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

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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:

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.

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Format
SBML (L2V4)
Related Publication
  • 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 & systems pharmacology , 7/ 2015 , Volume 4 , pages: 396-405
  • Pharmacometrics, Global Innovative Pharma Business (GIPB), Pfizer Inc. Cambridge, Massachusetts, USA.
  • 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.
Contributors
Satyaprakash Nayak

Metadata information

is
BioModels Database MODEL1511160000
BioModels Database BIOMD0000000611
isDerivedFrom
BioModels Database BIOMD0000000340
BioModels Database MODEL1108260014
BioModels Database BIOMD0000000338
BioModels Database BIOMD0000000335
BioModels Database BIOMD0000000339
isDescribedBy
PubMed 26312163
hasTaxon
Taxonomy Homo sapiens
isVersionOf
Gene Ontology blood coagulation
hasProperty
Human Disease Ontology blood coagulation disease
Curation status
Curated
  • Model originally submitted by : Satyaprakash Nayak
  • Submitted: Nov 16, 2015 6:21:48 PM
  • Last Modified: Jan 13, 2017 4:14:08 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Jan 13, 2017 4:14:08 PM
    • Submitted by: Satyaprakash Nayak
    • With comment: Current version of Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
  • Version: 1 public model Download this version
    • Submitted on: Nov 16, 2015 6:21:48 PM
    • Submitted by: Satyaprakash Nayak
    • With comment: Original import of Coagulation_Systems_Model_Nayak_et_al
Curator's comment:
(added: 09 May 2016, 14:29:50, updated: 09 May 2016, 14:29:50)
Figures 2B, G, L and Q of the reference publication has been reproduced here. The matlab script that was used to generate the figures was sent by the author Satyaprakash Nayak. The script uses the Simbiology file to generate the figures. The matlab script and the SimBiology (.sbproj) of the model can be downloaded from below link. Note: The SBML file was also checked for simulation using Copasi v4.15 (Build 95), and it reproduces the results in the paper. The SBML file was generated from SimBiology.