Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.
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http://dx.doi.org/10.1038/s41540-023-00280-x | DOI Listing |
Background Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR. Methods Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
View Article and Find Full Text PDFCell Death Differ
January 2025
Dana Farber Cancer Institute, Boston, MA, USA.
Cellular senescence contributes to a variety of pathologies associated with aging and is implicated as a cellular state in which cancer cells can survive treatment. Reported senolytic drug treatments act through varying molecular mechanisms, but heterogeneous efficacy across the diverse contexts of cellular senescence indicates a need for predictive biomarkers of senolytic activity. Using multi-parametric analyses of commonly reported molecular features of the senescent phenotype, we assayed a variety of models, including malignant and nonmalignant cells, using several triggers of senescence induction and found little univariate predictive power of these traditional senescence markers to identify senolytic drug sensitivity.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands.
Background: Systemic diseases are often associated with endothelial cell (EC) dysfunction. A key function of ECs is to maintain the barrier between the blood and the interstitial space. The integrity of the endothelial cell barrier is maintained by VE-Cadherin homophilic interactions between adjacent cells.
View Article and Find Full Text PDFBioinform Adv
November 2024
Aix-Marseille University, CNRS, IBDM UMR7288, Turing Center for Living Systems (CENTURI), Marseille 13009, France.
Motivation: Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.
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