Rapid Fermenter Sampling With Multi-Well Filtration and Positioning
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Solution Overview
Problem
Synthetic biology processes are capital intensive, painstaking, and uncertain, lacking efficient methods for data integration and normalization across diverse data formats and sources, which hinders rapid innovation and optimization.
Innovation Solution
An AI-guided synthetic biology platform that integrates and normalizes biologic data using standardized formats, Bayesian statistical models, and multi-modal data integration to generate predictive models for synthetic biology design, while addressing batch-specific systemic variations and ensuring data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional lab-driven synthetic biology methods are used, then research can be conducted with existing tools, but the process becomes capital intensive, painstaking, expensive, and uncertain
Solution Approach 1:
The platform segments the synthetic biology development process into distinct modular components: data collection from multiple sources, standardized data integration, Bayesian normalization to remove batch effects, machine learning model training, and predictive design generation. Each module handles a specific aspect of the workflow, enabling parallel processing and reducing overall development time while maintaining manageable system complexity through clear separation of concerns
Solution Approach 2:
The patent introduces an AI-driven data integration and normalization platform as an intermediary layer between raw experimental data and synthetic biology design decisions. This intermediary systematically processes multi-modal data, applies Bayesian statistical models to correct batch effects, and generates predictive models that guide strain design, thereby reducing uncertainty and capital intensity while accelerating development
2Loss of information
If data from multiple sources and formats are integrated, then comprehensive analysis is achieved, but data processing complexity and time increase
Solution Approach 1:
The platform performs preliminary standardization and normalization of multi-modal data before analysis. Data from diverse sources (omics, phenotypic, process parameters) are pre-processed using standardized schemas and Bayesian normalization models to remove batch effects in advance, enabling faster downstream analysis without losing information from any data source
Solution Approach 2:
The system transforms heterogeneous data from multiple sources into a unified parameter space through standardized data schemas and normalization transformations. By changing the representation parameters of diverse data types into a common framework, the platform achieves comprehensive data integration while reducing processing time through efficient parameter-based operations
3Measurement precision
If batch-specific systemic variations are addressed through normalization, then data accuracy improves, but processing complexity increases
Solution Approach 1:
The platform implements Bayesian normalization that uses feedback from observed batch effects to iteratively refine normalization parameters. The system monitors systematic variations across batches, adjusts normalization factors based on statistical models, and validates improvements in data accuracy, thereby achieving high measurement precision through a managed complexity feedback loop rather than simple one-step correction
Data Source
AI summary
Platforms, systems, and methods for rapid fermenter sampling. According to one aspect, there is provided A rapid sampling system for obtaining samples from a fermentation system, comprising: a sample inlet fluidly connected to the fermentation system; a pump fluidly connected to the sample inlet and configured to draw a sample from the fermentation system; a first valve fluidly connected to an outlet of the pump; a second valve fluidly connected to a liquid nitrogen chamber; a multi-well filter plate, wherein an individual well of the multi-well filter plate is configured to collect and filter the sample; a motorized base operatively connected to the multi-well filter plate configured to adjust a position of the multi-well filter plate; and a control unit including one or more processors and one or more memories operatively connected to the pump, the first valve, the second valve, and the motorized base.