T Cell Motif Pattern Analysis for Immune Repertoire Diagnosis
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Solution Overview
Problem
Current methods fail to effectively characterize and utilize patterns of T cell exposed motifs in diagnosing and managing disease conditions, as well as designing immunomodulatory interventions, due to limitations in analyzing diverse antigenic stimuli and immune responses.
Innovation Solution
The development of methods to identify and analyze patterns of T cell exposed motifs in proteomes and cellular repertoires, using comparative frequency analysis and graphical representations, to generate outputs for diagnosing health and disease states and designing immunomodulatory interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to analyze antigenic stimuli and immune responses, then the analysis process is simple, but the diagnostic accuracy and ability to characterize immune repertoire patterns is insufficient
Solution Approach 1:
The patent segments the immune repertoire analysis into distinct components: antigenic stimulus characterization, immune response pattern identification, and comparative frequency analysis. This segmentation allows for systematic processing of complex immune data while maintaining diagnostic accuracy through structured methodology.
Solution Approach 2:
The patent introduces graphical representations and pattern visualization as additional dimensions for analyzing immune repertoire data. By transforming molecular sequence data into visual patterns and frequency distributions, the system enhances diagnostic capability without requiring proportional increases in analytical complexity.
2Reliability
If comprehensive proteome analysis is performed to identify T cell exposed motifs, then the diagnostic utility is improved, but the computational and analytical burden increases
Solution Approach 1:
The patent employs preliminary action by pre-processing proteome data to identify and catalog T cell exposed motifs before clinical analysis. Reference databases of motif frequencies are prepared in advance, allowing rapid comparison against patient samples and reducing real-time analysis time while maintaining comprehensive diagnostic coverage.
Solution Approach 2:
The patent uses copying by creating simplified representations of complex immune repertoire data through graphical patterns and frequency distributions. These copied representations capture essential diagnostic information in a more efficient format that reduces computational burden while preserving diagnostic reliability.
3Adaptability or versatility
If detailed characterization of immune repertoire patterns is achieved, then the ability to design immunomodulatory interventions is enhanced, but the complexity of data processing increases
Solution Approach 1:
The patent introduces pattern recognition algorithms and frequency analysis methods as intermediaries between raw immune repertoire data and intervention design decisions. These intermediary processing steps transform complex molecular data into actionable pattern information that guides immunomodulatory intervention strategies without requiring direct complex data processing at each decision point.
Data Source
AI summary
The present invention provides methods and systems for identifying and classifying patterns comprising the T cell exposed motifs and the frequencies of such motifs in collections of proteins that make up the human proteome, immunoglobulinome, T cell receptor repertoire or microbiome, and other proteomes of environmental of microbial origin, or subsets thereof. It further provides graphical representations that facilitate comparisons of T cell exposed motif patterns between samples or between time points. The present invention also provides methods and systems for identifying and classifying patterns in repertoires of cells including receptor bearing cells and cells of tissue samples and detecting patterns of utility in diagnosis and monitoring of health and disease.