Genome-scale Metabolic Models Research Articles

Background: Drug-induced cardiotoxicity is a major cause of clinical trial failures and post-market drug withdrawals. Current screening methods rely primarily on cell assays and transcriptomic profiling, but metabolic perturbations may provide additional predictive signals for cardiotoxic liability in drugs not yet tested in humans. Hypothesis: We hypothesize that predicted metabolic flux changes derived from gene expression data would outperform transcriptomic features for predicting drug cardiotoxicity in a machine learning framework. Methods: We developed a novel computational pipeline integrating a modified iCardio genome-scale metabolic model with transcriptomic data from 5 genetically distinct hiPSC-derived cardiomyocyte cell lines treated with a variety of 31 antineoplastic and immunomodulating drugs (accessed from DToxS Center). Our mathematical framework converts gene expression changes to enzyme activity, then to relative metabolic reaction flux change (4122 reactions) using a novel constrained quadratic approach. Ensemble classifiers were trained to predict cardiotoxicity using FDA Adverse Event Reporting System Reporting Odds Ratio (ROR) as a reference, with drugs above median ROR classified as cardiotoxic. Predictive models were generated using 5-fold cross validation for hyperparameter optimization with 25% hold out for quality metric calculation (reported as mean ± SEM, P values calculated from t-test) over 100 independent iterations. Results: The metabolic flux-based classifiers demonstrated fair predictive performance of drug cardiotoxicity with an AUROC of 0.70±0.02. The flux approach produced equivalent accuracy (+0.02, P = 0.56), and significantly higher F1 (+0.10, P = 0.01), AUROC (+0.08, P = 0.01), and AUPRC (+0.07, P = 0.03) than the gene expression approach. Further analysis revealed that perturbations to fatty acid metabolism were most predictive of cardiotoxic liability, with 42 out of top 100 predictive reactions belonging to fatty acid related subsystems ( P < 1e-5 by binomial test). Conclusions: Metabolic flux prediction from transcriptomic data provides enhanced discrimination of drug cardiotoxicity compared to gene expression analysis alone. This approach enables more informative pre-clinical screening of drug candidates before human exposure, potentially reducing late-stage clinical failures and improving drug safety assessment protocols.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremor, rigidity, and bradykinesia. The WHO estimates that 10 million people currently have PD, with its prevalence expected to double to 20 million by 2050. Key risk factors include age, male sex, environmental contaminants, and family history. Emerging evidence links gut microbiota dysbiosis to PD, suggesting it contributes to neuroinflammation and disease progression, though the role of dietary interventions remains unclear. This study used computational simulations with genome-scale metabolic models (GEMs) to analyze how diet impacts the gut microbiota in PD patients. Fecal microbiota from PD patients and healthy controls were compared across three diets: high-fiber, Mediterranean, and vegan. Simulations revealed increased pro-inflammatory bacteria (e.g., Escherichia coli O157) in PD patients, likely due to reduced bacterial competition, alongside the decreased production of beneficial metabolites like butyrate, phenylalanine, and cysteine. The Mediterranean diet showed higher short-chain fatty acid production, potentially benefiting PD patients. These findings underscore the importance of dietary interventions in modulating the gut microbiome and suggest that targeted diets may complement PD therapies, improving patient outcomes.

Pan-reactome analysis of Streptomyces strains reveals association and disconnection between primary and secondary metabolism.

Guide the design of lactic acid bacteria synthesis community through computational metabolic interaction experimental pipeline.

Extremophiles, microorganisms that thrive under extreme environmental conditions, represent a powerful yet underexploited resource for advancing industrial biotechnology. Their unique metabolic and physiological adaptations allow survival in high temperature, pressure, salinity, or acidity, making them ideal candidates for processes that exceed the tolerance limits of conventional microbial hosts. Advances in genetic engineering, synthetic biology, and systems biology have enabled the tailoring of extremophiles into robust chassis organisms for the sustainable production of biofuels, bioplastics, enzymes, and other bioproducts under harsh industrial conditions. This review highlights the classification, significance, and engineering strategies of extremophiles, with a focus on genetic modification approaches such as CRISPR-Cas systems, adaptive evolution, and genome-scale metabolic modeling. Case studies demonstrate successful applications in biofuel production, biodegradable plastic synthesis, and agricultural resilience. Despite promising progress, challenges remain, including difficulties in genetic manipulation, limited understanding of extremophile biology, and the high cost of scale-up. Addressing these barriers through interdisciplinary research and technological innovation could position engineered extremophiles as central players in green chemistry, climate resilience, and the circular bioeconomy. Keywords: Extremophiles, Industrial Biotechnology, Genetic Engineering, Synthetic Biology and Biofuels and Bioproducts.

Microalgae are increasingly recognised as powerful platforms for the sustainable production of lipids and terpenoids, with expanding applications in the food, fuel and biomanufacturing industries. In this updated review, we consolidate and critically assess the most recent advances in synthetic biology and metabolic engineering of key microalgal models, including Chlamydomonas reinhardtii, Nannochloropsis spp. and Phaeodactylum tricornutum. We focus on developments that have emerged in the latest waves of research, emphasising novel genetic toolkits that accelerate the Design-Build-Test-Learn (DBTL) cycle, breakthroughs in genome-scale metabolic modelling, and innovative strategies for organelle-targeted biosynthesis of high-value compounds. Recent case studies are compared to highlight trends in successful engineering approaches. By capturing these up-to-date insights, this review outlines the current trajectory of microalgal biotechnology toward scalable, carbon-neutral biofactories for polyunsaturated fatty acids (PUFAs) and diverse terpenoids, reinforcing their role in global sustainability and the circular bioeconomy.

Human induced pluripotent cells (hiPSCs), generated in vitro, represent a groundbreaking tool for tissue regeneration and repair. Understanding the metabolic intricacies governing hiPSCs is crucial for optimizing their performance across diverse environmental conditions and improving production strategies. To this end, in this work, we introduce hiPSCGEM01, the first genome-scale, context-specific metabolic model (GEM) uniquely tailored to fibroblast-derived hiPSCs, marking a clear distinction from existing models of embryonic and cancer stem cells. hiPSCGEM01 was developed using relevant genome expression data carefully selected from the Gene Expression Omnibus (GEO), and integrated with the RECON 3D framework, a comprehensive genome-scale metabolic model of human metabolism. Redundant and unused reactions and genes were identified and removed from the model. Key reactions, including those facilitating the exchange and transport of metabolites between extracellular and intracellular environments, along with all metabolites required to simulate the growth medium, were integrated into hiPSCGEM01. Finally, blocked reactions and dead-end metabolites were identified and adequately solved. Knockout simulations combined with flux balance analysis (FBA) were employed to identify essential genes and metabolites within the metabolic network, providing a comprehensive systems-level view of fibroblast-derived hiPSC metabolism. Notably, the model uncovered the unexpected involvement of nitrate and xenobiotic metabolism—pathways not previously associated with hiPSCs—highlighting potential novel mechanisms of cellular adaptation that merit further investigation. hiPSCGEM01 establishes a robust platform for in silico analysis and the rational optimization of in vitro experiments. Future applications include the evaluation and refinement of culture media, the design of new formulations, and the prediction of hiPSC responses under varying growth conditions, ultimately advancing both experimental and clinical outcomes.

From bulk to single-cell and spatial data: An AI framework to characterise breast cancer metabolic dysregulations across modalities.

Industrial microorganisms often struggle to utilize renewable substrates such as methanol, formate, and xylose. Here, we introduce AdaptUC, a computational framework that demonstrates how the fraction of biomass precursors synthesized from unadapted carbon sources governs both the evolutionary driving force and the minimal substrate requirement. AdaptUC predicts gene knockout strategies for constructing the starting strain for adaptive laboratory evolution by selectively blocking metabolic pathways, thereby rendering specific precursor pools dependent on the unadapted substrate. We show that smaller dependency fractions correspond to higher driving forces for evolution of the starting strain. Case studies in Escherichia coli and Corynebacterium glutamicum, validated against experimental records and literature, confirm AdaptUC’s ability to identify knockout combinations that fine-tune precursor dependency and accelerate adaptation. By leveraging genome-scale metabolic models, AdaptUC navigates vast candidate pools without combinatorial explosion, reducing experimental screening and prioritizing strains with stronger evolutionary drives.

The development of anticancer peptides (ACPs) has emerged as a promising strategy in targeted cancer therapy due to their high specificity and therapeutic potential. Cathelicidin-derived antimicrobial peptides represent a particularly attractive class of ACPs, yet systematic evaluation of their anticancer activity remains limited. In this study, we conducted virtual screening of eight cathelicidin-derived peptides (AL-38, LL-37, RK-31, KS-30, KR-20, FK-16, FK-13, and KR-12) to assess their potential against colon cancer. Among these, LL-37 and FK-16 were identified as the most promising candidates, with LL-37 exhibiting the strongest inhibitory effects on both non-metastatic (HT-29) and metastatic (SW-620) colon cancer cell lines in vitro. To overcome challenges associated with peptide stability and delivery, we employed the probiotic lactic acid bacterium Limosilactobacillus fermentum KUB-D18 as both a biosynthetic platform and delivery vehicle. A genome-scale metabolic model (GEM), iTM505, was reconstructed to predict the strain’s biosynthetic capacity for ACP production. Model simulations identified trehalose, sucrose, maltose, and cellobiose as optimal carbon sources supporting both high peptide yield and biomass accumulation, which was subsequently confirmed experimentally. Notably, L. fermentum expressing LL-37 achieved a growth rate of 2.16 gDW/L, closely matching the model prediction of 1.93 gDW/L (accuracy 89.69%), while the measured LL-37 concentration (26.96 ± 0.08 µM) aligned with predictions at 90.65% accuracy. The strong concordance between in silico predictions and experimental outcomes underscore the utility of GEM-guided metabolic engineering for optimizing peptide biosynthesis. This integrative approach—combining virtual screening, genome-scale modeling, and experimental validation—provides a robust framework for accelerating ACP discovery. Moreover, our findings highlight the potential of probiotic-based systems as effective delivery platforms for anticancer peptides, offering new avenues for the rational design and production of peptide therapeutics.

Despite many reports focusing on the engineering aspects of biodesulfurization, there is a lack of comprehensive analysis on metabolic pathways and integration of engineering and metabolism. In this study, a genome-scale metabolic model was reconstructed for Thioalkalivibrio versutus D301, a potent strain in biodesulfurization. The model, named TVD301, was refined using extracted RNA sequencing data, and flux balance analysis demonstrated its accuracy in predicting growth and sulfur species rates. Importantly, experimental validation in a regulated medium confirmed a 60% decrease in sulfate production compared to control cultures, showing the strong practical relevance of the model. The TVD301 model also revealed that T. versutus lacks the enzymes needed to convert sulfide to sulfate, making it a strong strain in biodesulfurization. To optimize sulfur recovery and reduce sulfate production in industrial processes using microbial consortia, the TVD301 model was adapted to a consortium model. Sensitivity analysis highlighted the importance of DsrAB and Cys enzymes in preventing undesired sulfate production. By inhibiting these enzymes via inhibitors extracted from Brenda database, elemental sulfur production increased significantly. These findings suggest promising strategies for enhancing biodesulfurization processes in industrial settings.

Metabolic model-guided strain design for improved succinic acid production in Yarrowia lipolytica.

S-adenosylmethionine (SAM) is a high-value metabolite with widespread applications in medicine and nutrition, yet its microbial production remains constrained by high energy demands and inefficient precursor utilization. In this study, we investigated sodium citrate supplementation as a strategy to enhance SAM biosynthesis in Pichia pastoris under methanol induction. Integrating transcriptomics with a newly reconstructed genome-scale metabolic model (iLD1283), we systematically elucidated the molecular and metabolic mechanisms underlying citrate-mediated improvements. Physiological analysis revealed that sodium citrate supplementation significantly increased biomass accumulation, methanol and L-methionine assimilation, and intracellular ATP levels, resulting in a 70% enhancement in SAM titer. Transcriptomic profiling demonstrated global metabolic reprogramming, including the upregulation of glycolysis, the tricarboxylic acid cycle, oxidative phosphorylation, and amino acid biosynthesis, collectively supporting improved energy supply and precursor availability. Constraint-based simulations using iLD1283 identified an optimal citrate feeding rate that balanced energy generation and SAM production, which was validated in 5-L fed-batch fermentation, achieving a peak SAM concentration of 10.87g/L. Metabolic flux analysis further confirmed increased flux through central carbon pathways and elevated cofactor regeneration. Together, these findings provide mechanistic insight into sodium citrate-induced metabolic rewiring and establish a model-guided framework for rational optimization of energy-intensive microbial processes. This work highlights the potential of combining omics data and metabolic modeling to guide precision feeding strategies for enhanced bioproduction.

The opportunistic human fungal pathogen Candida albicans possesses a remarkable metabolic plasticity, which is essential for both fungal commensalism and virulence and influences its physiology and behavior in multiple ways. The investigation of such processes particularly benefits from the emergence of multi-omics and in silico approaches. In this study, we combined a multi-omics approach with genome-scale metabolic modeling to investigate the fungal metabolic adaptation to amino acid utilization and starvation. Most strikingly, we found an altered activity of the shikimate pathway upon amino acid starvation, accompanied by a simultaneous induction of two metabolic gene clusters required for the metabolism of hydroxybenzenes. Further analyses revealed so far unknown potential functional and regulatory links between both metabolic pathways, which provide starting points for future research leading to a better understanding of the fungal adaptation to dynamic host conditions.

Cancer cells frequently reprogramme their metabolism to support growth and survival, making metabolic pathways attractive targets for therapy. In this study, we investigated the metabolic effects of three kinase inhibitors and their synergistic combinations in the gastric cancer cell line AGS using genome-scale metabolic models and transcriptomic profiling. We applied the tasks inferred from the differential expression (TIDE) algorithm to infer pathway activity changes in the different conditions. We also explored a variant of TIDE that uses task-essential genes to infer metabolic task changes, providing a complementary perspective to the original algorithm. Our results revealed widespread down-regulation of biosynthetic pathways, particularly in amino acid and nucleotide metabolism. Combinatorial treatments induced condition-specific metabolic alterations, including strong synergistic effects in the PI3Ki–MEKi condition affecting ornithine and polyamine biosynthesis. These metabolic shifts provide insight into drug synergy mechanisms and highlight potential therapeutic vulnerabilities. To support reproducibility, we developed an open-source Python package, MTEApy, implementing both TIDE frameworks.

Genome-scale metabolic modelling in antimicrobial pharmacology: Present and future.

Methylene-tetrahydrofolate reductase (MTHFR) is an important enzyme for acetogenic carbon fixation, but the redox mechanism driving this reaction is not clearly understood. Previous enzymology work and energetic accounting on species such as Clostridium autoethanogenum has led to confounding results when placed in the context of in vivo experiments. In this work, we create multiple C. autoethanogenum strains harboring alternative MTHFR enzyme complexes as well as genome-scale metabolic models to better understand how these organisms conserve energy on gas substrates. The inclusion of a Type-III MTHFR unexpectedly allows for higher growth than expected and suggests the possibility of an additional redox balancing cycle employed during autotrophic growth.

Elucidating metabolic mechanisms underlying the influence of specific growth rate on alkaline protease synthesis in Bacillus licheniformis through combined omics and computational modeling analysis.

In- silico guided tailoring of whole cells exploring the Genome-scale metabolic model for production of bio-hydrogen using lignocellulose derived sugars: Prioritizing higher growth rate and productivity

Global climate change will result in plants being subjected to abiotic stresses with greater frequency and intensity. Such stresses necessarily impact the metabolic network in terms of both its structure and fluxes. The construction and analysis of genome-scale metabolic models (GEMs) have proved to be useful for both the prediction of the effects of climate change on metabolism and identification of targets for breeding increased resilience. In this review, we first explain how such GEMs are constructed and how fluxes can be predicted, providing a detailed account of how models can be developed to capture metabolic variations across both space and time. Although GEMs are a growing field, the number of plant GEMs is lower than that of other taxa; here we discuss the reasons behind this disparity and propose solutions. We then highlight studies that have investigated the effects of changing CO2 concentrations, drought, and high temperature on metabolism, making use of innovations in the construction of context-specific and multi-organ models. CAM and C4 are also discussed as types of photosynthesis that are typically associated with tolerance of high temperatures and low water availability. Overall, we aim to demonstrate that plant GEMs can be a useful addition to the physiologist's toolkit and can generate important insights and testable hypotheses regarding plant responses to stress.