Microbial Ensemble Analysis via Protein Markers and Network Modeling
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Solution Overview
Problem
Current methods fail to accurately determine the absolute cell count of active microorganisms in microbial communities and their interactions with environmental parameters, limiting understanding and manipulation of these complex ecosystems.
Innovation Solution
A method involving the identification of unique markers and network analysis to determine the absolute cell count and activity of microorganism strains, allowing for the selection of microbial ensembles that can alter specific environmental parameters, such as those in rumen microbial communities to enhance milk production in dairy cows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional microbial community analysis methods are used, then species diversity can be observed, but absolute cell count of active microorganisms cannot be accurately determined
Solution Approach 1:
The patent segments the microbial community analysis into multiple independent measurement components: (1) DNA-based taxonomic identification to determine species composition, (2) protein marker detection to identify active microorganisms, and (3) cell counting to quantify absolute numbers. This segmentation allows each component to be optimized independently, resolving the contradiction between measuring species diversity and determining absolute cell counts of active microbes.
Solution Approach 2:
The patent introduces protein markers as an intermediary substance that bridges the gap between DNA-based identification and functional activity assessment. These markers serve as mediators that can be detected in environmental samples to indicate active microorganisms, enabling the simultaneous determination of both species identity and activity status without requiring cultivation.
2Loss of information
If metagenomics and metatranscriptomics are applied, then functional traits can be identified, but interaction relationships between microorganisms remain unclear
Solution Approach 1:
The patent transforms the complex interaction analysis problem into a network parameter optimization problem. By representing microorganisms as nodes and their interactions as edges with associated parameters (such as co-occurrence frequencies, metabolic complementarity scores, and environmental response correlations), the system can identify functional relationships through parameter-based network analysis rather than direct observation of complex biological interactions.
3Loss of information
If microbial communities are analyzed in complex heterogeneous environments, then ecological roles can be understood, but manipulation of specific ecosystem processes becomes difficult
Solution Approach 1:
The patent applies preliminary action by using in silico simulations and predictive modeling to identify optimal microbial ensemble compositions before actual ecosystem manipulation. The system simulates various microbial community configurations and their expected effects on ecosystem processes, allowing researchers to predictively design and select ensembles that will achieve desired outcomes before field application, thereby easing the complexity of ecosystem manipulation.
Data Source
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AI summary
Methods, apparatuses, and systems for screening, analyzing and selecting microorganisms from complex heterogeneous communities, predicting and identifying functional relationships and interactions thereof, and synthesizing microbial ensembles based thereon 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.