Microbial Ensemble Analysis via Absolute Cell Counting
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Solution Overview
Problem
Current methods fail to effectively analyze and understand the complex interactions within microbial communities, particularly in identifying active microorganisms and their absolute cell counts, which are crucial for predicting and altering environmental properties.
Innovation Solution
A method for multivariate microorganism strain analysis that involves determining the absolute cell count of active microorganisms in samples from various environments, detecting unique markers, and forming bioensembles to alter target biological environments based on identified relationships and metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to analyze microbial communities, then the analysis process is simple, but the ability to identify active microorganisms and their absolute cell counts is insufficient
Solution Approach 1:
The method segments the complex task of microbial community analysis into distinct steps: detecting unique markers to identify microorganism types, determining absolute cell counts for each type, and analyzing interactions. This segmentation allows for precise identification of active microorganisms while managing the complexity through systematic breakdown of the analysis process.
Solution Approach 2:
The patent introduces a new dimension of measurement by determining absolute cell counts rather than just relative abundances. This dimensional change from relative to absolute measurements enables more accurate identification of active microorganisms and their functional relationships within the community.
2Loss of information
If comprehensive analysis of all microorganisms in a community is performed, then complete understanding of community interactions is achieved, but the time and computational resources required increase significantly
Solution Approach 1:
The method performs preliminary detection of unique markers and absolute cell counts before conducting interaction analysis. By establishing the absolute abundance data first, the subsequent interaction analysis can be more efficiently performed, reducing overall analysis time while maintaining completeness of interaction information.
Solution Approach 2:
The patent replaces traditional mechanical culturing and counting methods with molecular detection techniques for determining absolute cell counts. This substitution significantly reduces analysis time while preserving complete interaction data, as molecular methods can process multiple samples simultaneously without the time-consuming steps of culture-based approaches.
3Productivity
If relative abundance data is used instead of absolute cell counts, then the measurement process is faster, but the ability to predict and alter environmental properties is reduced
Solution Approach 1:
The patent changes the measurement parameter from relative abundance to absolute cell count. Although absolute counting requires additional steps compared to relative abundance measurements, the parameter change enables reliable prediction and alteration of environmental properties by providing accurate quantitative data on active microorganism populations.
Data Source
AI summary
Methods, apparatuses, and systems for screening, analyzing and selecting microorganisms from complex heterogeneous communities, predicting and identifying functional relationships and interactions thereof, synthesizing microbial ensembles based thereon, and forming and administering endomicrobial feed supplements are disclosed. Methods for identifying and determining the absolute cell count of microorganism types and strains, along with identifying the network relationships between active microorganisms and environmental parameters, are also disclosed.