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Computational Framework for Machine-Learning-Enabled 13C Fluxomics
Chao Wu1, Jianping Yu1, Michael Guarnieri1
1Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States.
ACS Synthetic Biology
|October 27, 2021
Summary
A new machine learning framework accelerates 13C metabolic flux analysis (MFA) for synthetic biology. This method uses flux ratios and metabolite labeling to predict metabolic networks, improving speed and stability for high-throughput phenotyping.
Area of Science:
- Synthetic Biology
- Metabolic Engineering
- Computational Biology
Background:
- 13C metabolic flux analysis (MFA) is crucial for synthetic biology but is computationally intensive and sensitive to initial conditions.
- Optimization-based MFA often yields long computation times and unstable solutions.
Purpose of the Study:
- To develop a machine learning (ML) framework to enhance the speed and stability of 13C fluxomics.
- To create a reliable and high-throughput method for metabolic phenotyping.
Main Methods:
- Generated training and test datasets using metabolic network decomposition and flux sampling.
- Utilized flux ratios as training targets and metabolite labeling patterns as features.
- Implemented automated processes for flux ratio selection and feature screening based on importance and solvability.
Main Results:
- The ML framework significantly improved prediction accuracy by incorporating both amino acids and central carbon metabolites.
- Predicted flux ratios, combined with external fluxes, accurately determined global flux distributions.
- Validated the approach using simulated and experimental data, showing comparable or superior performance to canonical 13C MFA.
Conclusions:
- The developed ML framework offers a reliable and efficient alternative to traditional 13C MFA.
- This approach facilitates high-throughput metabolic phenotyping and advances the application of intelligent algorithms in synthetic biology's Test and Learn phase.
