scholarly journals cGMP signaling: probing antagonistic cyclic nucleotide platelet signals by modeling and experiment

2021 ◽  
Author(s):  
Tim Breitenbach ◽  
Nils Englert ◽  
Özge Osmanoglu ◽  
Natalia Rukoyatkina ◽  
Gaby Wangorsch ◽  
...  
Keyword(s):  
Cyclic Nucleotide ◽  
Optimization Methods ◽  
Data Driven ◽  
Pharmacological Interventions ◽  
Modular Model ◽  
Vasp Phosphorylation ◽  
Cgmp Signaling ◽  
Data Driven Modeling ◽  
External Stimuli ◽  
Camp And Cgmp

AbstractBackgroundThe cyclic nucleotides cAMP and cGMP inhibit platelet activation.ResultsWe extended an older model and systematically integrated drugs as external stimuli. Data driven modeling allowed us to design models that provide a quantitative output for quantitative input information. This relies on condensed information about involved regulation and modeling of pharmacological interventions by systematic optimization methods. By multi-experiment fitting, we validated our model optimizing the parameters of the model. In addition, we show how the output of the developed cGMP model can be used as input for a modular model of VASP phosphorylation and for the activity of cAMP and cGMP pathways in platelets.ConclusionsWe present a model for cGMP signaling and VASP phosphorylation, that allows to estimate drug action on any of the inhibitory cyclic nucleotide pathways (cGMP, cAMP) and has been validated by experimental data.

scholarly journals Clinical and Molecular Genetics of the Phosphodiesterases (PDEs)

2013 ◽  
Vol 35 (2) ◽  
pp. 195-233 ◽  
Author(s):  
Monalisa F. Azevedo ◽  
Fabio R. Faucz ◽  
Eirini Bimpaki ◽  
Anelia Horvath ◽  
Isaac Levy ◽  
...  
Keyword(s):  
Cyclic Nucleotide ◽  
Heart Function ◽  
Biological Effects ◽  
Genetic Diseases ◽  
Functional Variants ◽  
Cgmp Signaling ◽  
Health And Disease ◽  
Nucleotide Signaling ◽  
Hydrolysis Of ◽  
Camp And Cgmp

Abstract Cyclic nucleotide phosphodiesterases (PDEs) are enzymes that have the unique function of terminating cyclic nucleotide signaling by catalyzing the hydrolysis of cAMP and GMP. They are critical regulators of the intracellular concentrations of cAMP and cGMP as well as of their signaling pathways and downstream biological effects. PDEs have been exploited pharmacologically for more than half a century, and some of the most successful drugs worldwide today affect PDE function. Recently, mutations in PDE genes have been identified as causative of certain human genetic diseases; even more recently, functional variants of PDE genes have been suggested to play a potential role in predisposition to tumors and/or cancer, especially in cAMP-sensitive tissues. Mouse models have been developed that point to wide developmental effects of PDEs from heart function to reproduction, to tumors, and beyond. This review brings together knowledge from a variety of disciplines (biochemistry and pharmacology, oncology, endocrinology, and reproductive sciences) with emphasis on recent research on PDEs, how PDEs affect cAMP and cGMP signaling in health and disease, and what pharmacological exploitations of PDEs may be useful in modulating cyclic nucleotide signaling in a way that prevents or treats certain human diseases.

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

Data-Driven Modeling Approach to Predict the Recovery Performance of Low-Salinity Waterfloods

2021 ◽  
Author(s):  
Shams Kalam ◽  
Rizwan Ahmed Khan ◽  
Shahnawaz Khan ◽  
Muhammad Faizan ◽  
Muhammad Amin ◽  
...  
Keyword(s):  
Data Driven ◽  
Low Salinity ◽  
Modeling Approach ◽  
Recovery Performance ◽  
Data Driven Modeling

scholarly journals From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

2021 ◽  
Author(s):  
K. Midzodzi Pekpe ◽  
Djamel Zitouni ◽  
Gilles Gasso ◽  
Wajdi Dhifli ◽  
Benjamin C. Guinhouya
Keyword(s):  
System Theory ◽  
Data Driven ◽  
Modeling Approach ◽  
Data Driven Modeling
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