Decoding thrombosis through code: a review of computational models.

Noelia Grande Gutiérrez, Debanjan Mukherjee, David Bark
Author Information
  1. Noelia Grande Gutiérrez: Carnegie Mellon University, Department of Mechanical Engineering Pittsburgh, PA, USA. Electronic address: https://twitter.com/ngrandeg.
  2. Debanjan Mukherjee: University of Colorado Boulder, Paul M. Rady Department of Mechanical Engineering Boulder, CO, USA. Electronic address: https://twitter.com/debanjanmukh.
  3. David Bark: Washington University in St Louis, Department of Pediatrics, Division of Hematology and Oncology St Louis, MO, USA; Washington University in St Louis, Department of Biomedical Engineering St Louis, MO, USA. Electronic address: bark@wustl.edu.

Abstract

From the molecular level up to a blood vessel, Thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of Thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based Thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in Thrombosis are poised to bring about significant knowledge growth in the years to come.

Keywords

References

  1. Math Med Biol. 2009 Dec;26(4):323-36 [PMID: 19451209]
  2. Cardiovasc Eng Technol. 2021 Dec;12(6):576-588 [PMID: 34859378]
  3. Biophys J. 2013 Jul 16;105(2):502-11 [PMID: 23870271]
  4. Sci Rep. 2016 Dec 01;6:38025 [PMID: 27905492]
  5. Blood. 2014 Sep 11;124(11):1816-23 [PMID: 24951425]
  6. J Thorac Cardiovasc Surg. 2009 Feb;137(2):394-403.e2 [PMID: 19185159]
  7. Biophys J. 1992 Jul;63(1):111-28 [PMID: 1420861]
  8. Semin Thromb Hemost. 2021 Mar;47(2):129-138 [PMID: 33657623]
  9. Philos Trans A Math Phys Eng Sci. 2014 Aug 6;372(2021): [PMID: 24982253]
  10. Semin Thorac Cardiovasc Surg. 2022 Summer;34(2):521-532 [PMID: 33711465]
  11. Blood Rev. 2016 Sep;30(5):357-68 [PMID: 27133256]
  12. Annu Rev Fluid Mech. 2015 Jan 1;47:377-403 [PMID: 26236058]
  13. J R Soc Interface. 2017 Nov;14(136): [PMID: 29142014]
  14. J Hematol Oncol. 2018 Oct 11;11(1):125 [PMID: 30305116]
  15. Math Med Biol. 2011 Mar;28(1):47-84 [PMID: 20439306]
  16. Ann Biomed Eng. 2021 Sep;49(9):2646-2658 [PMID: 34401970]
  17. R Soc Open Sci. 2020 Dec 23;7(12):201838 [PMID: 33489295]
  18. Thromb Res. 2014 May;133 Suppl 1:S12-4 [PMID: 24759131]
  19. J Biomech. 2021 Jun 9;122:110398 [PMID: 33933859]
  20. Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jun;83(6 Pt 1):061924 [PMID: 21797420]
  21. J R Soc Interface. 2021 Feb;18(175):20200834 [PMID: 33530862]
  22. Annu Rev Biomed Eng. 2009;11:109-34 [PMID: 19400706]
  23. Biotechnol Bioeng. 2012 Oct;109(10):2642-50 [PMID: 22539078]
  24. Cell Mol Bioeng. 2014 Dec 1;7(4):552-574 [PMID: 25530818]
  25. J R Soc Interface. 2008 Jul 6;5(24):705-22 [PMID: 17925274]
  26. Biophys J. 2010 May 19;98(9):L35-7 [PMID: 20441731]
  27. J Clin Invest. 2018 Aug 1;128(8):3356-3368 [PMID: 29723163]
  28. Ann Biomed Eng. 2020 Oct;48(10):2400-2411 [PMID: 32415483]
  29. Biophys J. 2001 Mar;80(3):1050-74 [PMID: 11222273]
  30. Math Med Biol. 2007 Mar;24(1):111-30 [PMID: 17018571]
  31. Ann Biomed Eng. 2005 Jun;33(6):780-97 [PMID: 16078618]
  32. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285 [PMID: 34756653]
  33. Curr Opin Biomed Eng. 2021 Sep;19: [PMID: 34693101]
  34. J Biomech. 2021 May 7;120:110349 [PMID: 33711601]
  35. J Mol Model. 2018 Apr 5;24(5):109 [PMID: 29623504]
  36. Biophys J. 2016 Apr 26;110(8):1869-1885 [PMID: 27119646]
  37. PLoS Comput Biol. 2021 Sep 7;17(9):e1009331 [PMID: 34491991]
  38. Biophys J. 2014 Jun 3;106(11):2529-40 [PMID: 24896133]
  39. Sci Rep. 2017 Aug 7;7(1):6914 [PMID: 28785035]
  40. Biophys J. 1993 Dec;65(6):2622-43 [PMID: 8312497]
  41. Curr Opin Biomed Eng. 2022 Jun;22: [PMID: 35386550]
  42. Int J Cardiol. 2019 Apr 15;281:15-21 [PMID: 30728104]
  43. J Trauma Acute Care Surg. 2019 Feb;86(2):250-259 [PMID: 30531331]
  44. Biophys J. 2013 Mar 5;104(5):1181-90 [PMID: 23473501]
  45. Math Med Biol. 2017 Dec 11;34(4):523-546 [PMID: 27672182]
  46. Ann Thorac Surg. 2018 Jul;106(1):70-78 [PMID: 29501642]
  47. Ann Biomed Eng. 2010 Jan;38(1):88-99 [PMID: 19898936]
  48. J R Soc Interface. 2021 Dec;18(185):20210583 [PMID: 34905967]
  49. PLoS Comput Biol. 2020 Apr 28;16(4):e1007709 [PMID: 32343724]
  50. Nat Med. 2009 Jun;15(6):665-73 [PMID: 19465929]
  51. Bull Math Biol. 2013 Aug;75(8):1255-83 [PMID: 23097125]
  52. Biophys J. 2013 Apr 16;104(8):1764-72 [PMID: 23601323]
  53. Math Med Biol. 2018 Jun 13;35(2):225-256 [PMID: 28339733]
  54. Sci Transl Med. 2017 Sep 27;9(409): [PMID: 28954929]
  55. Biomech Model Mechanobiol. 2021 Jun;20(3):1013-1030 [PMID: 33782796]
  56. Cell. 2017 Nov 30;171(6):1368-1382.e23 [PMID: 29195076]
  57. J Biol Chem. 1994 Sep 16;269(37):23367-73 [PMID: 8083242]
  58. Front Physiol. 2014 Nov 07;5:417 [PMID: 25426077]
  59. Math Biosci. 2001 Jul;172(1):1-13 [PMID: 11472773]
  60. Brief Bioinform. 2016 May;17(3):429-39 [PMID: 26116831]
  61. J Biomech. 2021 Oct 11;127:110692 [PMID: 34479090]
  62. PLoS Comput Biol. 2013;9(6):e1003095 [PMID: 23785270]
  63. Res Pract Thromb Haemost. 2023 Jan 10;7(1):100037 [PMID: 36846647]
  64. Ann Biomed Eng. 1999 Jul-Aug;27(4):436-48 [PMID: 10468228]
  65. Ann Biomed Eng. 2018 Aug;46(8):1128-1145 [PMID: 29691787]
  66. Biophys J. 1995 Sep;69(3):803-9 [PMID: 8519981]
  67. Biopharm Drug Dispos. 1998 Mar;19(2):131-40 [PMID: 9533114]
  68. Postepy Kardiol Interwencyjnej. 2022 Sep;18(3):296-299 [PMID: 36751287]
  69. Langmuir. 2007 May 22;23(11):6321-8 [PMID: 17417890]
  70. Sci Rep. 2018 Oct 25;8(1):15810 [PMID: 30361673]
  71. Biomech Model Mechanobiol. 2020 Apr;19(2):761-778 [PMID: 31686306]
  72. Ann Biomed Eng. 2002 Apr;30(4):483-97 [PMID: 12086000]
  73. Biophys J. 2008 Sep;95(5):2556-74 [PMID: 18515386]
  74. Cell Mol Bioeng. 2019 Aug;12(4):327-343 [PMID: 31662802]
  75. Blood. 2012 Jul 5;120(1):190-8 [PMID: 22517902]
  76. PLoS Comput Biol. 2022 Jan 28;18(1):e1009850 [PMID: 35089923]
  77. Artif Organs. 2015 Jul;39(7):576-83 [PMID: 25808300]
  78. Blood Adv. 2022 Aug 23;6(16):4834-4846 [PMID: 35728058]
  79. J Thorac Cardiovasc Surg. 2002 Sep;124(3):561-74 [PMID: 12202873]
  80. Sci Rep. 2018 Feb 6;8(1):2515 [PMID: 29410467]
  81. Pharmaceutics. 2019 Mar 07;11(3): [PMID: 30866489]
  82. Biomech Model Mechanobiol. 2018 Jun;17(3):645-663 [PMID: 29181799]
  83. J Biol Chem. 2002 May 24;277(21):18322-33 [PMID: 11893748]
  84. Blood. 2015 Jul 9;126(2):242-6 [PMID: 25979951]
  85. Soft Matter. 2016 May 11;12(19):4339-51 [PMID: 27087267]
  86. Arterioscler Thromb Vasc Biol. 2021 Jan;41(1):79-86 [PMID: 33115272]
  87. J Biomech Eng. 2022 Apr 1;144(4): [PMID: 34529037]
  88. Arteriosclerosis. 1988 Nov-Dec;8(6):819-24 [PMID: 3196226]
  89. Nat Biotechnol. 2010 Jul;28(7):727-32 [PMID: 20562863]
  90. Pathophysiol Haemost Thromb. 2005;34(2-3):91-108 [PMID: 16432311]
  91. J Biomech. 2021 Oct 11;127:110693 [PMID: 34450517]
  92. Biomech Model Mechanobiol. 2021 Apr;20(2):701-715 [PMID: 33438148]
  93. Front Physiol. 2013 Aug 26;4:229 [PMID: 23986721]
  94. J R Soc Interface. 2016 Nov;13(124): [PMID: 27807275]

Grants

  1. R01 HL164424/NHLBI NIH HHS
  2. R21 EB029736/NIBIB NIH HHS
  3. R21 EB034579/NIBIB NIH HHS

MeSH Term

Humans
Thrombosis
Inflammation
Blood Platelets
Hemostasis
Computer Simulation

Word Cloud

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