Microbial Network Analysis for Key Driver Identification
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Solution Overview
Problem
Current methods for identifying key driver organisms in microbial populations associated with diseases fail to quantify community-level changes and inter-microbial interactions, relying on global and local network properties that ignore mutual associations and inhibitions, leading to incomplete understanding of disease mechanisms.
Innovation Solution
A system and method that filters and processes DNA sequences to create matrices of microbial abundance profiles, generates and filters networks to retain common nodes, calculates Jaccard edge indices and coreness values, and uses the NESH score to identify key drivers by analyzing community shuffling and network rewiring, providing a comprehensive analysis of microbial community changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If differential abundance analysis is used to identify microbes associated with disease, then statistically differentially abundant microbes can be identified, but the combined effect of mutual association and inhibition within microbial communities cannot be quantified
Solution Approach 1:
The patent combines differential abundance analysis with network analysis to create a comprehensive approach that simultaneously identifies differentially abundant microbes and quantifies their inter-microbial associations and inhibitions, resolving the contradiction between identifying driver organisms and preserving interaction information
Solution Approach 2:
The patent introduces network properties (such as betweenness centrality, degree, and clustering coefficient) as intermediary metrics that bridge the gap between simple abundance data and complex community-level interactions, enabling quantification of both individual microbe effects and their collective interactions
2Quantity of substance
If global network property measures are used to compare microbial association networks, then overall network changes can be assessed, but changes endured by individual nodes cannot be scrutinized
Solution Approach 1:
The patent segments the analysis into two levels: global network property measures for overall network changes and local node-level metrics (betweenness, degree, clustering coefficient) for individual node scrutiny, allowing simultaneous assessment at both scales
3Ease of operation
If local network properties like degree and betweenness are used to compare networks, then qualitative measures of node importance can be obtained, but the constituent members and their specific interactions are ignored
Solution Approach 1:
The patent applies local quality by calculating node-specific metrics (betweenness, degree, clustering coefficient) that capture both the qualitative importance of individual nodes and their specific interactions with constituent members, providing detailed local network characteristics
4Stability of the object's composition
If traditional network property comparison is used, then networks with similar properties can be identified, but network rewiring where connections between nodes are entirely different cannot be detected
Solution Approach 1:
The patent introduces dynamic analysis by comparing networks at multiple levels (global properties, local node metrics, and specific edge comparisons) to detect both stable structural similarities and dynamic rewiring events where connections between nodes change
5Productivity
If methods relying only on genera abundance information are used, then differential abundance can be calculated, but inter-microbial interactions that influence disease state are completely ignored
Solution Approach 1:
The patent merges rapid abundance-based differential analysis with network-based interaction analysis, maintaining computational efficiency while incorporating inter-microbial interaction information to provide a comprehensive view of disease-associated microbial changes
Data Source
AI summary
The present disclosure addresses the technical problem related to the identification of microbial basis of a disease in microbiome. A system and method for identification of key driver responsible for bringing a change in a microbial population has been disclosed. A subset of common taxa between the ‘control’ and ‘case’ dataset is chosen and corresponding microbial association network is created. The method involves the characterization of the important community level changes between two association networks (‘control’ and ‘case’) that are obtained for a particular disease or condition. A taxon in the diseased state with an altered set of associations (identified by a high network shift score), while still being increasingly important (identified with a positive increase in betweenness) for the whole network, necessarily holds a key significance in the identification of key driver.