Hamostaseologie 2020; 40(04): 524-535
DOI: 10.1055/a-1213-2117
Review Article

In Silico Hemostasis Modeling and Prediction

Dmitry Y. Nechipurenko*
1   Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia
2   Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia
3   Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
,
Aleksey M. Shibeko*
2   Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia
3   Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
,
Anastasia N. Sveshnikova
1   Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia
2   Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia
3   Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
,
Mikhail A. Panteleev
1   Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia
2   Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia
3   Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
› Author Affiliations

Abstract

Computational physiology, i.e., reproduction of physiological (and, by extension, pathophysiological) processes in silico, could be considered one of the major goals in computational biology. One might use computers to simulate molecular interactions, enzyme kinetics, gene expression, or whole networks of biochemical reactions, but it is (patho)physiological meaning that is usually the meaningful goal of the research even when a single enzyme is its subject. Although exponential rise in the use of computational and mathematical models in the field of hemostasis and thrombosis began in the 1980s (first for blood coagulation, then for platelet adhesion, and finally for platelet signal transduction), the majority of their successful applications are still focused on simulating the elements of the hemostatic system rather than the total (patho)physiological response in situ. Here we discuss the state of the art, the state of the progress toward the efficient “virtual thrombus formation,” and what one can already get from the existing models.

* These authors contributed equally to this work.




Publication History

Received: 14 April 2020

Accepted: 06 July 2020

Article published online:
11 September 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Stuttgart · New York

 
  • References

  • 1 Xu Z, Kamocka M, Alber M, Rosen ED. Computational approaches to studying thrombus development. Arterioscler Thromb Vasc Biol 2011; 31 (03) 500-505
  • 2 Shibeko AM, Panteleev MA. Untangling the complexity of blood coagulation network: use of computational modelling in pharmacology and diagnostics. Brief Bioinform 2016; 17 (03) 429-439
  • 3 Dunster JL, Panteleev MA, Gibbins JM, Sveshnikova AN. Mathematical techniques for understanding platelet regulation and the development of new pharmacological approaches. Methods Mol Biol 2018; 1812: 255-279
  • 4 Belyaev AV, Dunster JL, Gibbins JM, Panteleev MA, Volpert V. Modeling thrombosis in silico: frontiers, challenges, unresolved problems and milestones. Phys Life Rev 2018; 26–27: 57-95
  • 5 Panteleev MA, Andreeva AA, Lobanov AI. Differential drug target selection in blood coagulation: what can we get from computational systems biology models?. Curr Pharm Des 2020; 26 (18) 2109-2115
  • 6 Panteleev MA, Ananyeva NM, Ataullakhanov FI, Saenko EL. Mathematical models of blood coagulation and platelet adhesion: clinical applications. Curr Pharm Des 2007; 13 (14) 1457-1467
  • 7 Ataullakhanov FI, Panteleev MA. Mathematical modeling and computer simulation in blood coagulation. Pathophysiol Haemost Thromb 2005; 34 (2–3): 60-70
  • 8 Nesbitt WS, Mangin P, Salem HH, Jackson SP. The impact of blood rheology on the molecular and cellular events underlying arterial thrombosis. J Mol Med (Berl) 2006; 84 (12) 989-995
  • 9 Swieringa F, Spronk HMH, Heemskerk JWM, van der Meijden PEJ. Integrating platelet and coagulation activation in fibrin clot formation. Res Pract Thromb Haemost 2018; 2 (03) 450-460
  • 10 Panteleev MA, Dashkevich NM, Ataullakhanov FI. Hemostasis and thrombosis beyond biochemistry: roles of geometry, flow and diffusion. Thromb Res 2015; 136 (04) 699-711
  • 11 Kovalenko TA, Panteleev MA, Sveshnikova AN. Substrate delivery mechanism and the role of membrane curvature in factor X activation by extrinsic tenase. J Theor Biol 2017; 435: 125-133
  • 12 Panteleev MA, Saenko EL, Ananyeva NM, Ataullakhanov FI. Kinetics of factor X activation by the membrane-bound complex of factor IXa and factor VIIIa. Biochem J 2004; 381 (Pt 3): 779-794
  • 13 Panteleev MA, Zarnitsina VI, Ataullakhanov FI. Tissue factor pathway inhibitor: a possible mechanism of action. Eur J Biochem 2002; 269 (08) 2016-2031
  • 14 Podoplelova NA, Sveshnikova AN, Kotova YN. et al. Coagulation factors bound to procoagulant platelets concentrate in cap structures to promote clotting. Blood 2016; 128 (13) 1745-1755
  • 15 Podoplelova NA, Sveshnikova AN, Kurasawa JH. et al. Hysteresis-like binding of coagulation factors X/Xa to procoagulant activated platelets and phospholipids results from multistep association and membrane-dependent multimerization. Biochim Biophys Acta 2016; 1858 (06) 1216-1227
  • 16 Terentyeva VA, Sveshnikova AN, Panteleev MA. Kinetics and mechanisms of surface-dependent coagulation factor XII activation. J Theor Biol 2015; 382: 235-243
  • 17 Zakharova NV, Artemenko EO, Podoplelova NA. et al. Platelet surface-associated activation and secretion-mediated inhibition of coagulation factor XII. PLoS One 2015; 10 (02) e0116665
  • 18 Weisel JW, Nagaswami C. Computer modeling of fibrin polymerization kinetics correlated with electron microscope and turbidity observations: clot structure and assembly are kinetically controlled. Biophys J 1992; 63 (01) 111-128
  • 19 Goldsmith HL, Turitto VT. Rheological aspects of thrombosis and haemostasis: basic principles and applications. ICTH-Report--Subcommittee on Rheology of the International Committee on Thrombosis and Haemostasis. Thromb Haemost 1986; 55 (03) 415-435
  • 20 Tokarev AA, Butylin AA, Ermakova EA, Shnol EE, Panasenko GP, Ataullakhanov FI. Finite platelet size could be responsible for platelet margination effect. Biophys J 2011; 101 (08) 1835-1843
  • 21 Czaja B, Gutierrez M, Závodszky G, de Kanter D, Hoekstra A, Eniola-Adefeso O. The influence of red blood cell deformability on hematocrit profiles and platelet margination. PLOS Comput Biol 2020; 16 (03) e1007716
  • 22 Vahidkhah K, Diamond SL, Bagchi P. Platelet dynamics in three-dimensional simulation of whole blood. Biophys J 2014; 106 (11) 2529-2540
  • 23 Tokarev AA, Butylin AA, Ataullakhanov FI. Platelet adhesion from shear blood flow is controlled by near-wall rebounding collisions with erythrocytes. Biophys J 2011; 100 (04) 799-808
  • 24 van Rooij BJM, Závodszky G, Azizi Tarksalooyeh VW, Hoekstra AG. Identifying the start of a platelet aggregate by the shear rate and the cell-depleted layer. J R Soc Interface 2019; 16 (159) 20190148
  • 25 Bark Jr DL, Ku DN. Platelet transport rates and binding kinetics at high shear over a thrombus. Biophys J 2013; 105 (02) 502-511
  • 26 Mody NA, Lomakin O, Doggett TA, Diacovo TG, King MR. Mechanics of transient platelet adhesion to von Willebrand factor under flow. Biophys J 2005; 88 (02) 1432-1443
  • 27 Belyaev AV. Long ligands reinforce biological adhesion under shear flow. Phys Rev E 2018; 97 (4–1): 042407
  • 28 Nesbitt WS, Westein E, Tovar-Lopez FJ. et al. A shear gradient-dependent platelet aggregation mechanism drives thrombus formation. Nat Med 2009; 15 (06) 665-673
  • 29 Receveur N, Nechipurenko D, Knapp Y. et al. Shear rate gradients promote a bi-phasic thrombus formation on weak adhesive proteins, such as fibrinogen in a VWF-dependent manner. Haematologica 2019; (e-pub ahead of print) DOI: 10.3324/haematol.2019.235754.
  • 30 Fu H, Jiang Y, Yang D, Scheiflinger F, Wong WP, Springer TA. Flow-induced elongation of von Willebrand factor precedes tension-dependent activation. Nat Commun 2017; 8 (01) 324
  • 31 Deng W, Wang Y, Druzak SA. et al. A discontinuous autoinhibitory module masks the A1 domain of von Willebrand factor. J Thromb Haemost 2017; 15 (09) 1867-1877
  • 32 Schneider SW, Nuschele S, Wixforth A. et al. Shear-induced unfolding triggers adhesion of von Willebrand factor fibers. Proc Natl Acad Sci U S A 2007; 104 (19) 7899-7903
  • 33 Belyaev AV, Panteleev MA, Ataullakhanov FI. Threshold of microvascular occlusion: injury size defines the thrombosis scenario. Biophys J 2015; 109 (02) 450-456
  • 34 Stalker TJ, Traxler EA, Wu J. et al. Hierarchical organization in the hemostatic response and its relationship to the platelet-signaling network. Blood 2013; 121 (10) 1875-1885
  • 35 Welsh JD, Poventud-Fuentes I, Sampietro S, Diamond SL, Stalker TJ, Brass LF. Hierarchical organization of the hemostatic response to penetrating injuries in the mouse macrovasculature. J Thromb Haemost 2017; 15 (03) 526-537
  • 36 Yazdani A, Li H, Humphrey JD, Karniadakis GE. A general shear-dependent model for thrombus formation. PLOS Comput Biol 2017; 13 (01) e1005291
  • 37 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 2013; 337: 30-41
  • 38 Babushkina ES, Bessonov NM, Ataullakhanov FI, Panteleev MA. Continuous modeling of arterial platelet thrombus formation using a spatial adsorption equation. PLoS One 2015; 10 (10) e0141068
  • 39 Tomaiuolo M, Stalker TJ, Welsh JD, Diamond SL, Sinno T, Brass LF. A systems approach to hemostasis: 2. Computational analysis of molecular transport in the thrombus microenvironment. Blood 2014; 124 (11) 1816-1823
  • 40 Mirramezani M, Herbig BA, Stalker TJ. et al. Platelet packing density is an independent regulator of the hemostatic response to injury. J Thromb Haemost 2018; 16 (05) 973-983
  • 41 Xu S, Xu Z, Kim OV, Litvinov RI, Weisel JW, Alber M. Model predictions of deformation, embolization and permeability of partially obstructive blood clots under variable shear flow. J R Soc Interface 2017; 14 (136) 14
  • 42 Govindarajan V, Zhu S, Li R. et al. Impact of tissue factor localization on blood clot structure and resistance under venous shear. Biophys J 2018; 114 (04) 978-991
  • 43 Nechipurenko DY, Receveur N, Yakimenko AO. et al. Clot contraction drives the translocation of procoagulant platelets to thrombus surface. Arterioscler Thromb Vasc Biol 2019; 39 (01) 37-47
  • 44 Trifanov P, Kaneva V, Strijhak S. et al. Developing quasi-steady model for studying hemostatic response using supercomputer technologies. Supercomp Front Innovat 2018; 5: 67-72
  • 45 Tosenberger A, Ataullakhanov F, Bessonov N, Panteleev M, Tokarev A, Volpert V. Modelling of platelet-fibrin clot formation in flow with a DPD-PDE method. J Math Biol 2016; 72 (03) 649-681
  • 46 Leiderman K, Fogelson AL. The influence of hindered transport on the development of platelet thrombi under flow. Bull Math Biol 2013; 75 (08) 1255-1283
  • 47 Khanin MA, Semenov VV. A mathematical model of the kinetics of blood coagulation. J Theor Biol 1989; 136 (02) 127-134
  • 48 Sorensen EN, Burgreen GW, Wagner WR, Antaki JF. Computational simulation of platelet deposition and activation: I. Model development and properties. Ann Biomed Eng 1999; 27 (04) 436-448
  • 49 Purvis JE, Chatterjee MS, Brass LF, Diamond SL. A molecular signaling model of platelet phosphoinositide and calcium regulation during homeostasis and P2Y1 activation. Blood 2008; 112 (10) 4069-4079
  • 50 Sveshnikova AN, Ataullakhanov FI, Panteleev MA. Compartmentalized calcium signaling triggers subpopulation formation upon platelet activation through PAR1. Mol Biosyst 2015; 11 (04) 1052-1060
  • 51 Shepelyuk TO, Panteleev MA, Sveshnikova AN. Computational modeling of quiescent platelet energy metabolism in the context of whole-body glucose turnover. Math Model Nat Phenom 2016; 11: 91-101
  • 52 Obydennyi SI, Artemenko EO, Sveshnikova AN. et al. Mechanisms of increased mitochondria-dependent necrosis in Wiskott-Aldrich syndrome platelets. Haematologica 2020; 105 (04) 1095-1106
  • 53 Shakhidzhanov SS, Shaturny VI, Panteleev MA, Sveshnikova AN. Modulation and pre-amplification of PAR1 signaling by ADP acting via the P2Y12 receptor during platelet subpopulation formation. Biochim Biophys Acta 2015; 1850 (12) 2518-2529
  • 54 Martyanov AA, Balabin FA, Dunster JL, Panteleev MA, Gibbins JM, Sveshnikova AN. Control of platelet CLEC-2-mediated activation by receptor clustering and tyrosine kinase signaling. Biophys J 2020; 118 (11) 2641-2655
  • 55 Sveshnikova AN, Balatskiy AV, Demianova AS. et al. Systems biology insights into the meaning of the platelet's dual-receptor thrombin signaling. J Thromb Haemost 2016; 14 (10) 2045-2057
  • 56 Bergmann FT, Hoops S, Klahn B. et al. COPASI and its applications in biotechnology. J Biotechnol 2017; 261: 215-220
  • 57 Burkhart JM, Vaudel M, Gambaryan S. et al. The first comprehensive and quantitative analysis of human platelet protein composition allows the comparative analysis of structural and functional pathways. Blood 2012; 120 (15) e73-e82
  • 58 Makhoul S, Walter E, Pagel O. et al. Effects of the NO/soluble guanylate cyclase/cGMP system on the functions of human platelets. Nitric Oxide 2018; 76: 71-80
  • 59 Dunster JL, Mazet F, Fry MJ, Gibbins JM, Tindall MJ. Regulation of early steps of GPVI signal transduction by phosphatases: a systems biology approach. PLOS Comput Biol 2015; 11 (11) e1004589
  • 60 Dunster JL, Unsworth AJ, Bye AP. et al. Interspecies differences in protein expression do not impact the spatiotemporal regulation of glycoprotein VI mediated activation. J Thromb Haemost 2020; 18 (02) 485-496
  • 61 Martyanov AA, Morozova DS, Sorokina MA. et al. Heterogeneity of integrin αIIbβ3 function in pediatric immune thrombocytopenia revealed by continuous flow cytometry analysis. Int J Mol Sci 2020; 21 (09) 21
  • 62 Obydennyy SI, Sveshnikova AN, Ataullakhanov FI, Panteleev MA. Dynamics of calcium spiking, mitochondrial collapse and phosphatidylserine exposure in platelet subpopulations during activation. J Thromb Haemost 2016; 14 (09) 1867-1881
  • 63 Kotova YN, Ataullakhanov FI, Panteleev MA. Formation of coated platelets is regulated by the dense granule secretion of adenosine 5'diphosphate acting via the P2Y12 receptor. J Thromb Haemost 2008; 6 (09) 1603-1605
  • 64 Flamm MH, Colace TV, Chatterjee MS. et al. Multiscale prediction of patient-specific platelet function under flow. Blood 2012; 120 (01) 190-198
  • 65 Levine SN. Enzyme amplifier kinetics. Science 1966; 152 (3722): 651-653
  • 66 Ratto N, Tokarev A, Chelle P, Tardy-Poncet B, Volpert V. Clustering of thrombin generation test data using a reduced mathematical model of blood coagulation. Acta Biotheor 2020; 68 (01) 21-43
  • 67 Zavyalova EG, Ustinov NB, Kopylov AM. Exploring the efficiency of thrombin inhibitors with a quantitative model of the coagulation cascade. FEBS Lett 2020; 594 (06) 995-1004
  • 68 Link KG, Stobb MT, Sorrells MG. et al. A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A. J Thromb Haemost 2020; 18 (02) 306-317
  • 69 Siekmann I, Bjelosevic S, Landman K, Monagle P, Ignjatovic V, Crampin EJ. Mathematical modelling indicates that lower activity of the haemostatic system in neonates is primarily due to lower prothrombin concentration. Sci Rep 2019; 9 (01) 3936
  • 70 Mitrophanov AY, Szlam F, Sniecinski RM, Levy JH, Reifman J. Controlled multifactorial coagulopathy: effects of dilution, hypothermia, and acidosis on thrombin generation in vitro. Anesth Analg 2020; 130 (04) 1063-1076
  • 71 Lo K, Denney WS, Diamond SL. Stochastic modeling of blood coagulation initiation. Pathophysiol Haemost Thromb 2005; 34 (2–3): 80-90
  • 72 Balandina AN, Shibeko AM, Kireev DA. et al. Positive feedback loops for factor V and factor VII activation supply sensitivity to local surface tissue factor density during blood coagulation. Biophys J 2011; 101 (08) 1816-1824
  • 73 Dashkevich NM, Ovanesov MV, Balandina AN. et al. Thrombin activity propagates in space during blood coagulation as an excitation wave. Biophys J 2012; 103 (10) 2233-2240
  • 74 Diamond SL, Anand S. Inner clot diffusion and permeation during fibrinolysis. Biophys J 1993; 65 (06) 2622-2643
  • 75 Panteleev MA, Ovanesov MV, Kireev DA. et al. Spatial propagation and localization of blood coagulation are regulated by intrinsic and protein C pathways, respectively. Biophys J 2006; 90 (05) 1489-1500
  • 76 Parunov LA, Fadeeva OA, Balandina AN. et al. Improvement of spatial fibrin formation by the anti-TFPI aptamer BAX499: changing clot size by targeting extrinsic pathway initiation. J Thromb Haemost 2011; 9 (09) 1825-1834
  • 77 Shibeko AM, Lobanova ES, Panteleev MA, Ataullakhanov FI. Blood flow controls coagulation onset via the positive feedback of factor VII activation by factor Xa. BMC Syst Biol 2010; 4: 5
  • 78 Zhalyalov AS, Panteleev MA, Gracheva MA, Ataullakhanov FI, Shibeko AM. Co-ordinated spatial propagation of blood plasma clotting and fibrinolytic fronts. PLoS One 2017; 12 (07) e0180668
  • 79 Anand M, Rajagopal K, Rajagopal KR. 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. J Theor Biol 2008; 253 (04) 725-738
  • 80 Kuharsky AL, Fogelson AL. Surface-mediated control of blood coagulation: the role of binding site densities and platelet deposition. Biophys J 2001; 80 (03) 1050-1074
  • 81 Ataullakhanov FI, Guria GT, Sarbash VI, Volkova RI. Spatiotemporal dynamics of clotting and pattern formation in human blood. Biochim Biophys Acta 1998; 1425 (03) 453-468
  • 82 Hockin MF, Jones KC, Everse SJ, Mann KG. A model for the stoichiometric regulation of blood coagulation. J Biol Chem 2002; 277 (21) 18322-18333
  • 83 Beltrami E, Jesty J. Mathematical analysis of activation thresholds in enzyme-catalyzed positive feedbacks: application to the feedbacks of blood coagulation. Proc Natl Acad Sci U S A 1995; 92 (19) 8744-8748
  • 84 Beltrami E, Jesty J. The role of membrane patch size and flow in regulating a proteolytic feedback threshold on a membrane: possible application in blood coagulation. Math Biosci 2001; 172 (01) 1-13
  • 85 Kastrup CJ, Shen F, Runyon MK, Ismagilov RF. Characterization of the threshold response of initiation of blood clotting to stimulus patch size. Biophys J 2007; 93 (08) 2969-2977
  • 86 Panteleev MA, Balandina AN, Lipets EN, Ovanesov MV, Ataullakhanov FI. Task-oriented modular decomposition of biological networks: trigger mechanism in blood coagulation. Biophys J 2010; 98 (09) 1751-1761
  • 87 Diamandis P, Wildenhain J, Clarke ID. et al. Chemical genetics reveals a complex functional ground state of neural stem cells. Nat Chem Biol 2007; 3 (05) 268-273
  • 88 Kuprash AD, Shibeko AM, Vijay R. et al. Sensitivity and robustness of spatially dependent thrombin generation and fibrin clot propagation. Biophys J 2018; 115 (12) 2461-2473
  • 89 Mitrophanov AY, Wolberg AS, Reifman J. Kinetic model facilitates analysis of fibrin generation and its modulation by clotting factors: implications for hemostasis-enhancing therapies. Mol Biosyst 2014; 10 (09) 2347-2357
  • 90 Mitrophanov AY, Szlam F, Sniecinski RM, Levy JH, Reifman J. A step toward balance: thrombin generation improvement via procoagulant factor and antithrombin supplementation. Anesth Analg 2016; 123 (03) 535-546
  • 91 Sinauridze EI, Gorbatenko AS, Seregina EA, Lipets EN, Ataullakhanov FI. Moderate plasma dilution using artificial plasma expanders shifts the haemostatic balance to hypercoagulation. Sci Rep 2017; 7 (01) 843
  • 92 Brummel-Ziedins KE, Gissel M, Neuhaus J. et al; INSIGHT SMART, ESPRIT Study Groups. In silico thrombin generation: Plasma composition imbalance and mortality in human immunodeficiency virus. Res Pract Thromb Haemost 2018; 2 (04) 708-717
  • 93 Gupta S, Bravo MC, Heiman M. et al. Mathematical model of thrombin generation and bleeding phenotype in Amish carriers of Factor IX:C deficiency vs. controls. Thromb Res 2019; 182: 43-50
  • 94 Brummel-Ziedins KE, Whelihan MF, Gissel M, Mann KG, Rivard GE. Thrombin generation and bleeding in haemophilia A. Haemophilia 2009; 15 (05) 1118-1125
  • 95 Brummel-Ziedins KE, Vossen CY, Butenas S, Mann KG, Rosendaal FR. Thrombin generation profiles in deep venous thrombosis. J Thromb Haemost 2005; 3 (11) 2497-2505
  • 96 Brummel-Ziedins K, Vossen CY, Rosendaal FR, Umezaki K, Mann KG. The plasma hemostatic proteome: thrombin generation in healthy individuals. J Thromb Haemost 2005; 3 (07) 1472-1481
  • 97 Chelle P, Morin C, Montmartin A, Piot M, Cournil M, Tardy-Poncet B. Evaluation and calibration of in silico models of thrombin generation using experimental data from healthy and haemophilic subjects. Bull Math Biol 2018; 80 (08) 1989-2025
  • 98 Duarte RCF, Ferreira CN, Rios DRA, Reis HJD, Carvalho MDG. Thrombin generation assays for global evaluation of the hemostatic system: perspectives and limitations. Rev Bras Hematol Hemoter 2017; 39 (03) 259-265
  • 99 van Veen JJ, Gatt A, Makris M. Thrombin generation testing in routine clinical practice: are we there yet?. Br J Haematol 2008; 142 (06) 889-903
  • 100 Liang Y, Woodle SA, Shibeko AM, Lee TK, Ovanesov MV. Correction of microplate location effects improves performance of the thrombin generation test. Thromb J 2013; 11 (01) 12
  • 101 Bannish BE, Keener JP, Fogelson AL. Modelling fibrinolysis: a 3D stochastic multiscale model. Math Med Biol 2014; 31 (01) 17-44
  • 102 Bannish BE, Chernysh IN, Keener JP, Fogelson AL, Weisel JW. Molecular and physical mechanisms of fibrinolysis and thrombolysis from mathematical modeling and experiments. Sci Rep 2017; 7 (01) 6914
  • 103 Shibeko AM, Woodle SA, Mahmood I, Jain N, Ovanesov MV. Predicting dosing advantages of factor VIIa variants with altered tissue factor-dependent and lipid-dependent activities. J Thromb Haemost 2014; 12 (08) 1302-1312
  • 104 Piebalgs A, Gu B, Roi D, Lobotesis K, Thom S, Xu XY. Computational simulations of thrombolytic therapy in acute ischaemic stroke. Sci Rep 2018; 8 (01) 15810