Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

van Lent, P.H. (TU Delft Pattern Recognition and Bioinformatics)
Schmitz, Joep (DSM)
Abeel, T.E.P.M.F. (TU Delft Pattern Recognition and Bioinformatics; Broad Institute of MIT and Harvard)

2023

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

automated recommendation
combinatorial pathway optimization
DBTL cycles
machine learning
metabolic engineering

http://resolver.tudelft.nl/uuid:d6c6ffb4-38df-4b8c-8ac2-62b30150fa34

https://doi.org/10.1021/acssynbio.3c00186

2161-5063

ACS Synthetic Biology, 12 (9), 2588-2599

Institutional Repository

journal article

© 2023 P.H. van Lent, Joep Schmitz, T.E.P.M.F. Abeel