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.

Clostridium acetobutylicum is an anaerobic bacterium that is extensively studied for its ability to produce butanol. Over the past two decades, various genetic and metabolic engineering approaches have been used to investigate the physiology and regulation system of the biphasic metabolic pathway in this organism. However, there has been a relatively limited amount of research focused on the fermentation dynamics of C. acetobutylicum. In this study, we developed a pH-based phenomenological model to predict the fermentative production of butanol from glucose using C. acetobutylicum in a batch system. The model describes the relationship between the dynamics of growth and the production of desired metabolites and the extracellular pH of the media. Our model was found to be successful in predicting the fermentation dynamics of C. acetobutylicum, and the simulations were validated using experimental fermentation data. Furthermore, the proposed model has the potential to be extended to represent the dynamics of butanol production in other fermentation systems, such as fed-batch or continuous fermentation using single and multi-sugars.

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health ^1–5 . However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research ^6–8 . Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

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.