Omics Spectral Database Normalization for Synthetic Biology Modeling

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Synthetic biology research is currently lab-driven, capital-intensive, and uncertain, with high costs and inefficiencies, limiting innovation and accessibility.

Innovation Solution

An AI-guided synthetic biology platform that integrates and normalizes diverse biologic data, applies machine learning models, and performs data quality assurance to generate predictive models for biologic system design, addressing batch-specific systemic variations and technical factors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If lab-driven synthetic biology methods are used, then research can be conducted with current technology, but the process becomes capital intensive, expensive, and uncertain

Engineering Contradiction:

Improvedevelopment certaintyVSAvoidplatform complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces an AI-guided analytic platform as an intermediary system that mediates between raw biologic data and predictive models. This platform integrates multiple databases, applies normalization processes, and uses machine learning algorithms to reduce uncertainty in synthetic biology development while maintaining manageable complexity through modular architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional lab-driven mechanical and manual processes with AI-based computational systems. Machine learning models and automated data processing algorithms substitute for manual experimental design and analysis, reducing capital intensity and improving development certainty through data-driven predictions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If diverse biologic data from multiple databases is integrated, then data comprehensiveness improves, but data format and semantic inconsistencies increase

Engineering Contradiction:

Improvedata integration capabilityVSAvoiddata processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies data normalization processes that transform diverse biologic data into standardized formats. By changing data parameters through normalization techniques, the system maintains adaptability to integrate multiple data sources while reducing processing complexity through consistent data structures

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent segments the data integration process into distinct modules: data collection from multiple databases, format conversion, normalization processing, and model application. This segmentation allows comprehensive data integration while managing complexity through organized, stepwise processing

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If batch-specific systemic variation is addressed through normalization, then measurement accuracy improves, but processing time increases

Engineering Contradiction:

Improvestrain performance measurement accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies normalization processes as preliminary actions before machine learning model training. By pre-processing data to remove batch-specific systematic variations in advance, the system improves measurement accuracy while reducing the time required for subsequent model training and analysis

Inventive Principle:

Principle #10Preliminary action

4Productivity

If AI models and machine learning methods are applied, then innovation speed increases, but computational resource requirements increase

Engineering Contradiction:

Improveinnovation rateVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent applies machine learning methods selectively to normalized biologic data rather than processing all raw data. By applying AI models only to the essential normalized dataset, the system maintains high innovation speed while reducing unnecessary computational energy consumption

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260029387A1Platforms, systems, and methods for automated omics for generalization using spectral databases in synthetic biology development

Publication Date: 2026.01.29 X DEVELOPMENT LLC

AI summary

Platforms, systems, and methods for automated omics for generalization using spectral databases in synthetic biology development. According to one aspect, there is provided a system for converting raw data from an analytical and mass spectrometry instrument to model-ready data, comprising: computing hardware configured to: receive data from the analytical and mass spectrometry instrument, wherein the data includes measurement data from a set of control samples and a set of test samples; extract a set of peak lists comprising a set of test peak lists and a set of control peak lists from the received data; compress the extracted peak lists using a compression algorithm; identify a set of metabolites that correspond to a set of peaks from the compressed peak lists by comparing a set of mass-to-charge ratios and a set of retention times associated with the set of peaks with the mass-to-charge ratios.