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实例探究 > Using Machine Learning for Optimization of Cellular Factories To Produce Industrial Products

Using Machine Learning for Optimization of Cellular Factories To Produce Industrial Products

技术

分析与建模 - 机器学习
分析与建模 - 预测分析
功能应用 - 远程监控系统

适用行业

生命科学
药品

适用功能

产品研发
质量保证

用例

机器状态监测
预测性维护
远程资产管理

服务

数据科学服务
软件设计与工程服务
系统集成

挑战

Identify efficient tools to predict productivity in yeast cell factories and optimize protein pathways. Enabling accurate genotype-to-phenotype predictions through machine learning. Exploiting the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

关于客户

The Jensen Lab at DTU Biosustain, also known as Synthetic Biology Tools for Yeast (SBTY), develops engineering tools and high-resolution data that can dramatically increase the speed, efficiency, and rationale by which yeast-based cell factories are screened, selected, and engineered. Their particular focus is on how to engineer and evolve yeast genomes for improved performance of yeast as biocatalysts, and the development of high-throughput screening and selection tools enabled by genetically encoded biosensor devices. The Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain) at the Technical University of Denmark is focused on developing new knowledge and technologies to help facilitate the transformation from the existing oil-based chemical industry to a more sustainable bio-based society in which chemicals are produced biologically.

解决方案

The partnership between DTU Biosustain and TeselaGen, in collaboration with the Berkeley Lab, aimed to identify the efficacy of using mechanistic models and machine learning models to enable accurate genotype-to-phenotype predictions. TeselaGen’s DISCOVER module provides an interface and compute infrastructure to create, train, and execute ML algorithms using TeselaGen’s Proprietary Software for fast data loading and processing. Once the genetic constructs of the study are designed, built, and experimentally characterized, various DISCOVER Machine Learning and/or Deep Learning tools created by TeselaGen can be applied. This approach allows for the deployment of powerful machine learning models, training predictive models, generating peptide leads, and running evolutive models, ultimately leading to optimized metabolic pathway designs and improved productivity in yeast cell factories.

运营影响

The Machine Learning approach recommended diverse candidate genes and cell factory designs with significant improvements compared to the original designs.
The collaboration enabled predictive strain engineering for high-performing results, achieving a GFP synthesis rate 106% higher than the improved platform design.
Optimal metabolic pathway designs were identified, resulting in improved titer and productivity of tryptophan by 74% and 43%, respectively, beyond the best cell factory designs used for training the algorithms.

数量效益