Dataset Information

Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers

ABSTRACT: Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers Note: 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. Abstract: 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. This model is hosted on BioModels Database and identified by: MODEL1511160000. To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

DISEASE(S): Blood Coagulation Disease

SUBMITTER: Satyaprakash Nayak

PROVIDER: BIOMD0000000611 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000000611?filename=BIOMD0000000611-biopax2.owl	Owl
	BIOMD0000000611?filename=BIOMD0000000611-biopax3.owl	Owl
	BIOMD0000000611?filename=BIOMD0000000611-matlab.m	Other
	BIOMD0000000611?filename=BIOMD0000000611-octave.m	Other
	BIOMD0000000611?filename=BIOMD0000000611.m	Other

Items per page:

1 - 5 of 18

Publications

Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.

Nayak S S Lee D D Patel-Hett S S Pittman D D DD Martin S W SW Heatherington A C AC Vicini P P Hua F F

CPT: pharmacometrics & systems pharmacology 20150619 7

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 norm ...[more]

PMID: 26312163

Similar Datasets

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 11893748, Abstract: 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: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. Abstract: 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. This model is hosted on BioModels Database and identified by: BIOMD0000000358 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.