Project description:We propose a detailed CellML model of the human cerebral circulation that runs faster than real time on a desktop computer and is designed for use in clinical settings when the speed of response is important. A lumped parameter mathematical model, which is based on a one-dimensional formulation of the flow of an incompressible fluid in distensible vessels, is constructed using a bond graph formulation to ensure mass conservation and energy conservation. The model includes arterial vessels with geometric and anatomical data based on the ADAN circulation model. The peripheral beds are represented by lumped parameter compartments. We compare the hemodynamics predicted by the bond graph formulation of the cerebral circulation with that given by a classical one-dimensional Navier-Stokes model working on top of the whole-body ADAN model. Outputs from the bond graph model, including the pressure and flow signatures and blood volumes, are compared with physiological data.
Project description:Graph neural networks (GNNs) recursively propagate signals along the edges of an input graph, integrate node feature information with graph structure, and learn object representations. Like other deep neural network models, GNNs have notorious black box character. For GNNs, only few approaches are available to rationalize model decisions. We introduce EdgeSHAPer, a generally applicable method for explaining GNN-based models. The approach is devised to assess edge importance for predictions. Therefore, EdgeSHAPer makes use of the Shapley value concept from game theory. For proof-of-concept, EdgeSHAPer is applied to compound activity prediction, a central task in drug discovery. EdgeSHAPer's edge centricity is relevant for molecular graphs where edges represent chemical bonds. Combined with feature mapping, EdgeSHAPer produces intuitive explanations for compound activity predictions. Compared to a popular node-centric and another edge-centric GNN explanation method, EdgeSHAPer reveals higher resolution in differentiating features determining predictions and identifies minimal pertinent positive feature sets.
Project description:The aim of this study is to identify candidate genes modulating platelet reactivity in aspirin-treated cardiovascular patients using an integrative network-based approach. Platelet reactivity was assessed in 110 cardiovascular patients treated with aspirin 100mg/d by aggregometry using several agonists. Patients with extreme high or low PR were selected for further analysis. Data derived from quantitative proteomic of platelets and platelet sub-cellular fractions, as well as from transcriptomic analysis were integrated with a network biology approach.
Project description:We performed RNA sequencing (RNA-seq) and m6A methylated RNA immunoprecipitation sequencing (MeRIP-seq) in control and Zfp217 knockout 3T3L1 cells with MDI treatment for 0d and 2d Loss-of-function study demonstrates that Zfp217 deficiency impaired adipogenesis together with a global increase of m6A modification in 3T3L1cells. To gain an overview of the global role of Zfp217 in adipogenesis, we performed RNA sequencing (RNA-seq) in control and Zfp217 knockout 3T3L1 cells with MDI treatment for 0d and 2d and identified 12,188 and 11,566 different expressed genes (DEG) for 0d and 2d, respectively. Next, to elucidate the mechanism by which Zfp217 regulates m6A restriction, m6A methylated RNA immunoprecipitation sequencing (MeRIP-seq) was used to analyze the m6A mRNA methylation in control and Zfp217 knockout 3T3L1 cells with MDI treatment for 0d and 2d, and identified 3149 and 294 of m6A peaks experienced an increase in m6A RNA modification after Zfp217 depletion, respectively.
Project description:A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.