity.
Solution Approach 2:
The patent creates virtual copies or simulated profiles of mixed samples based on mathematical models that predict mix composition from individual sample profiles. These predicted profiles serve as substitutes for actual sequencing of mixed samples, reducing time and resource consumption while providing sufficient information for treatment selection.
2Productivity
If linear prediction is used to estimate mix composition from individual profiles, then the prediction process is simple and fast, but the accuracy of the predicted profiles deteriorates due to shifts and drifts between predicted and true profiles
Solution Approach 1:
The patent implements a feedback mechanism where the system compares predicted mix profiles with actual measured profiles from sequenced mixed samples. This feedback is used to iteratively refine and recalibrate the prediction model, improving accuracy over time while maintaining the computational efficiency of the prediction approach.
Solution Approach 2:
The patent transforms the prediction approach by changing the parameters and structure of the prediction model from simple linear combinations to more sophisticated mathematical models that account for microbial interactions, competition, and environmental adaptation. This allows accurate prediction of mix profiles while maintaining computational feasibility.
3Ease of manufacture
If empirical mixing methods are used without predictive modeling, then the process is simple to implement, but the ability to control and guarantee treatment efficacy is reduced
Solution Approach 1:
The patent performs preliminary computational modeling and prediction of mix profiles before actual mixing occurs. This allows the system to identify optimal combinations of donor samples that are predicted to achieve desired therapeutic outcomes, ensuring treatment efficacy is optimized before the mixing process begins.
Solution Approach 2:
The patent replaces empirical trial-and-error mixing approaches with a computational modeling system that uses mathematical algorithms to predict mix composition and efficacy. This substitution of computational methods for empirical methods maintains ease of implementation while significantly improving the reliability and predictability of treatment outcomes.
Data Source
AI summary
Prediction of a mix of complex communities of microorganisms includes a linear prediction, e.g. matrix-based, that is corrected using an interaction model, e.g. a matrix, learnt from reference true mix profiles and corresponding reference linear-predicted profiles. Reverse prediction makes it possible to determine a mix of samples to be made given a target mix profile.