Microservices Architecture for Precision Fermentation Optimization
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Solution Overview
Problem
Current methods for optimizing fermentation-produced animal proteins face challenges in balancing yield and quality, particularly in food applications, where sensory and functional properties are complex and difficult to measure, and existing technologies lack effective integration of human sensory data and machine learning for holistic optimization across multiple scales.
Innovation Solution
A microservices architecture that combines discovery, strain, manufacturing, and product services using machine learning and computer vision to design yeast strains, optimize fermentation parameters, and assess protein characteristics, incorporating DNA sequencing, RNA sequencing, and human panel data for holistic optimization of quality and quantity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional fermentation optimization methods are used, then process development is simpler, but yield and quality optimization precision is insufficient
Solution Approach 1:
The system segments the fermentation optimization process into distinct microservices: discovery service (protein identification), strain service (yeast strain design), manufacturing service (fermentation parameter optimization), and product service (quality assessment). Each microservice handles specific tasks independently, enabling precise optimization at each stage while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The platform creates a universal optimization system that handles multiple functions across the entire fermentation pipeline - from protein discovery to strain design to manufacturing optimization to product quality assessment. This multi-functional platform addresses various optimization needs (yield, quality, sensory properties, functional properties) within a single integrated system.
2Productivity
If comprehensive data integration is implemented, then holistic optimization is achieved, but data processing complexity increases
Solution Approach 1:
The platform introduces intermediary components including standardized APIs that mediate between different data sources and the optimization algorithms, and a centralized data lake that serves as an intermediary storage layer. These intermediaries simplify data integration by providing standardized interfaces and unified data access patterns, reducing the complexity of handling diverse data types from DNA sequencing, RNA sequencing, sensory panels, and manufacturing processes.
Solution Approach 2:
The system replaces manual data integration and analysis methods with automated machine learning algorithms and computational models. ML models automatically process and integrate data from multiple sources (genomic data, transcriptomic data, sensory data, manufacturing data) to generate optimization recommendations, eliminating the need for manual data synthesis and reducing integration complexity.
3Loss of time
If manual experimentation is used, then data collection is straightforward, but optimization speed and precision are limited
Solution Approach 1:
The system performs preliminary computational actions before physical experimentation - using ML models to predict optimal fermentation parameters, strain characteristics, and product properties based on training data. This preliminary computational optimization guides subsequent experimental work, reducing the number of iterations needed and accelerating development while improving precision through data-driven predictions.
Solution Approach 2:
The platform implements comprehensive feedback loops where data from manufacturing processes, product quality assessments, and sensory evaluations are fed back into the ML models to continuously improve predictions. This closed-loop system uses actual measurement data to refine optimization algorithms, progressively improving both speed and precision of optimization over time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise optimization of fermentation processes to maximize titers and quality, integrating complex sensory and functional data for improved product performance across various scales, reducing manual experimentation and enhancing data-driven decision-making.
Implementation Method 1
design a yeast strain to produce said protein of interest
Data Source
AI summary
Provided herein are methods for precision fermentation that iteratively build models, collect data broadly, and deliver information to users both directly (via API or user interface) or through third party software. Furthermore, in addition to traditional facades and coordinated operations like sagas, subsystems often require the ad-hoc ability to communicate with each other like to operate on data about the same sample or strain across multiple services.