Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

<p>Abstract</p> <p>Background</p> <p>Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors.</p> <p>Methods</p> <p>In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology.</p> <p>Results</p> <p>Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes.</p> <p>Conclusions</p> <p>Overall, our <it>in silico </it>study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.</p

Integrated machine learning and mechanistic modelling of Metabolic Syndrome development and dynamics

Metabolic derailments associated with metabolic syndrome and type 2 diabetes can be studied with mixed meal tests (MMT’s). The plasma metabolome enriched with measurements of other biomarkers, such as hormones and cytokines, provide valuable information about the physiological state of an individual. An increasingly important, but complex task is to extract biomedical parameters with diagnostic value from large and multivariate datasets. We applied model-based data processing and analysis, combining computer simulation models of the human physiological system, stochastic models of uncertainties and machine learning of time-series data obtained from repeated blood sampling during MMT’s.\u3cbr/\u3eIn a mouse model of metabolic syndrome we identified differences in lipid metabolism to be associated with variation in weight gain and development of NAFLD (fatty liver disease). The computational model predicted the progression of dyslipidemia to be linked to bile acids, which was confirmed in a validation study including a larger group of mice that were followed for a longer period of time. \u3cbr/\u3eTo investigate the role of bile acids in humans with metabolic syndrome a detailed simulation model of bile acid metabolism and physiology was developed. The dozens of different bile acid species present in blood are to a large extent produced by gut bacteria. Model-based analysis of plasma bile acids provides a metabolic ‘window’ on the gut microbiome and other digestive processes in the gastrointestinal tract. The model was applied to simulate bariatric surgery in patients with metabolic syndrome. The model predicts changes in bile acid concentrations and dynamics in the small intestine to result in a stronger and faster GLP-1 response, hence insulin secretion, explaining observations of rapid glycemic improvement after surgery. The simulation model turned out to be sufficiently robust that personalized variants for individual patients could be made, using MMT plasma bile acid metabolomics as input

Presentation : Development of an age-specific genome-scale model of skeletal muscle metabolism

Skeletal myocytes are among the most metabolically active cell types, implicated in nutrient balance, contributing to the insulin-stimulated clearance of glucose from the blood, and secreting myokines that contribute in regulating inflammation and the ageing process. The loss of muscle mass and strength with age (sarcopenia), is a risk factor for cardiovascular and metabolic diseases, it increases the risk of falls, of developing frailty and disabilities, and results in an impairment in the quality of life and autonomy of an individual.\u3cbr/\u3eAn active lifestyle is the most immediate and accessible treatment to prevent sarcopenia, with a considerable impact on the ageing process: PANINI is a European Training Network whose aim is understanding how lifestyle factors can influence healthy ageing.\u3cbr/\u3e\u3cbr/\u3eIn this context, we present the first age-specific genome-scale metabolic model of the skeletal muscle, a mathematical representation of the myocyte metabolic network in the elderly, built using RECON2, the human metabolic reconstruction, and gene expression data, gathered from older adults' muscle tissue biopsies.\u3cbr/\u3eThis model will be used to analyze patient-specific data for potential mechanisms able to explain the different ageing paces of different individuals and to investigate the effectiveness of different nutritional and physical exercise regimes in stimulating post-exercise protein synthesis, which is often impaired in the elderly.\u3cbr/\u3eThe aim is to identify an optimal and personalized lifestyle change intervention able to prevent the onset of sarcopenia. \u3cbr/\u3e\u3cbr/\u3

The glutamate synthase (GOGAT) of Saccharomyces cerevisiae plays an important role in the central nitrogen metabolism

Central nitrogen metabolism contains two pathways for glutamate biosynthesis, glutaminases and glutamate synthase (GOGAT), using glutamine as the sole nitrogen source. GOGAT's importance for cellular metabolism is still unclear. For a further physiological characterisation of the GOGAT function in central nitrogen metabolism, a GOGAT-negative (¿glt1) mutant strain (VWk274 LEU+) was studied in glutamine-limited continuous cultures. As reference, we did the same experiments with a wild-type strain (VWk43). Intracellular and extracellular metabolites were analysed during different steady states in both strains. The redox state of the cell was taken into account and the NAD(H) and NADP(H) concentrations were determined as well as the reduced and oxidised forms of glutathione (GSH and GSSG, respectively). The results of this study confirm an earlier suggestion, based on a metabolic network model, that GOGAT may be a link between the carbon catabolic reactions (energy production) and nitrogen anabolic reactions (biomass production) by working as a shuttle between cytosol and mitochondria