Scale-Down Fermentation Models for Reliable Microorganism Screening
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Solution Overview
Problem
Current methods for high-throughput genomic engineering of microorganisms face challenges in predicting performance at larger scales from smaller-scale experiments, particularly in optimizing metabolic production and scaling up bioprocesses efficiently, due to the complexity of living cell systems and the need for reliable screening conditions.
Innovation Solution
A structured and analytical method is employed to scale down and scale up bioprocesses using key performance indicators (KPIs), involving multi-objective optimization and response surface methodology to analyze fermentation processes, enabling the development of predictive models for microorganism performance across different scales, from 96-well plates to commercial scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If small-scale high-throughput screening is conducted to quickly identify best candidates, then productivity increases, but reliability of performance prediction at larger scale deteriorates
Solution Approach 1:
The patent applies parameter changes by systematically varying physical and chemical parameters in scale-down models to match the heterogeneous conditions found in large-scale bioreactors. By adjusting parameters such as oxygen transfer rates, mixing speeds, and nutrient concentrations across different scale-down model configurations, the method creates screening conditions that reliably predict large-scale performance while maintaining high-throughput plate format productivity.
Solution Approach 2:
The patent introduces scale-down models as intermediary systems that bridge the gap between small-scale plate screening and large-scale bioreactor performance. These intermediary models replicate key hydrodynamic and mass transfer characteristics of industrial bioreactors, serving as a predictive mediator that enables high-throughput screening while maintaining reliability of performance prediction through controlled parameter variation.
2Productivity
If many thousands of microorganisms are screened for desired properties, then productivity increases, but device complexity increases due to need for multiple screening conditions
Solution Approach 1:
The patent applies segmentation by dividing the complex screening process into modular scale-down model configurations, each representing specific hydrodynamic or mass transfer conditions found in large-scale bioreactors. By segmenting the screening into standardized plate-based modules with controlled parameter variations, the method enables high-throughput screening of thousands of strains while managing complexity through systematic organization of screening conditions.
Solution Approach 2:
The patent achieves universality by developing a platform of scale-down models that can be applied across different microorganism types and bioprocess applications. The standardized plate-based approach with systematically varied parameters creates a universal screening framework that handles diverse strains and conditions through a single integrated methodology, reducing overall system complexity while maintaining high productivity.
3Loss of time
If scale-down models are used to predict large-scale performance, then time for screening is reduced, but measurement precision may deteriorate due to scale differences
Solution Approach 1:
The patent compensates for scale differences through systematic parameter changes in the scale-down models. By carefully adjusting physical and chemical parameters such as oxygen transfer coefficients, mixing intensities, and nutrient feed rates to match large-scale bioreactor conditions, the method maintains measurement precision despite the reduced scale, enabling accurate performance prediction in a time-efficient manner.
Data Source
AI summary
Systems, methods and computer-readable media are provided for designing experiments for organisms at a first scale to generate first-scale performance data used in predicting performance of the organisms at a second, larger scale. The design includes determining first-scale screening conditions based at least in part upon the contribution of second-scale conditions to performance parameters of an organism at the second scale. The first-scale screening conditions include one or more proxies for second-scale conditions that cannot be replicated at first scale. The design determines first-scale screening parameters based at least in part upon computer modeling of the metabolism of the organism at the second scale.