Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model ( i MK1961) for Cylindrotheca closterium , an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. i MK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With i MK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium ’s metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium’ s metabolic flexibility and its potential role in global carbon cycling.

The denitrifying bacterium Thauera sp. MZ1T, a common member of microbial communities in wastewater treatment facilities, can produce different compounds from a range of carbon (C) and nitrogen (N) sources under aerobic and anaerobic conditions. In these different conditions, Thauera modifies its metabolism to produce different compounds that influence the microbial community. In particular, Thauera sp. MZ1T produces different exopolysaccharides with floc-forming properties, impacting the physical disposition of wastewater consortia and the efficiency of nutrient assimilation by the microbial community. Under N-limiting conditions, Thauera sp. MZ1T decreases its growth rate and accelerates the accumulation of polyhydroxyalkanoate-related (PHA) compounds including polyhydroxybutyrate (PHB), which plays a fundamental role as C and energy storage in this β-proteobacterium. However, the metabolic mechanisms employed by Thauera sp. MZ1T to assimilate and catabolize many of the different C and N sources under aerobic and anaerobic conditions remain unknown. Systems biology approaches such as genome-scale metabolic modeling have been successfully used to unveil complex metabolic mechanisms for various microorganisms. Here, we developed a comprehensive metabolic model (M-model) for Thauera sp. MZ1T (iThauera861), consisting of 1,744 metabolites, 2,384 reactions, and 861 genes. We validated the model experimentally using over 70 different C and N sources under both aerobic and anaerobic conditions. iThauera861 achieved a prediction accuracy of 95% for growth on various C and N sources and close to 85% for assimilation of aromatic compounds under denitrifying conditions. The M-model was subsequently deployed to determine the effects of substrates, oxygen presence, and the C:N ratio on the production of PHB and exopolysaccharides (EPS), showing the highest polymer yields are achieved with nucleotides and amino acids under aerobic conditions. This comprehensive M-model will help reveal the metabolic processes by which this ubiquitous species influences communities in wastewater treatment systems and natural environments.

Understanding plant response to environmental factors such as temperature, drought, diseases, and carbon-to-nitrogen (C:N) ratio is essential for crop resilience, quality, and adaptation to climate change. Here, we present i Citrus2616, a high-resolution organ-specific genome-scale metabolic model for Citrus clementina , comprising 2,616 genes, 8,653 metabolites, and 10,654 reactions. The model integrates organ-specific metabolomics data, i.e., leaf, stem, and root, and predicts plant responses to different conditions with high accuracy. Lower C:N ratios showed higher growth rates compared to higher C:N ratios, suggesting an inverse relationship between growth and C:N ratios. Simulations show that polymers such as starch and hemicellulose increased 4-fold under mixotrophic compared to phototrophic conditions, contributing to enhanced rigidity of cell walls, thus improving mechanical and drought stress. Furthermore, i Citrus2616 revealed higher production of specialized metabolites such as flavonoids in the presence of specific nutrients. Additionally, transcriptomics data from symptomatic and asymptomatic leaf and root tissues across four seasons (winter, spring, fall, and summer) during Huanglongbing infection (citrus greening) were integrated into the model. This integration revealed tissue-specific metabolic adaptations, including shifts in energy allocation, secondary metabolite production, and stress-response pathways under biotic stress. These findings underscore the utility of i Citrus2616 in elucidating the metabolic underpinnings of biotic and abiotic stress resilience and could aid in improving crop productivity and quality, thereby meeting escalating market demands.