Predicting Growth Conditions from Internal Metabolic Fluxes in an In-Silico Model of E. coli

Sridhara, Viswanadham; Meyer, Austin G.; Rai, Piyush; Barrick, Jeffrey E.; Ravikumar, Pradeep; Segrè, Daniel; Wilke, Claus O.

doi:10.1371/journal.pone.0114608

Cited by 25 publications

(18 citation statements)

References 30 publications

(31 reference statements)

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“…For instance, Sridhara and colleagues investigated whether bacterial growth conditions could be inferred from intracellular flux configurations [67]. Multinomial logistic regression was used in conjunction with least absolute shrinkage and selection operator (LASSO) regularization to relate growth conditions to simulated metabolic fluxes.…”

Section: Supervised Fluxomic Analysismentioning

confidence: 99%

Machine and deep learning meet genome-scale metabolic modeling

et al. 2019

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Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

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Section: Supervised Fluxomic Analysismentioning

confidence: 99%

Machine and deep learning meet genome-scale metabolic modeling

et al. 2019

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“…This fluxomic data can be trained directly through ML methods to obtain more biological insight into the desired system (Figure 5A). [ 129,130 ] (b) ML is an effective tool for merging and analyzing heterogeneous omics datasets beyond ML applications to single omics. [ 131 ] By combining these multi‐omics datasets with GEMs, context‐specific models are generated.…”

Section: Synergisms Of Cbm and MLmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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“…For this reason, the prediction conducted using a simple linear regression. The results showed that using the intracellular flux values, carbon and nitrogen sources utilized in the initial culture medium could be predicted even with a small number of impurities [114]. In a recent study, Oyetunde et al extracted over 1,200 curated bioprocess datasets from 100 articles to predict microbial factories' performance (yield, titer, and rate).…”

Section: Instances Of Cbm Coupled With ML For Fermentation Analysis and Optimizationmentioning

confidence: 99%

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Isp

Lewis

et al. 2021

Preprint

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Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable models are produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

show abstract

Predicting Growth Conditions from Internal Metabolic Fluxes in an In-Silico Model of E. coli

Cited by 25 publications

References 30 publications

Machine and deep learning meet genome-scale metabolic modeling

Machine and deep learning meet genome-scale metabolic modeling

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

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