AI-Guided Synthetic Biology Optimization for Batch-Normalized Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Synthetic biology processes are currently lab-driven, capital intensive, and uncertain, with high costs and inefficiencies due to the lack of integration of diverse data formats and sources, leading to systemic variations and batch effects.
Innovation Solution
An AI-guided synthetic biology platform that integrates and normalizes biologic data from multiple sources, applies machine learning models to generate predictive designs, and ensures data quality through normalization and quality control processes, using techniques like Bayesian statistical modeling and multi-modal data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If diverse data formats and sources are integrated, then data comprehensiveness is improved, but data standardization and quality control become more difficult
Solution Approach 1:
The patent introduces an intermediary layer (data normalization and standardization module) that mediates between diverse data sources and the predictive modeling system. This intermediary converts various data formats into a unified structure, enabling comprehensive data integration without increasing overall system complexity.
Solution Approach 2:
The system applies parameter changes by transforming data from different formats and sources into a standardized parameter structure. This allows the system to maintain data comprehensiveness while working with uniform parameters that are compatible with predictive models.
2Manufacturing precision
If batch-specific systemic variation is minimized through normalization, then predictive model accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing data normalization and batch effect correction before predictive modeling. This preprocessing step eliminates the need for repeated corrections during model training and validation, ultimately reducing total processing time while improving model accuracy.
Solution Approach 2:
The system replaces manual or iterative normalization processes with automated computational algorithms that efficiently correct batch effects. This substitution reduces processing time while maintaining or improving the precision of predictive models.
3Productivity
If AI and machine learning methods are applied to biologic data, then innovation speed and efficiency are improved, but computational costs and infrastructure requirements increase
Solution Approach 1:
The patent segments the computational workflow into distinct modules: data preprocessing, normalization, feature extraction, and predictive modeling. This segmentation allows each component to be optimized independently and enables distributed computing, reducing the computational burden on any single system while maintaining high innovation speed.
4Reliability
If multi-modal data integration is implemented, then predictive model robustness is improved, but data processing complexity and quality control requirements increase
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data modalities (genomic, proteomic, metabolomic, phenotypic) through a single standardized interface. This multi-functional approach improves model robustness by integrating diverse data types while avoiding the need for separate processing pipelines for each modality.
Data Source
AI summary
Platforms, systems, and methods for multi-objective optimization and comparative analysis for synthetic biology development. According to one aspect, there is provided an AI-guided analytic platform for development of biologic synthesis processes, comprising: a multi-objective optimization system for performing multi-objective optimizations of the biologic synthesis processes; at least one multi-objective evaluation artificial intelligence model configured to evaluate a biologic product according to each of at least two objectives; and at least one variant evaluation module configured to: generate a set of variants of a biologic parent of the biologic product, and evaluate each variant of the set of variants of the biologic parent using the at least one multi-objective evaluation artificial intelligence model.