ArticlePloS one2019
Machine learning framework for assessment of microbial factory performance.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
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- Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.Machine learning · 2025Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning.Metabolic engineering communications · 2023Article
- MCF2Chem: A manually curated knowledge base of biosynthetic compound production.Biotechnology for biofuels and bioproducts · 2023Article
- Optimizing Ethanol Production inACS synthetic biology · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology.Bioengineering (Basel, Switzerland) · 2023Review
- Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities.Quantitative biology (Beijing, China) · 2023Review
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- Optimization and Scale-Up of Fermentation Processes Driven by Models.Bioengineering (Basel, Switzerland) · 2022Review
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.Frontiers in microbiology · 2022Review
- Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.Frontiers in artificial intelligence · 2022Article
- Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022Review
- Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.Metabolic engineering communications · 2020Review
- Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale.Nature communications · 2020Article
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).
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