Stortelder1997 - Thrombin Generation Amidolytic Activity
Mathematical modelling of a part of the blood coagulation mechanism.
This model is described in the article:
Mathematical modelling in blood coagulation : simulation and parameter estimation.
Stortelder W.J.H., Hemker P.W., Hemker, H.C.
CWI. Modelling, Analysis and Simulation, No. R 9720, p.1-11.
This paper describes the mathematical modelling of a part of the blood coagulation mechanism. The model includes the activation of factor X by a purified enzyme from Russel's Viper Venom (RVV), factor V and prothrombin, and also comprises the inactivation of the products formed. In this study we assume that in principle the mechanism of the process is known. However, the exact structure of the mechanism is unknown, and the process still can be described by different mathematical models. These models are put to test by measuring their capacity to explain the course of thrombin generation as observed in plasma after recalcification in presence of RVV. The mechanism studied is mathematically modelled as a system of differential-algebraic equations (DAEs). Each candidate model contains some freedom, which is expressed in the model equations by the presence of unknown parameters. For example, reaction constants or initial concentrations are unknown. The goal of parameter estimation is to determine these unknown parameters in such a way that the theoretical (i.e., computed) results fit the experimental data within measurement accuracy and to judge which modifications of the chemical reaction scheme allow the best fit. We present results on model discrimination and estimation of reaction constants, which are hard to obtain in another way.
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Project description:Mathematical model of blood coagulation. Extended model of Mitrophanov2011 (which is an extension of Hockin2002). Additional reactions added involving thrombin. Modelling the effects of dilution and addition of recombinant factor VIIa, II, VII, IX, X, AT.
Project description:Model listing the reactions of the intrinsic pathway as listed in Zarnitsina1996. Publication model is a spatio-termporal mathematical model of blood coagulation.
Model used as the example of the numerical intrinsic pathway in Braescu et al. (2011).
L. Braescu, M. Leretter, T. George, New direct inhibitors and their computed effect on the dynamics of thrombin formation in blood coagulation, in: T. F. George (Ed.), Computational Studies of New Materials II, World Scientific, 2011, 173–190. doi:10.3389/fphys.2012.00266.
Project description:Mathematical model of blood coagulation. Extension of Luan2007. New reactions, e.g., the activation of platelet by thrombin through PARs, the release of ADP and TXA2 from activated platelets, platelet activation by ADP and TXA2, the intrinsic pathway reactions, were added to the model.
Project description:Mathematical model of blood coagulation investigating effects of varied factor VIIa on thrombin generation. Model derived from Hockin2002/Butenas2004. Butenas added two new reactions (R28 and R29) and two new parameters (k43 and k44). Here, changes to parameters k32 and k38 and TF initial conc. changed to 5e-12
Project description:This model is from the article:
A model for the stoichiometric regulation of blood coagulation.
Hockin MF, Jones KC, Everse SJ, Mann KG.
Journal of Biological ChemistryVolume 277, Issue 21, 24 May 2002, Pages 18322 -18333
We have developed a model of the extrinsic blood coagulation system that includes the stoichiometric anticoagulants. The model accounts for the formation, expression, and propagation of the vitamin K-dependent procoagulant complexes and extends our previous model by including: (a) the tissue factor pathway inhibitor (TFPI)-mediated inactivation of tissue factor (TF).VIIa and its product complexes; (b) the antithrombin-III (AT-III)-mediated inactivation of IIa, mIIa, factor VIIa, factor IXa, and factor Xa; (c) the initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; (d) factor VIIIa dissociation/activity loss; (e) the binding competition and kinetic activation steps that exist between TF and factors VII and VIIa; and (f) the activation of factor VII by IIa, factor Xa, and factor IXa. These additions to our earlier model generate a model consisting of 34 differential equations with 42 rate constants that together describe the 27 independent equilibrium expressions, which describe the fates of 34 species. Simulations are initiated by "exposing" picomolar concentrations of TF to an electronic milieu consisting of factors II, IX, X, VII, VIIa, V, and VIIII, and the anticoagulants TFPI and AT-III at concentrations found in normal plasma or associated with coagulation pathology. The reaction followed in terms of thrombin generation, proceeds through phases that can be operationally defined as initiation, propagation, and termination. The generation of thrombin displays a nonlinear dependence upon TF, AT-III, and TFPI and the combination of these latter inhibitors displays kinetic thresholds. At subthreshold TF, thrombin production/expression is suppressed by the combination of TFPI and AT-III; for concentrations above the TF threshold, the bolus of thrombin produced is quantitatively equivalent. A comparison of the model with empirical laboratory data illustrates that most experimentally observable parameters are captured, and the pathology that results in enhanced or deficient thrombin generation is accurately described.
Project description:Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
The SBML model is generated from SimBiology. The SimBiology (.sbproj) file is available for download from the curation tab.
This model is described in the article:
Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.
Nayak S, Lee D, Patel-Hett S, Pittman DD, Martin SW, Heatherington AC, Vicini P, Hua F.
CPT Pharmacometrics Syst Pharmacol. 2015 Jul;4(7):396-405.
A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.
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