Free Access
Issue |
Math. Model. Nat. Phenom.
Volume 9, Number 6, 2014
Blood flows
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Page(s) | 4 - 16 | |
DOI | https://doi.org/10.1051/mmnp/20149602 | |
Published online | 31 July 2014 |
- M.J. Davies. The pathophysiology of acute coronary syndromes. Heart, 83 (2000), 361–366. [CrossRef] [PubMed] [Google Scholar]
- State-specific mortality from sudden cardiac death –United States, 1999. MMWR Morb. Mortal. Wkly. Rep., 51 (2002), 123–126. [Google Scholar]
- H.C. Hemker, S. Kerdelo, R.M. Kremers. Is there value in kinetic modeling of thrombin generation? No (unless...). J. Thromb. Haemost., 10 (2012), 1470–1477. [Google Scholar]
- K.G. Mann. Is there value in kinetic modeling of thrombin generation? Yes. J. Thromb.Haemost., 10 (2012), 1463–1469. [Google Scholar]
- J.D. Barr, A.K. Chauhan, G.V. Schaeffer, J.K. Hansen, D.G. Motto. Red blood cells mediate the onset of thrombosis in the ferric chloride murine model. Blood, 121 (2013), 3733–3741. [CrossRef] [PubMed] [Google Scholar]
- F.I. Ataullakhanov, M.A. Panteleev. Mathematical modeling and computer simulation in blood coagulation. Pathophysiol. Haemost. Thromb., 34 (2005), 60–70. [CrossRef] [PubMed] [Google Scholar]
- K.E. Brummel-Ziedins, T. Orfeo, F.R. Rosendaal, A. Undas, G.E. Rivard, S. Butenas, K.G. Mann. Empirical and theoretical phenotypic discrimination. J. Thromb. Haemost., 7 Suppl 1 (2009), 181–186. [CrossRef] [PubMed] [Google Scholar]
- S.L. Diamond. Systems biology to predict blood function. Journal of Thrombosis and Haemostasis, 7 (2009), 177–180. [CrossRef] [Google Scholar]
- A.Y. Mitrophanov, J. Reifman. Kinetic modeling sheds light on the mode of action of recombinant factor VIIa on thrombin generation. Thromb. Res., 128 (2011), 381–390. [CrossRef] [PubMed] [Google Scholar]
- A.Y. Mitrophanov, F.R. Rosendaal, J. Reifman. Therapeutic correction of thrombin generation in dilution-induced coagulopathy: computational analysis based on a data set of healthy subjects. J. Trauma Acute. Care Surg., 73 (2012), S95–S102. [CrossRef] [PubMed] [Google Scholar]
- A.Y. Mitrophanov, F.R. Rosendaal, J. Reifman. Computational analysis of intersubject variability and thrombin generation in dilutional coagulopathy. Transfusion, 52 (2012), 2475–2486. [CrossRef] [PubMed] [Google Scholar]
- A.Y. Mitrophanov, F.R. Rosendaal, J. Reifman. Computational Analysis of the Effects of Reduced Temperature on Thrombin Generation: The Contributions of Hypothermia to Coagulopathy. Anesth. Analg., 2013. [Google Scholar]
- T. Orfeo, S. Butenas, K.E. Brummel-Ziedins, M. Gissel, K.G. Mann. Anticoagulation by factor Xa inhibitors. J. Thromb. Haemost., 8 (2010), 1745–1753. [CrossRef] [PubMed] [Google Scholar]
- M.A. Panteleev, N.M. Ananyeva, F.I. Ataullakhanov, E.L. Saenko. Mathematical models of blood coagulation and platelet adhesion: clinical applications. Curr. Pharm. Des, 13 (2007), 1457–1467. [Google Scholar]
- L.A. Parunov, O.A. Fadeeva, A.N. Balandina, N.P. Soshitova, K.G. Kopylov, M.A. Kumskova, J.C. Gilbert, R.G. Schaub, K.E. McGinness, F.I. Ataullakhanov, M.A. Panteleev. Improvement of spatial fibrin formation by the anti-TFPI aptamer BAX499: changing clot size by targeting extrinsic pathway initiation. J. Thromb. Haemost., 9 (2011), 1825–1834. [Google Scholar]
- J.E. Purvis, M.S. Chatterjee, L.F. Brass, S.L. Diamond. A molecular signaling model of platelet phosphoinositide and calcium regulation during homeostasis and P2Y1 activation. Blood, 112 (2008), 4069–4079. [CrossRef] [PubMed] [Google Scholar]
- A.M. Shibeko, S.A. Woodle, T.K. Lee, M.V. Ovanesov. Unifying the mechanism of recombinant FVIIa action: dose dependence is regulated differently by tissue factor and phospholipids. Blood, 120 (2012), 891–899. [CrossRef] [PubMed] [Google Scholar]
- T. Wajima, G.K. Isbister, S.B. Duffull. A comprehensive model for the humoral coagulation network in humans. Clin. Pharmacol. Ther., 86 (2009), 290–298. [Google Scholar]
- Z. Xu, M. Kamocka, M. Alber, E.D. Rosen, Computational approaches to studying thrombus development. Arterioscler. Thromb. Vasc. Biol., 31 (2011), 500–505. [CrossRef] [PubMed] [Google Scholar]
- Z. Xu, S. Christley, J. Lioi, O. Kim, C. Harvey, W. Sun, E.D. Rosen, M. Alber. Multiscale model of fibrin accumulation on the blood clot surface and platelet dynamics. Methods Cell Biol., 110 (2012), 367–388. [CrossRef] [PubMed] [Google Scholar]
- Z. Xu, O. Kim, M. Kamocka, E.D. Rosen, M. Alber. Multiscale models of thrombogenesis. Wiley. Interdiscip. Rev. Syst. Biol. Med., 4 (2012), 237–246. [Google Scholar]
- A. Fasano, R.F. Santos, A. Sequeira. Blood coagulation: A puzzle for biologists, a maze for mathematicians. Modeling of Physiological Flows, Springer, (2012), 41–75. [Google Scholar]
- F.I. Ataullakhanov, N.M. Dashkevich, C. Negrier, M.A. Panteleev. Factor XI and traveling waves: the key to understanding coagulation in hemophilia? Expert. Rev. Hematol., 6 (2013), 111–113. [CrossRef] [PubMed] [Google Scholar]
- M.A. Panteleev, A.N. Balandina, E.N. Lipets, M.V. Ovanesov, F.I. Ataullakhanov. Task-oriented modular decomposition of biological networks: trigger mechanism in blood coagulation. Biophys. J., 98 (2010), 1751–1761. [Google Scholar]
- K.E. Brummel-Ziedins, T. Orfeo, P.W. Callas, M. Gissel, K.G. Mann, E.G. Bovill. The prothrombotic phenotypes in familial protein C deficiency are differentiated by computational modeling of thrombin generation. PLoS.One., 7 (2012), e44378. [CrossRef] [Google Scholar]
- C.M. Danforth, T. Orfeo, S.J. Everse, K.G. Mann, K.E. Brummel-Ziedins. Defining the boundaries of normal thrombin generation: investigations into hemostasis. PLoS One., 7 (2012), e30385. [CrossRef] [PubMed] [Google Scholar]
- A.O. Yakimenko, F.Y. Verholomova, Y.N. Kotova, F.I. Ataullakhanov, M.A. Panteleev. Identification of different proaggregatory abilities of activated platelet subpopulations. Biophys. J., 102 (2012), 2261–2269. [CrossRef] [PubMed] [Google Scholar]
- M.A. Panteleev, N.M. Ananyeva, N.J. Greco, F.I. Ataullakhanov, E.L. Saenko. Factor VIIIa regulates substrate delivery to the intrinsic factor X-activating complex. FEBS J., 273 (2006), 374–387. [CrossRef] [PubMed] [Google Scholar]
- R.D. Guy, A.L. Fogelson, J.P. Keener. Fibrin gel formation in a shear flow. Math. Med. Biol., 24 (2007), 111–130. [CrossRef] [PubMed] [Google Scholar]
- L.M. Crowl, A.L. Fogelson. Computational model of whole blood exhibiting lateral platelet motion induced by red blood cells. Int.j.numer.method.biomed.eng, 26 (2010), 471–487. [Google Scholar]
- T. Skorczewski, L.C. Erickson, A.L. Fogelson. Platelet motion near a vessel wall or thrombus surface in two–dimensional whole blood simulations. Biophys. J., 104 (2013), 1764–1772. [CrossRef] [PubMed] [Google Scholar]
- D.A. Fedosov, B. Caswell, G.E. Karniadakis. A multiscale red blood cell model with accurate mechanics, rheology, and dynamics. Biophys. J., 98 (2010), 2215–2225. [Google Scholar]
- D.A. Fedosov, H. Lei, B. Caswell, S. Suresh, G.E. Karniadakis. Multiscale modeling of red blood cell mechanics and blood flow in malaria. PLoS Comput.Biol., 7 (2011), e1002270. [Google Scholar]
- D.A. Fedosov, H. Noguchi, G. Gompper. Multiscale modeling of blood flow: from single cells to blood rheology. Biomech. Model. Mechanobiol., 2013. [Google Scholar]
- C.R. Sweet, S. Chatterjee, Z. Xu, K. Bisordi, E.D. Rosen, M. Alber. Modelling platelet-blood flow interaction using the subcellular element Langevin method. J.R. Soc. Interface, 8 (2011), 1760–1771. [Google Scholar]
- A. Tosenberger, V. Salnikov, N. Bessonov, E. Babushkina, V. Volpert. Particle dynamics methods of blood flow simulations. Mathematical Modelling of Natural Phenomena, 6 (2011), 320–332. [Google Scholar]
- A.A. Tokarev, A.A. Butylin, E.A. Ermakova, E.E. Shnol, G.P. Panasenko, F.I. Ataullakhanov. Finite platelet size could be responsible for platelet margination effect. Biophys. J., 101 (2011), 1835–1843. [CrossRef] [PubMed] [Google Scholar]
- A.A. Tokarev, A.A. Butylin, F.I. Ataullakhanov. Platelet adhesion from shear blood flow is controlled by near-wall rebounding collisions with erythrocytes. Biophys. J., 100 (2011), 799–808. [CrossRef] [PubMed] [Google Scholar]
- N.A. Mody, M.R. King. Influence of Brownian motion on blood platelet flow behavior and adhesive dynamics near a planar wall. Langmuir, 23 (2007), 6321–6328. [CrossRef] [PubMed] [Google Scholar]
- W. Wang, N.A. Mody, M.R. King. Multiscale model of platelet translocation and collision. J. Comput. Phys., 244 (2013), 223–235. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Z. Xu, N. Chen, M.M. Kamocka, E.D. Rosen, M. Alber. A multiscale model of thrombus development. J.R.Soc.Interface, 5 (2008), 705–722. [Google Scholar]
- Z. Xu, J. Lioi, J. Mu, M.M. Kamocka, X. Liu, D.Z. Chen, E.D. Rosen, M. Alber. A multiscale model of venous thrombus formation with surface-mediated control of blood coagulation cascade. Biophys. J., 98 (2010), 1723–1732. [Google Scholar]
- A.L. Fogelson, Y.H. Hussain, K. Leiderman, Blood clot formation under flow: the importance of factor XI depends strongly on platelet count. Biophysical journal, 102 (2012), 10–18. [CrossRef] [PubMed] [Google Scholar]
- K. Leiderman, A.L. Fogelson. Grow with the flow: a spatial-temporal model of platelet deposition and blood coagulation under flow. Mathematical Medicine and Biology, 28 (2011), 47–84. [CrossRef] [MathSciNet] [Google Scholar]
- N. Filipovic, M. Kojic, A. Tsuda. Modelling thrombosis using dissipative particle dynamics method. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366 (2008), 3265–3279. [Google Scholar]
- H. Kamada, Y. Imai, M. Nakamura, T. Ishikawa, T. Yamaguchi. Computational study on thrombus formation regulated by platelet glycoprotein and blood flow shear. Microvasc. Res., 89 (2013), 95–106. [CrossRef] [PubMed] [Google Scholar]
- S. Zimny, B. Chopard, S. Malaspinas, E. Lorenz, K. Jain, S. Roller, J. Bernsdorf. A multiscale approach for the coupled simulation of blood flow and thrombus formation in intracranial aneurysms. Procedia Computer Science, 18 (2013), 1006–1015. [CrossRef] [Google Scholar]
- A.L. Fogelson, R.D. Guy. Platelet-wall interactions in continuum models of platelet thrombosis: formulation and numerical solution. Math. Med. Biol., 21 (2004), 293–334. [CrossRef] [PubMed] [Google Scholar]
- J.E. Purvis, R. Radhakrishnan, S.L. Diamond. Steady-state kinetic modeling constrains cellular resting states and dynamic behavior. PLoS computational biology, 5 (2009), e1000298. [CrossRef] [PubMed] [Google Scholar]
- L. Lenoci, M. Duvernay, S. Satchell, E. DiBenedetto, H.E. Hamm. Mathematical model of PAR1-mediated activation of human platelets. Mol. Biosyst., 7 (2011), 1129–1137. [CrossRef] [PubMed] [Google Scholar]
- G. Wangorsch, E. Butt, R. Mark, K. Hubertus, J. Geiger, T. Dandekar, M. Dittrich. Time-resolved in silico modeling of fine-tuned cAMP signaling in platelets: feedback loops, titrated phosphorylations and pharmacological modulation. BMC. Syst. Biol., 5 (2011), 178. [CrossRef] [PubMed] [Google Scholar]
- M.A. Panteleev, M.V. Ovanesov, D.A. Kireev, A.M. Shibeko, E.I. Sinauridze, N.M. Ananyeva, A.A. Butylin, E.L. Saenko, F.I. Ataullakhanov. Spatial propagation and localization of blood coagulation are regulated by intrinsic and protein C pathways, respectively. Biophys. J., 90 (2006), 1489–1500. [Google Scholar]
- A. Tosenberger, F. Ataullakhanov, N. Bessonov, M. Panteleev, A. Tokarev, V. Volpert. Modelling of thrombus growth and growth stop in flow by the method of dissipative particle dynamics. Russian Journal of Numerical Analysis and Mathematical Modelling, 27 (2013), 507–522. [Google Scholar]
- A. Tosenberger, F. Ataullakhanov, N. Bessonov, M. Panteleev, A. Tokarev, V. Volpert. Modelling of thrombus growth in flow with a DPD-PDE method. J. Theor. Biol., 337 (2013), 30–41. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- K.C. Jones, K.G. Mann. A model for the tissue factor pathway to thrombin. II. A mathematical simulation. J. Biol. Chem., 269 (1994), 23367–23373. [PubMed] [Google Scholar]
- M.F. Hockin, K.C. Jones, S.J. Everse, K.G. Mann. A model for the stoichiometric regulation of blood coagulation. J. Biol. Chem., 277 (2002), 18322–18333. [Google Scholar]
- K.E. Brummel-Ziedins, T. Orfeo, M. Gissel, K.G. Mann, F.R. Rosendaal. Factor Xa generation by computational modeling: an additional discriminator to thrombin generation evaluation. PLoS.One., 7 (2012), e29178. [CrossRef] [Google Scholar]
- S. Butenas, T. Orfeo, M.T. Gissel, K.E. Brummel, K.G. Mann. The significance of circulating factor IXa in blood. J. Biol. Chem., 279 (2004), 22875–22882. [CrossRef] [PubMed] [Google Scholar]
- C.M. Danforth, T. Orfeo, K.G. Mann, K.E. Brummel-Ziedins, S.J. Everse. The impact of uncertainty in a blood coagulation model. Math. Med. Biol., 26 (2009), 323–336. [Google Scholar]
- T. Orfeo, M. Gissel, S. Butenas, A. Undas, K.E. Brummel-Ziedins, K.G. Mann. Anticoagulants and the propagation phase of thrombin generation. PLoS. One., 6 (2011), e27852. [CrossRef] [PubMed] [Google Scholar]
- M.S. Chatterjee, W.S. Denney, H. Jing, S.L. Diamond. Systems biology of coagulation initiation: kinetics of thrombin generation in resting and activated human blood. PLoS computational biology, 6 (2010), e1000950. [Google Scholar]
- A.N. Balandina, A.M. Shibeko, D.A. Kireev, A.A. Novikova, I.I. Shmirev, M.A. Panteleev, F.I. Ataullakhanov. Positive feedback loops for factor V and factor VII activation supply sensitivity to local surface tissue factor density during blood coagulation. Biophys. J., 101 (2011), 1816–1824. [Google Scholar]
- N.M. Dashkevich, M.V. Ovanesov, A.N. Balandina, S.S. Karamzin, P.I. Shestakov, N.P. Soshitova, A.A. Tokarev, M.A. Panteleev, F.I. Ataullakhanov. Thrombin activity propagates in space during blood coagulation as an excitation wave. Biophys. J., 103 (2012), 2233–2240. [CrossRef] [PubMed] [Google Scholar]
- A.M. Shibeko, E.S. Lobanova, M.A. Panteleev, F.I. Ataullakhanov. Blood flow controls coagulation onset via the positive feedback of factor VII activation by factor Xa. BMC. Syst. Biol., 4 (2010), 5. [Google Scholar]
- A.A. Tokarev, Y.V. Krasotkina, M.V. Ovanesov, M.A. Panteleev, M.A. Azhigirova, V.A. Volpert, F.I. Ataullakhanov, A.A. Butilin. Spatial Dynamics of Contact-Activated Fibrin Clot Formation in vitro and in silico in Haemophilia B: Effects of Severity and Ahemphil B Treatment. Mathematical Modelling of Natural Phenomena, 1 (2006), 124–137. [Google Scholar]
- M. Anand, K. Rajagopal, K.R. Rajagopal. A model incorporating some of the mechanical and biochemical factors underlying clot formation and dissolution in flowing blood: review article. Journal of Theoretical Medicine, 5 (2003), 183–218. [CrossRef] [MathSciNet] [Google Scholar]
- M. Anand, K. Rajagopal, K.R. Rajagopal. A model for the formation, growth, and lysis of clots in quiescent plasma. A comparison between the effects of antithrombin III deficiency and protein C deficiency. Journal of theoretical biology, 253 (2008), 725–738. [Google Scholar]
- M.A. Panteleev, E.L. Saenko, N.M. Ananyeva, F.I. Ataullakhanov. Kinetics of Factor X activation by the membrane-bound complex of Factor IXa and Factor VIIIa. Biochem. J., 381 (2004), 779–794. [CrossRef] [PubMed] [Google Scholar]
- Y.N. Kotova, F.I. Ataullakhanov, M.A. Panteleev. Formation of coated platelets is regulated by the dense granule secretion of adenosine 5’diphosphate acting via the P2Y12 receptor. J. Thromb. Haemost., 6 (2008), 1603–1605. [CrossRef] [PubMed] [Google Scholar]
- M.A. Panteleev, N.M. Ananyeva, N.J. Greco, F.I. Ataullakhanov, E.L. Saenko. Two subpopulations of thrombin-activated platelets differ in their binding of the components of the intrinsic factor X-activating complex. J. Thromb. Haemost., 3 (2005), 2545–2553. [CrossRef] [PubMed] [Google Scholar]
- A.V. Pokhilko, F.I. Ataullakhanov. Contact activation of blood coagulation: trigger properties and hysteresis. Kinetic recognition of foreign surfaces upon contact activation of blood coagulation: a hypothesis. J. Theor. Biol., 191 (1998), 213–219. [CrossRef] [PubMed] [Google Scholar]
- B.E. Bannish, J.P. Keener, M. Woodbury, J.W. Weisel, A.L. Fogelson. Modelling fibrinolysis: 1D continuum models. Math. Med. Biol., 2012. [Google Scholar]
- B.E. Bannish, J.P. Keener, A.L. Fogelson. Modelling fibrinolysis: a 3D stochastic multiscale model. Math. Med. Biol., 2012. [Google Scholar]
- M.V. Ovanesov, M.A. Panteleev, E.I. Sinauridze, D.A. Kireev, O.P. Plyushch, K.G. Kopylov, E.G. Lopatina, E.L. Saenko, F.I. Ataullakhanov. Mechanisms of action of recombinant activated factor VII in the context of tissue factor concentration and distribution. Blood Coagul. Fibrinolysis, 19 (2008), 743–755. [CrossRef] [PubMed] [Google Scholar]
- K. Leiderman, A.L. Fogelson. The Influence of Hindered Transport on the Development of Platelet Thrombi Under Flow. Bull. Math. Biol., 2012. [Google Scholar]
- O.V. Kim, Z. Xu, E.D. Rosen, M.S. Alber. Fibrin Networks Regulate Protein Transport during Thrombus Development. PLoS Comput. Biol., 9 (2013), e1003095. [CrossRef] [PubMed] [Google Scholar]
- M.M. Kamocka, J. Mu, X. Liu, N. Chen, A. Zollman, B. Sturonas–Brown, K. Dunn, Z. Xu, D.Z. Chen, M.S. Alber, E.D. Rosen. Two-photon intravital imaging of thrombus development. J. Biomed. Opt., 15 (2010), 016020. [Google Scholar]
- T.J. Stalker, E.A. Traxler, J. Wu, K.M. Wannemacher, S.L. Cermignano, R. Voronov, S.L. Diamond, L.F. Brass. Hierarchical organization in the hemostatic response and its relationship to the platelet-signaling network. Blood, 121 (2013), 1875–1885. [CrossRef] [PubMed] [Google Scholar]
- D. Luan, M. Zai, J.D. Varner. Computationally derived points of fragility of a human cascade are consistent with current therapeutic strategies. PLoS computational biology, 3 (2007), e142. [Google Scholar]
- M.H. Flamm, T.V. Colace, M.S. Chatterjee, H. Jing, S. Zhou, D. Jaeger, L.F. Brass, T. Sinno, S.L. Diamond. Multiscale prediction of patient–specific platelet function under flow. Blood, 120 (2012), 190–198. [CrossRef] [PubMed] [Google Scholar]
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