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Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights. [ABSTRACT FROM AUTHOR]

Cupriavidus necator is a bacterium with a high phenotypic diversity and versatile metabolic capabilities. It has been extensively studied as a model hydrogen oxidizer, as well as a producer of polyhydroxyalkanoates (PHA), plastic-like biopolymers with a high potential to substitute petroleum-based materials. Thanks to its adaptability to diverse metabolic lifestyles and to the ability to accumulate large amounts of PHA, C. necator is employed in many biotechnological processes, with particular focus on PHA production from waste carbon sources. The large availability of genomic information has enabled a characterization of C. necator 's metabolism, leading to the establishment of metabolic models which are used to devise and optimize culture conditions and genetic engineering approaches. In this work, the characteristics of available C. necator strains and genomes are reviewed, underlining how a thorough comprehension of the genetic variability of C. necator is lacking and it could be instrumental for wider application of this microorganism. The metabolic paradigms of C. necator and how they are connected to PHA production and accumulation are described, also recapitulating the variety of carbon substrates used for PHA accumulation, highlighting the most promising strategies to increase the yield. Finally, the review describes and critically analyzes currently available genome-scale metabolic models and reduced metabolic network applications commonly employed in the optimization of PHA production. Overall, it appears that the capacity of C. necator of performing CO 2 bioconversion to PHA is still underexplored, both in biotechnological applications and in metabolic modeling. However, the accurate characterization of this organism and the efforts in using it for gas fermentation can help tackle this challenging perspective in the future. • Taxonomy of Cupriavidus necator strains is examined by comparative genomics. • The metabolism of C. necator is suited to diverse aerobic and anaerobic lifestyles. • C. necator can convert many waste products to polyhydroxyalkanoates. • Metabolic modeling of C. necator is a powerful tool for bioprocess optimization. [ABSTRACT FROM AUTHOR]