Predicting bacterial growth conditions from mRNA and protein abundances

Abstract

Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches

“…These findings could indicate that for these reactions, the fluxes are controlled at the gene expression level [53] . However, other works found that a combination of both omics yields slightly better results [54] , while others point to higher predictive power from proteomics data [55] , [56] . In essence, it seems that the two omics contain relevant information for metabolic modeling and it would probably be best to use a combination of both in order to maximize the amount of available information.…”

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

“…Next, by evaluating the accuracy, the model's hyperparameters can be tuned to improve its generalization performance for unseen data. [ 101,102 ] The model can be retrained after finding the best hyperparameters. Figure 4 shows how evaluation methods can help to improve the model's performance.…”

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

“…When k-fold cross-validation is operated, one can see how sensitive the model is to the training dataset. Next, by evaluating the accuracy, the model's parameters can be tuned to improve its generalization performance for unseen data [89,90]. The final evaluation of the model is conducted with test data, and the predictive ability of the model is quantified by different evaluation metrics such as accuracy, sensitivity, specificity, precision, and recall [91,92].…”