System Reconstruction Integrates Multi-Omics Data for Disease Pathway Modeling
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Solution Overview
Problem
Current bioinformatics technologies lack a systematic approach to integrate and understand the complexity of human biology, particularly in correlating genotype with phenotype, due to the examination of genomic data out of context, which hinders the understanding of disease mechanisms and drug development.
Innovation Solution
The development of System Reconstruction technology, which integrates organism- and tissue-specific biochemical pathways, genome sequences, conditional gene expression, and clinical manifestations to construct a network of interconnected functional pathways, allowing for the comparison of normal and diseased states and identification of potential drug targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If genomic data are examined individually without integration, then data processing is simpler, but the understanding of disease mechanisms and biological complexity is insufficient
Solution Approach 1:
The patent merges multiple types of genomic data (DNA sequences, gene expression data, proteomics, metabolomics, and cellomic data) into an integrated system model. This combination allows the preservation of biological context while systematically processing the data through a unified computational framework that correlates genotype with phenotype.
Solution Approach 2:
The patent introduces a computational biology framework as an intermediary layer between raw genomic data and biological interpretation. This mediator integrates diverse data types and provides systematic analysis, enabling the retention of contextual information while managing processing complexity through structured computational methods.
2Loss of information
If multiple types of genomic data are integrated, then the understanding of biological complexity improves, but the computational requirements and system complexity increase
Solution Approach 1:
The patent segments the integration process into distinct computational modules that handle different data types (DNA sequencing, gene expression, proteomics, metabolomics, cellomics). Each module processes specific data independently before integration, reducing overall system complexity while preserving comprehensive biological context.
Solution Approach 2:
The patent adds a computational biology dimension to traditional genomic analysis by introducing multi-omics integration. This transforms the analysis from single-dimensional genome sequencing to multi-dimensional data integration, enabling comprehensive biological understanding while managing complexity through structured computational layers.
3Loss of time
If genomic data are analyzed without contextual integration, then analysis time is reduced, but the correlation between genotype and phenotype cannot be achieved
Solution Approach 1:
The patent performs preliminary organization and standardization of multiple genomic data types before integration. By pre-processing and structuring DNA sequences, gene expression data, proteomics, metabolomics, and cellomics data in advance, the system enables faster subsequent analysis while maintaining the capacity to achieve phenotype-genotype correlations.
Data Source
AI summary
The process of System Reconstruction is used to integrate sequence data, clinical data, experimental data, and literature into functional models of disease pathways. System Reconstruction models serve as informational “skeletons” for integrating various types of “high throughput” data. The present invention provides the first metabolic reconstruction study of a eukaryotic organism based solely on expressed sequence tag (EST) data.