BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=curation_notes.txthttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338.pdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=manifest.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338.odehttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=metadata.rdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338.pnghttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338_url.sedmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338-matlab.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=BIOMD0000000338-octave.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000338?filename=curation_image.pngprimary200OKMichael SchubertManually curatedordinary differential equation modelHemophilia BL2V4https://www.ebi.ac.uk/biomodels/BIOMD000000033819516255falseBioModelsMODEL1108260015 biomodels.db MODEL1109160000 biomodels.db MODEL1109160001 biomodels.db BIOMD0000000365 biomodels.dbModelsSBMLWajima2009 BloodCoagulation aPTTtest2009MODEL1107010000T Wajima, G K Isbister, S B DuffullT Wajima19516255, Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.. 3, 86. School of Pharmacy, University of Otago, Dunedin, New Zealand.schubert@ebi.ac.ukEBIBIOMD0000000338Linear mathematical models of the kinetics of blood coagulation have previously been presented (Levine, 1966, Science, N.Y. 152, 651; Martorana & Moro, 1974, Math. Biosci. 21, 77). In this paper a non-linear mathematical model of the extrinsic pathway of blood coagulation is presented to take into account a positive feedback. The feedback is due to factor Va as a co-factor involved in thrombin formation. The extrinsic pathway is shown to function as an amplifier cascade if a vessel wall injury exceeds a threshold value. For sub-threshold stimulation, the extrinsic pathway does not function.This paper continues our study (see Part I) where we modeled the spatio-temporal dynamics of the intrinsic pathway of blood coagulation. Here, we analyzed this model and showed that it describes the threshold behavior of coagulation. When activation is subthreshold (which produces not more than 0.07 nM factor XIa at saturating free calcium concentrations of 2 mM or higher), the concentration of generated thrombin remains below 0.01 nM. At the abovethreshold activation corresponding to factor XIa exceeding 0.07 nM, the concentration of thrombin explosively increases and then abruptly decreases. The peak concentration of thrombin reaches hundreds nM. With respect to free calcium concentration, the system also behaves in a threshold manner. For activation corresponding to 0.3 nM factor XIa, the threshold concentration of free calcium where the outburst of explosive thrombin generation occur is equal to 0.21 mM. The model simulations are in a good agreement with the experimentally recorded kinetics of thrombin generation at different concentrations of free calcium (1). Analysis of the spatial dynamics of coagulation showed that if activation exceeded the threshold level at a certain point, the concentration wave of thrombin arises and propagates at a high speed from the activation zone. The parameters of this wave depends mainly on the efficiency of the feedback loops. The feedback loops through the backbone factors of the intrinsic pathway (autoactivation of factor X or activation of factor XI by thrombin) has a potential for the unlimited propagation of the thrombin wave. With increasing activity of activated protein C (the effect equivalent to that of thrombomodulin), oscillating regimes arise in the model. The first thrombin wave is followed by several secondary running waves. The amplitudes of secondary waves increases to the periphery of the clot consolidating its surface layer.Over the last two decades, mathematical modeling has become a popular tool in study of blood coagulation. The in silico methods were able to yield interesting and significant results in the understanding of both individual reaction mechanisms and regulation of large sections of the coagulation cascade. The objective of this paper is to review the development of theoretical research in hemostasis and thrombosis, to summarize the main findings, and outline problems and possible prospects in the use of mathematical modeling and computer simulation approaches. This review is primarily focused on the studies dealing with: (1) the membrane-dependent reactions of coagulation; (2) regulation of the coagulation cascade, including effects of positive and negative feedback loops, diffusion of coagulation factors, and blood flow.A mathematical model for the prothrombin time test is proposed. The time course of clotting factor activation during coagulation was calculated, and the sensitivity of the test to a decrease in the concentrations of coagulation proteins or their activities was studied. The model predicts that only severe coagulation disorders connected with a more than five-fold decrease in the concentrations or activities of the blood coagulation factors can be revealed by the test.Many snake venoms contain procoagulant toxins that activate the coagulation cascade and cause venom-induced consumptive coagulopathy (VICC). We developed a semi-mechanistic model of the clotting cascade in order to explore the effects of the procoagulant toxin from taipan venom on this system as well as the effects of antivenom. Simulations of the time course in the change of clotting factors were compared to data collected from taipan envenomed patients. The model accurately predicted the observed concentration of clotting factors over time following taipan envenomation. Investigations from the model indicated that the upper limit of the half-life of the procoagulant toxin was 1h. Simulations from the model also suggest that antivenom for Australasian elapids has negligible effect on reducing the recovery time of the coagulation profile unless administered almost immediately after envenomation. The model has generality to be expanded to describe the effects of other venoms and drugs on the clotting cascade.Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.The central event of the hemostatic process is the generation of thrombin through the tissue factor pathway. This is a highly regulated, dynamic process in which thrombin itself plays many roles, positively and negatively its production and destruction. The hemostatic process is essential to normal physiology and is also the Achilles heel of our aging population. The inappropriate generation of thrombin may lead to vascular occlusion with the consequence of myocardial infarction, stroke, pulmonary embolism, or venous thrombosis. In this review, we summarize our present views regarding the tissue factor pathway by which thrombin is generated and the roles played by extrinsic and intrinsic factor Xa generating complexes in hemostasis and the roles of the stoichiometric and dynamic inhibitors that regulate thrombin generation.This review examines the evidence that platelets play a major role in localizing and controlling the burst of thrombin generation leading to fibrin clot formation. From the first functional description of platelets, it has been recognized that platelets supply factors that support the activation of prothrombin. Studies have demonstrated that on activation, the amount of one specific lipid, phosphatidylserine, is significantly increased on the outer leaflet of platelet membranes. When it was found that phosphatidylserine containing lipid extracts could be substituted for platelets in clotting assays, this suggested the possibility that changes in platelet lipid composition were necessary and sufficient to account for platelet surface thrombin generation. Because a growing body of data suggest that platelet-binding proteins provide much of the specificity for platelet thrombin generation, we review in this report data suggesting that changes in lipid composition are necessary but not sufficient to account for platelet surface regulation of thrombin generation. Also, we review data suggesting that platelets from different individuals differ in their capacity to generate thrombin, whereas platelets from a single subject support thrombin generation in a reproducible manner. Individual differences in platelet thrombin generation might be accounted for by differences in platelet-binding proteins.The present paper describes a mathematical model of the kinetics of the extrinsic coagulation cascade in vitro. The coagulation factors FI, FII, FV, FVII, FX, heparin and antithrombin III (ATIII) as well as soluble fibrin polymers are considered. The effect of single-factor deficiencies of the factors II, V, VII and X, diseases like hypo- and dysfibrinogenaemia, hepatic insufficiency, inhibited polymerisation by degradation products, heparin therapy with and without ATIII deficiency and coumarin therapy on prothrombin time can be portrayed. Physiology of coagulation is represented in a dynamic mathematical model as a differential equation system. The model is based on three reaction types: enzymatic cleavage, complex formation and polymerisation. The model was implemented in a continuous simulation program on a personal computer using the Pascal programming language. Unknown rate constants were estimated by chi 2 fit. Prothrombin time calculated by the model was compared to the training set of 20 plasma samples. In most but not all cases the model harmonized quite well with the coagulometric data.A mathematical simulation pathway for the generation of thrombin has been developed with various assumptions made of kinetic rate laws and their summation for reactions involving the activation of factors VIII, IX, X and V and protein C in the formation of thrombin. The object of the computational modelling study is to stimulate the activation and inhibition of blood coagulation. The level of complexity and assumed parameters makes conclusions uncertain. However, an interesting outcome is that kinetic rates may show oscillation behavior under particular high levels of protein C feedback inhibition. The model, which permits the assessment of the reaction over a broad range of conditions, would defy quantitative practical use, but could have predictive value as a qualitative descriptor of coagulation.A mathematical model of the kinetics of blood coagulation.The dynamics of thrombin formation.Mathematical modeling and computer simulation in blood coagulation.The quick machine--a mathematical model for the extrinsic activation of coagulation.A kinetic model for simulation of blood coagulation and inhibition in the intrinsic path.A mathematical model for the spatio-temporal dynamics of intrinsic pathway of blood coagulation. II. Results.A model for venom-induced consumptive coagulopathy in snake bite.A comprehensive model for the humoral coagulation network in humans.Mathematical model for the blood coagulation prothrombin time test.Platelets and thrombin generation.Tanos P P PP, Isbister G K GK, Lalloo D G DG, Kirkpatrick C M J CM, Duffull S B SBQiao Y H YH, Liu J L JL, Zeng Y J YJKhanin M A MA, Semenov V V VVKhanin M A MA, Rakov D V DV, Kogan A E AEMann Kenneth G KG, Butenas Saulius S, Brummel Kathleen KAtaullakhanov Fazoil I FI, Panteleev Mikhail A MAZarnitsina V I VI, Pokhilko A V AV, Ataullakhanov F I FIPohl B B, Beringer C C, Bomhard M M, Keller F FMonroe Dougald M DM, Hoffman Maureane M, Roberts Harold R HRWajima T T, Isbister G K GK, Duffull S B SBfalseWajima2009_BloodCoagulation_aPTTtest This model is from the article: A comprehensive model for the humoral coagulation network in humans. Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255, Abstract: Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network. 2024-08-212024-09-022011-07-01BIOMD00000003381951625596459162779263158048558948060164323081252422012231555188319817843644CHEBI:28384CHEBI:28304CHEBI:10033MODEL1107010000BIOMD0000000338GO:00723789606P00734P00451P04070P07225P00740P03951P00748P08709P00742P12259P02675P02671P02679P00747P05160P00488P07204P13726P10646P03952P01008