Publications by authors named "Bruno G Galuzzi"
Chronic gastrointestinal disorders such as inflammatory bowel diseases (IBDs) and irritable bowel syndrome (IBS) impose significant health burdens globally. IBDs, encompassing Crohn's disease and ulcerative colitis, are multifactorial disorders characterized by chronic inflammation of the gastrointestinal tract. On the other hand, IBS is one of the principal gastrointestinal tract functional disorders and is characterized by abdominal pain and altered bowel habits.
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- The study focuses on identifying metabolic flux differences in diseases across patient cohorts by using constraint-based models tailored from genomic data.
- Researchers compared sampling strategies for assessing false discovery rates (FDR) in these metabolic networks, particularly contrasting hit-and-run and corner-based algorithms.
- Findings reveal that the corner-based algorithm is more efficient and reduces false discoveries compared to traditional methods while highlighting the significance of the Kullback-Leibler divergence for correcting FDR in metabolic modeling.
Background: Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production.
View Article and Find Full Text PDFDetermining the redox potentials of protein cofactors and how they are influenced by their molecular neighborhoods is essential for basic research and many biotechnological applications, from biosensors and biocatalysis to bioremediation and bioelectronics. The laborious determination of redox potential with current experimental technologies pushes forward the need for computational approaches that can reliably predict it. Although current computational approaches based on quantum and molecular mechanics are accurate, their large computational costs hinder their usage.
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- Metabolism is regulated through complex mechanisms that involve both enzyme expression levels and interactions with metabolites, affecting the reaction rates in metabolic pathways.
- High-throughput data from metabolomics and transcriptomics need to be integrated to properly understand these regulatory interactions, as analyzing them separately fails to capture their interdependencies.
- The proposed INTEGRATE computational pipeline combines these data types using metabolic models, helping to distinguish how different regulatory layers affect metabolic fluxes, with practical applications in personalized cancer therapies.