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Systems Biology and Systems Pharmacology of Thrombosis

Published online by Cambridge University Press:  31 July 2014

M.A. Panteleev ,
A.N. Sveshnikova ,
A.V. Belyaev ,
D.Y. Nechipurenko ,
I. Gudich ,
S.I. Obydenny ,
N. Dovlatova ,
S.C. Fox  and
E.L. Holmuhamedov
M.A. Panteleev*
Affiliation:
Federal Research and Clinical Center of Pediatric Hematology Oncology and Immunology, 117198 Moscow, Russia Center for Theoretical Problems of Physicochemical Pharmacology Russian Academy of Sciences, 119991 Moscow, Russia Faculty of Physics, Moscow State University, 119899 Moscow, Russia National Research Center for Hematology, 125167 Moscow, Russia
A.N. Sveshnikova
Affiliation:
Federal Research and Clinical Center of Pediatric Hematology Oncology and Immunology, 117198 Moscow, Russia Center for Theoretical Problems of Physicochemical Pharmacology Russian Academy of Sciences, 119991 Moscow, Russia Faculty of Physics, Moscow State University, 119899 Moscow, Russia
A.V. Belyaev
Affiliation:
Federal Research and Clinical Center of Pediatric Hematology Oncology and Immunology, 117198 Moscow, Russia Center for Theoretical Problems of Physicochemical Pharmacology Russian Academy of Sciences, 119991 Moscow, Russia
D.Y. Nechipurenko
Affiliation:
Faculty of Physics, Moscow State University, 119899 Moscow, Russia
I. Gudich
Affiliation:
Federal Research and Clinical Center of Pediatric Hematology Oncology and Immunology, 117198 Moscow, Russia Center for Theoretical Problems of Physicochemical Pharmacology Russian Academy of Sciences, 119991 Moscow, Russia
S.I. Obydenny
Affiliation:
Federal Research and Clinical Center of Pediatric Hematology Oncology and Immunology, 117198 Moscow, Russia Center for Theoretical Problems of Physicochemical Pharmacology Russian Academy of Sciences, 119991 Moscow, Russia
N. Dovlatova
Affiliation:
Cardiovascular Medicine, University of Nottingham, Nottingham, UK
S.C. Fox
Affiliation:
Cardiovascular Medicine, University of Nottingham, Nottingham, UK
E.L. Holmuhamedov
Affiliation:
Center for Integrative Research on Cardiovascular Aging Aurora University of Wisconsin Medical Group, Aurora Health Care Milwaukee, Wisconsin, United States of America Institute of Theoretical and Experimental Biophysics Russian Academy of Sciences, 142290 Pushchino, Russia
*
⋆ Corresponding author. E-mail: mapanteleev@yandex.ru
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Abstract

Thrombus formation in flowing blood is a complex time- and space-dependent process of cell adhesion and fibrin gel formation controlled by huge intricate networks of biochemical reactions. This combination of complex biochemistry, non-Newtonian hydrodynamics, and transport processes makes thrombosis difficult to understand. That is why numerous attempts to use mathematical modeling for this purpose were undertaken during the last decade. In particular, recent years witnessed something of a transition from the “systems biology” to the “systems pharmacology/systems medicine” stage: computational modeling is being increasingly applied to practical problems such as drug development, investigation of particular events underlying disease, analysis of the mechanism(s) of drug’s action, determining an optimal dosing protocols, etc. Here we review recent advances and challenges in our understanding of thrombus formation.

Keywords

systems biologysystems pharmacologythrombosishemostasisplateletsblood coagulation
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 9 , Issue 6: Blood flows , 2014 , pp. 4 - 16
Copyright
© EDP Sciences, 2014

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References

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