Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

Aida, Honoka; Uchida, Keisuke; Nagai, Motoki; Hashizume, Takamasa; Masuo, Shunsuke; Takaya, Naoki; Ying, Bei‐Wen

doi:10.1016/j.csbj.2023.04.020

Cited by 8 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML-assisted medium optimization often resulted in novel insights that were outside of current knowledge. Besides the present findings of the medium contributions to differentiated bacterial growth, our previous studies observed the secondary contribution of glucose to bacterial growth [65], the differentiated contribution of carbon, sulfate, and nitrogen for survival [32], and the diversified metabolic strategies in transcriptome reorganization for increased productivity [31]. Additionally, the cluster analysis intriguingly divided the medium components into four clusters, which were outside of any well-known chemical or biological categories.…”

Section: Discussionsupporting

confidence: 69%

“…The present study first demonstrated ML-assisted medium specialization for differentiated bacterial growth. ML was remarkably significant in medium optimization for microbial and mammalian cells [26,31], which could be widely applied to synthetic construction and production [48,49]. The present study provides alternative applications in medium development for selective cultures.…”

Section: Discussionmentioning

confidence: 76%

“…These four parameters were used as the machine learning (ML) objective variables, either in a single mode or a multiple combination (Figure 2A). ML-assisted medium optimization and specialization for different strains were performed using the gradient-boosting decision tree (GBDT), which has been repeatedly validated to have superior predictive performance and interpretability compared to other algorithms [31][32][33]. The initial training data (R0) were applied to the GBDT model to improve r_Lp or K_Lp (R1 and R2) (Figure 2A).…”

Section: Experimental and Computational Design Of Active Learning For...mentioning

confidence: 99%

“…That is, S1-1 and S1-2 considered the pairs of r or K (i.e., r_Lp vs. r_Ec, K_Lp vs. K_Ec) to maximize the difference of r or K between Lp and Ec, and S2-1, S2-2, and S3 considered all parameters to maximize the difference of both r and K between Lp and Ec (Figure 2C). ML-assisted medium optimization and specialization for different strains were performed using the gradient-boosting decision tree (GBDT), which has been repeatedly validated to have superior predictive performance and interpretability compared to other algorithms [31][32][33]. The initial training data (R0) were applied to the GBDT model to improve r_Lp or K_Lp (R1 and R2) (Figure 2A).…”

Section: Experimental and Computational Design Of Active Learning For...mentioning

confidence: 99%

See 3 more Smart Citations

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Zhang,

Aida,

Ying

2023

Applied Microbiology

Self Cite

View full text Add to dashboard Cite

Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.

show abstract

Section: Discussionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 76%

Section: Experimental and Computational Design Of Active Learning For...mentioning

confidence: 99%

Section: Experimental and Computational Design Of Active Learning For...mentioning

confidence: 99%

See 2 more Smart Citations

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Zhang,

Aida,

Ying

2023

Applied Microbiology

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first step is the learning process, i.e., data mining, which constructs the machine learning model and searches for the medium components deciding the growth fitness. The decision tree algorithms have been successfully used to find the decision-making components in the complicated culture medium for bacterial growth and substrate production [ 96 , 97 , 98 ]. The second step is to predict the medium composition leading to improved growth fitness according to the model constructed in the first step.…”

Section: Machine Learning-based Minimal Genome Methodsmentioning

confidence: 99%

Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Aida,

Ying

2023

Biology

Self Cite

View full text Add to dashboard Cite

Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.

show abstract