1932

Abstract

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome Project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science.

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2022-08-10
2024-05-25
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Literature Cited

  1. 1.
    Regev A, Teichmann SA, Lander ES, Amit I, Benoist C et al. 2017. The Human Cell Atlas. eLife 6:e27041
    [Google Scholar]
  2. 2.
    Power RM, Huisken J. 2017. A guide to light-sheet fluorescence microscopy for multiscale imaging. Nat. Methods 14:360–73
    [Google Scholar]
  3. 3.
    Hoffman DP, Shtengel G, Xu CS, Campbell KR, Freeman M et al. 2020. Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells. Science 367:eaaz5357
    [Google Scholar]
  4. 4.
    Xu CS, Pang S, Shtengel G, Müller A, Ritter AT et al. 2021. An open-access volume electron microscopy atlas of whole cells and tissues. Nature 599:147–51
    [Google Scholar]
  5. 5.
    Hériché J-K, Alexander S, Ellenberg J 2019. Integrating imaging and omics: computational methods and challenges. Annu. Rev. Biomed. Data Sci. 2:175–97
    [Google Scholar]
  6. 6.
    Babtie AC, Chan TE, Stumpf MPH. 2017. Learning regulatory models for cell development from single cell transcriptomic data. Curr. Opin. Syst. Biol. 5:72–81
    [Google Scholar]
  7. 7.
    Hunter PJ, Borg TK. 2003. Integration from proteins to organs: the Physiome Project. Nat. Rev. Mol. Cell Biol. 4:237–43
    [Google Scholar]
  8. 8.
    Niederer SA, Lumens J, Trayanova NA. 2019. Computational models in cardiology. Nat. Rev. Cardiol. 16:100–11
    [Google Scholar]
  9. 9.
    Fernandez J, Zhang J, Shim V, Munro JT, Sartori M et al. 2018. Musculoskeletal modelling and the physiome project. Multiscale Mechanobiology of Bone Remodeling and Adaptation P Pivonka 123–74 Cham, Switz: Springer
    [Google Scholar]
  10. 10.
    Lin C-L, Tawhai MH, Hoffman EA. 2013. Multiscale image-based modeling and simulation of gas flow and particle transport in the human lungs. WIREs Syst. Biol. Med. 5:643–55
    [Google Scholar]
  11. 11.
    Du P, Paskaranandavadivel N, Angeli TR, Cheng LK, O'Grady G. 2016. The virtual intestine: in silico modeling of small intestinal electrophysiology and motility and the applications. WIREs Syst. Biol. Med. 8:69–85
    [Google Scholar]
  12. 12.
    Clark AR, Kruger JA. 2017. Mathematical modeling of the female reproductive system: from oocyte to delivery. WIREs Syst. Biol. Med. 9:e1353
    [Google Scholar]
  13. 13.
    Miao Z, Humphreys BD, McMahon AP, Kim J. 2021. Multi-omics integration in the age of million single-cell data. Nat. Rev. Nephrol. 17:710–24
    [Google Scholar]
  14. 14.
    Alexandrov T. 2020. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu. Rev. Biomed. Data Sci. 3:61–87
    [Google Scholar]
  15. 15.
    Nemes P. 2021. Mass spectrometry comes of age for subcellular organelles. Nat. Methods 18:1157–58
    [Google Scholar]
  16. 16.
    Lewis SM, Asselin-Labat ML, Nguyen Q, Berthelet J, Tan X et al. 2021. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods 18:997–1012
    [Google Scholar]
  17. 17.
    Wan Y, McDole K, Keller PJ. 2019. Light-sheet microscopy and its potential for understanding developmental processes. Annu. Rev. Cell Dev. Biol. 35:655–81
    [Google Scholar]
  18. 18.
    Valm AM, Cohen S, Legant WR, Melunis J, Hershberg U et al. 2017. Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546:162–67
    [Google Scholar]
  19. 19.
    Saminathan A, Devany J, Veetil AT, Suresh B, Pillai KS et al. 2021. A DNA-based voltmeter for organelles. Nat. Nanotechnol. 16:96–103
    [Google Scholar]
  20. 20.
    Liu T-L, Upadhyayula S, Milkie DE, Singh V, Wang K et al. 2018. Observing the cell in its native state: imaging subcellular dynamics in multicellular organisms. Science 360:eaaq1392
    [Google Scholar]
  21. 21.
    Chen F, Tillberg PW, Boyden ES. 2015. Expansion microscopy. Science 347:543–48
    [Google Scholar]
  22. 22.
    Gao R, Asano SM, Upadhyayula S, Pisarev I, Milkie DE et al. 2019. Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution. Science 363:eaau8302
    [Google Scholar]
  23. 23.
    Zwettler FU, Reinhard S, Gambarotto D, Bell TDM, Hamel V et al. 2020. Molecular resolution imaging by post-labeling expansion single-molecule localization microscopy (Ex-SMLM). Nat. Commun. 11: 3388.
    [Google Scholar]
  24. 24.
    Darling EM, Di Carlo D. 2015. High-throughput assessment of cellular mechanical properties. Annu. Rev. Biomed. Eng. 17:35–62
    [Google Scholar]
  25. 25.
    Davidson PM, Fedorchak GR, Mondesert-Deveraux S, Bell ES, Isermann P et al. 2019. High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells. Lab. Chip 19:3652–63
    [Google Scholar]
  26. 26.
    Namvar A, Blanch AJ, Dixon MW, Carmo OMS, Liu B et al. 2021. Surface area-to-volume ratio, not cellular viscoelasticity, is the major determinant of red blood cell traversal through small channels. Cell Microbiol 23:e13270
    [Google Scholar]
  27. 27.
    Pushkarsky I, Tseng P, Black D, France B, Warfe L et al. 2018. Elastomeric sensor surfaces for high-throughput single-cell force cytometry. Nat. Biomed. Eng. 2:124–37
    [Google Scholar]
  28. 28.
    Lin J, Kim D, Tse HT, Tseng P, Peng L et al. 2017. High-throughput physical phenotyping of cell differentiation. Microsyst. Nanoeng. 3:17013
    [Google Scholar]
  29. 29.
    Guo F, Li P, French JB, Mao Z, Zhao H et al. 2015. Controlling cell–cell interactions using surface acoustic waves. PNAS 112:43–48
    [Google Scholar]
  30. 30.
    Sorkin R, Bergamaschi G, Kamsma D, Brand G, Dekel E et al. 2018. Probing cellular mechanics with acoustic force spectroscopy. Mol. Biol. Cell 29:2005–11
    [Google Scholar]
  31. 31.
    Schlichthaerle T, Lindner C, Jungmann R. 2021. Super-resolved visualization of single DNA-based tension sensors in cell adhesion. Nat. Commun. 12:2510
    [Google Scholar]
  32. 32.
    Wang X, Ha T. 2013. Defining single molecular forces required to activate integrin and notch signaling. Science 340:991–94
    [Google Scholar]
  33. 33.
    Zhang Y, Ge C, Zhu C, Salaita K. 2014. DNA-based digital tension probes reveal integrin forces during early cell adhesion. Nat. Commun. 5:5167
    [Google Scholar]
  34. 34.
    Meng F, Sachs F. 2012. Orientation-based FRET sensor for real-time imaging of cellular forces. J. Cell Sci. 125:743–50
    [Google Scholar]
  35. 35.
    Meng F, Suchyna TM, Sachs F. 2008. A fluorescence energy transfer-based mechanical stress sensor for specific proteins in situ. FEBS J 275:3072–87
    [Google Scholar]
  36. 36.
    LaCroix AS, Lynch AD, Berginski ME, Hoffman BD. 2018. Tunable molecular tension sensors reveal extension-based control of vinculin loading. eLife 7:e33927
    [Google Scholar]
  37. 37.
    Colom A, Derivery E, Soleimanpour S, Tomba C, Molin MD et al. 2018. A fluorescent membrane tension probe. Nat. Chem. 10:1118–25
    [Google Scholar]
  38. 38.
    Muddana HS, Gullapalli RR, Manias E, Butler PJ. 2011. Atomistic simulation of lipid and DiI dynamics in membrane bilayers under tension. Phys. Chem. Chem. Phys. 13:1368–78
    [Google Scholar]
  39. 39.
    Herrera-Perez RM, Cupo C, Allan C, Lin A, Kasza KE 2021. Using optogenetics to link myosin patterns to contractile cell behaviors during convergent extension. Biophys. J. 120:4214–29
    [Google Scholar]
  40. 40.
    Herrera-Perez RM, Cupo C, Allan C, Dagle AB, Kasza KE. 2022. Optogenetic dissection of actomyosin-dependent mechanics underlying tissue fluidity. bioRxiv 2021.11.07.467642. https://doi.org/10.1101/2021.11.07.467642
    [Crossref]
  41. 41.
    Tang W-C, Liu Y-T, Yeh C-H, Lin Y-L, Lin Y-C et al. 2022. Optogenetic manipulation of cell migration with high spatiotemporal resolution using lattice lightsheet microscopy. bioRxiv 2022.01.02.474058. https://doi.org/10.1101/2022.01.02.474058
    [Crossref]
  42. 42.
    Leyden F, Uthishtran S, Moorthi UK, York HM, Patil A et al. 2021. Rac1 activation can generate untemplated, lamellar membrane ruffles. BMC Biol 19:72
    [Google Scholar]
  43. 43.
    Wu YI, Wang X, He L, Montell D, Hahn KM. 2011. Spatiotemporal control of small GTPases with light using the LOV domain. Methods Enzymol 497:393–407
    [Google Scholar]
  44. 44.
    Arumugam S, Kaur A. 2017. The lipids of the early endosomes: making multimodality work. Chembiochem 18:1053–60
    [Google Scholar]
  45. 45.
    Arumugam S, Chwastek G, Schwille P. 2011. Protein-membrane interactions: the virtue of minimal systems in systems biology. WIREs Syst. Biol. Med. 3:269–80
    [Google Scholar]
  46. 46.
    Hansen SD, Huang WYC, Lee YK, Bieling P, Christensen SM, Groves JT. 2019. Stochastic geometry sensing and polarization in a lipid kinase–phosphatase competitive reaction. PNAS 116:15013–22
    [Google Scholar]
  47. 47.
    Tsai D, Sawyer D, Bradd A, Yuste R, Shepard KL. 2017. A very large-scale microelectrode array for cellular-resolution electrophysiology. Nat. Commun. 8:1802
    [Google Scholar]
  48. 48.
    Karunarathne WK, O'Neill PR, Gautam N. 2015. Subcellular optogenetics—controlling signaling and single-cell behavior. J. Cell Sci. 128:15–25
    [Google Scholar]
  49. 49.
    Perry SW, Norman JP, Barbieri J, Brown EB, Gelbard HA. 2011. Mitochondrial membrane potential probes and the proton gradient: a practical usage guide. Biotechniques 50:98–115
    [Google Scholar]
  50. 50.
    Bernardo BC, Weeks KL, Pretorius L, McMullen JR. 2010. Molecular distinction between physiological and pathological cardiac hypertrophy: experimental findings and therapeutic strategies. Pharmacol. Ther. 128:191–227
    [Google Scholar]
  51. 51.
    Zarain-Herzberg A, Fragoso-Medina J, Estrada-Avilés R. 2011. Calcium-regulated transcriptional pathways in the normal and pathologic heart. IUBMB Life 63:847–55
    [Google Scholar]
  52. 52.
    Wilkins BJ, Dai Y-S, Bueno OF, Parsons SA, Xu J et al. 2004. Calcineurin/NFAT coupling participates in pathological, but not physiological, cardiac hypertrophy. Circ. Res. 94:110–18
    [Google Scholar]
  53. 53.
    Zhang CL, McKinsey TA, Chang S, Antos CL, Hill JA, Olson EN. 2002. Class II histone deacetylases act as signal-responsive repressors of cardiac hypertrophy. Cell 110:479–88
    [Google Scholar]
  54. 54.
    Bers DM. 2002. Cardiac excitation–contraction coupling. Nature 415:198–205
    [Google Scholar]
  55. 55.
    Houser SR, Molkentin JD. 2008. Does contractile Ca2+ control calcineurin-NFAT signaling and pathological hypertrophy in cardiac myocytes?. Sci. Signal. 1:pe31
    [Google Scholar]
  56. 56.
    Higazi DR, Fearnley CJ, Drawnel FM, Talasila A, Corps EM et al. 2009. Endothelin-1-stimulated InsP3-induced Ca2+ release is a nexus for hypertrophic signaling in cardiac myocytes. Mol. Cell 33:472–82
    [Google Scholar]
  57. 57.
    Wu X, Zhang T, Bossuyt J, Li X, McKinsey TA et al. 2006. Local InsP3-dependent perinuclear Ca2+ signaling in cardiac myocyte excitation-transcription coupling. J. Clin. Investig. 116:675–82
    [Google Scholar]
  58. 58.
    Harzheim D, Movassagh M, Foo RS-Y, Ritter O, Tashfeen A et al. 2009. Increased InsP3Rs in the junctional sarcoplasmic reticulum augment Ca2+ transients and arrhythmias associated with cardiac hypertrophy. PNAS 106:11406–11
    [Google Scholar]
  59. 59.
    Laver DR. 2018. Regulation of the RyR channel gating by Ca2+ and Mg2+. Biophys. Rev. 10:1087–95
    [Google Scholar]
  60. 60.
    Foskett JK, White C, Cheung KH, Mak DO. 2007. Inositol trisphosphate receptor Ca2+ release channels. Physiol. Rev. 87:593–658
    [Google Scholar]
  61. 61.
    Ljubojevic S, Bers DM. 2015. Nuclear calcium in cardiac myocytes. J. Cardiovasc. Pharmacol. 65:211–17
    [Google Scholar]
  62. 62.
    Luo D, Yang D, Lan X, Li K, Li X et al. 2008. Nuclear Ca2+ sparks and waves mediated by inositol 1,4,5-trisphosphate receptors in neonatal rat cardiomyocytes. Cell Calcium 43:165–74
    [Google Scholar]
  63. 63.
    Ghosh S, Tran K, Delbridge LMD, Hickey AJR, Hanssen E et al. 2018. Insights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture. PLOS Comput. Biol. 14:e1006640
    [Google Scholar]
  64. 64.
    Bleck CKE, Kim Y, Willingham TB, Glancy B. 2018. Subcellular connectomic analyses of energy networks in striated muscle. Nat. Commun. 9:5111
    [Google Scholar]
  65. 65.
    Rafelski SM, Viana MP, Zhang Y, Chan YH, Thorn KS et al. 2012. Mitochondrial network size scaling in budding yeast. Science 338:822–24
    [Google Scholar]
  66. 66.
    Jarosz J, Ghosh S, Delbridge LM, Petzer A, Hickey AJ et al. 2017. Changes in mitochondrial morphology and organization can enhance energy supply from mitochondrial oxidative phosphorylation in diabetic cardiomyopathy. Am. J. Physiol. Cell Physiol. 312:C190–97
    [Google Scholar]
  67. 67.
    Glancy B, Hartnell LM, Malide D, Yu Z-X, Combs CA et al. 2015. Mitochondrial reticulum for cellular energy distribution in muscle. Nature 523:617–20
    [Google Scholar]
  68. 68.
    Glancy B, Hartnell LM, Combs CA, Femnou A, Sun J et al. 2017. Power grid protection of the muscle mitochondrial reticulum. Cell Rep 19:487–96
    [Google Scholar]
  69. 69.
    Hollander JM, Thapa D, Shepherd DL. 2014. Physiological and structural differences in spatially distinct subpopulations of cardiac mitochondria: influence of cardiac pathologies. Am. J. Physiol. Heart Circ. Physiol. 307:H1–14
    [Google Scholar]
  70. 70.
    Guo Y, Li D, Zhang S, Yang Y, Liu J-J et al. 2018. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell 175:1430–42.e17
    [Google Scholar]
  71. 71.
    Abrisch RG, Gumbin SC, Wisniewski BT, Lackner LL, Voeltz GK. 2020. Fission and fusion machineries converge at ER contact sites to regulate mitochondrial morphology. J. Cell Biol. 219:e201911122
    [Google Scholar]
  72. 72.
    Gomez-Suaga P, Paillusson S, Stoica R, Noble W, Hanger DP, Miller CCJ. 2017. The ER-mitochondria tethering complex VAPB-PTPIP51 regulates autophagy. Curr. Biol. 27:371–85
    [Google Scholar]
  73. 73.
    Wong YC, Kim S, Peng W, Krainc D. 2019. Regulation and function of mitochondria-lysosome membrane contact sites in cellular homeostasis. Trends Cell Biol 29:500–13
    [Google Scholar]
  74. 74.
    Peng W, Wong YC, Krainc D. 2020. Mitochondria-lysosome contacts regulate mitochondrial Ca2+ dynamics via lysosomal TRPML1. PNAS 117:19266–75
    [Google Scholar]
  75. 75.
    Denisenko TV, Gorbunova AS, Zhivotovsky B. 2019. Mitochondrial involvement in migration, invasion and metastasis. Front. Cell Dev. Biol. 7:355
    [Google Scholar]
  76. 76.
    Caino MC, Ghosh JC, Chae YC, Vaira V, Rivadeneira DB et al. 2015. PI3K therapy reprograms mitochondrial trafficking to fuel tumor cell invasion. PNAS 112:8638–43
    [Google Scholar]
  77. 77.
    Rangaraju V, Lewis TL, Hirabayashi Y, Bergami M, Motori E et al. 2019. Pleiotropic mitochondria: the influence of mitochondria on neuronal development and disease. J. Neurosci. 39:8200–8
    [Google Scholar]
  78. 78.
    Rangaraju V, Lauterbach M, Schuman EM. 2019. Spatially stable mitochondrial compartments fuel local translation during plasticity. Cell 176:73–84.e15
    [Google Scholar]
  79. 79.
    York HM, Coyle J, Arumugam S. 2020. To be more precise: the role of intracellular trafficking in development and pattern formation. Biochem. Soc. Trans. 48:2051–66
    [Google Scholar]
  80. 80.
    Koseska A, Bastiaens PIH. 2020. Processing temporal growth factor patterns by an epidermal growth factor receptor network dynamically established in space. Annu. Rev. Cell Dev. Biol. 36:359–83
    [Google Scholar]
  81. 81.
    Francavilla C, Papetti M, Rigbolt KT, Pedersen AK, Sigurdsson JO et al. 2016. Multilayered proteomics reveals molecular switches dictating ligand-dependent EGFR trafficking. Nat. Struct. Mol. Biol. 23:608–18
    [Google Scholar]
  82. 82.
    Villaseñor R, Nonaka H, Del Conte-Zerial P, Kalaidzidis Y, Zerial M. 2015. Regulation of EGFR signal transduction by analogue-to-digital conversion in endosomes. eLife 4:e06156
    [Google Scholar]
  83. 83.
    Stanoev A, Nandan AP, Koseska A. 2020. Organization at criticality enables processing of time-varying signals by receptor networks. Mol. Syst. Biol. 16:e8870
    [Google Scholar]
  84. 84.
    Lu M, van Tartwijk FW, Lin JQ, Nijenhuis W, Parutto P et al. 2020. The structure and global distribution of the endoplasmic reticulum network are actively regulated by lysosomes. Sci. Adv. 6:eabc7209
    [Google Scholar]
  85. 85.
    Burute M, Kapitein LC. 2019. Cellular logistics: unraveling the interplay between microtubule organization and intracellular transport. Annu. Rev. Cell Dev. Biol. 35:29–54
    [Google Scholar]
  86. 86.
    York HM, Patil A, Moorthi UK, Kaur A, Bhowmik A et al. 2021. Rapid whole cell imaging reveals a calcium-APPL1-dynein nexus that regulates cohort trafficking of stimulated EGF receptors. Commun. Biol. 4:224
    [Google Scholar]
  87. 87.
    Chen B-C, Legant WR, Wang K, Shao L, Milkie DE et al. 2014. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346:1257998
    [Google Scholar]
  88. 88.
    Akavia UD, Litvin O, Kim J, Sanchez-Garcia F, Kotliar D et al. 2010. An integrated approach to uncover drivers of cancer. Cell 143:1005–17
    [Google Scholar]
  89. 89.
    Johnson KA, Goody RS. 2011. The original Michaelis constant: translation of the 1913 Michaelis-Menten paper. Biochemistry 50:8264–69
    [Google Scholar]
  90. 90.
    Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500–44
    [Google Scholar]
  91. 91.
    Hunt H, Tilūnaitė A, Bass G, Soeller C, Roderick HL et al. 2020. Ca2+ release via IP3 receptors shapes the cardiac Ca2+ transient for hypertrophic signaling. Biophys. J. 119:1178–92
    [Google Scholar]
  92. 92.
    Tran K, Loiselle DS, Crampin EJ. 2015. Regulation of cardiac cellular bioenergetics: mechanisms and consequences. Physiol. Rep. 3:e12464
    [Google Scholar]
  93. 93.
    Colatsky T, Fermini B, Gintant G, Pierson JB, Sager P et al. 2016. The comprehensive in vitro proarrhythmia assay (CiPA) initiative—update on progress. J. Pharmacol. Toxicol. Methods 81:15–20
    [Google Scholar]
  94. 94.
    Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM et al. 2012. A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401
    [Google Scholar]
  95. 95.
    Macklin DN, Ahn-Horst TA, Choi H, Ruggero NA, Carrera J et al. 2020. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science 369:aav3751
    [Google Scholar]
  96. 96.
    Szigeti B, Roth YD, Sekar JAP, Goldberg AP, Pochiraju SC, Karr JR. 2018. A blueprint for human whole-cell modeling. Curr. Opin. Syst. Biol. 7:8–15
    [Google Scholar]
  97. 97.
    Dao M, Lim CT, Suresh S. 2003. Mechanics of the human red blood cell deformed by optical tweezers. J. Mech. Phys. Solids 51:2259–80
    [Google Scholar]
  98. 98.
    Ye T, Phan-Thien N, Lim CT. 2016. Particle-based simulations of red blood cells—a review. J. Biomech. 49:2255–66
    [Google Scholar]
  99. 99.
    Suresh S, Spatz J, Mills JP, Micoulet A, Dao M et al. 2005. Connections between single-cell biomechanics and human disease states: gastrointestinal cancer and malaria. Acta Biomater 1:15–30
    [Google Scholar]
  100. 100.
    Li X, Peng Z, Lei H, Dao M, Karniadakis GE. 2014. Probing red blood cell mechanics, rheology and dynamics with a two-component multi-scale model. Philos. Trans. A 372:20130389
    [Google Scholar]
  101. 101.
    Rajagopal V, Holmes WR, Lee PVS. 2018. Computational modeling of single-cell mechanics and cytoskeletal mechanobiology. WIREs Syst. Biol. Med. 10:e1407
    [Google Scholar]
  102. 102.
    Danuser G, Allard J, Mogilner A. 2013. Mathematical modeling of eukaryotic cell migration: insights beyond experiments. Annu. Rev. Cell Dev. Biol. 29:501–28
    [Google Scholar]
  103. 103.
    Yamada KM, Sixt M. 2019. Mechanisms of 3D cell migration. Nat. Rev. Mol. Cell Biol. 20:738–52
    [Google Scholar]
  104. 104.
    Luo T, Mohan K, Iglesias PA, Robinson DN. 2013. Molecular mechanisms of cellular mechanosensing. Nat. Mater. 12:1064–71
    [Google Scholar]
  105. 105.
    Niculescu I, Textor J, de Boer RJ. 2015. Crawling and gliding: a computational model for shape-driven cell migration. PLOS Comput. Biol. 11:e1004280
    [Google Scholar]
  106. 106.
    Strychalski W, Copos CA, Lewis OL, Guy RD. 2015. A poroelastic immersed boundary method with applications to cell biology. J. Comput. Phys. 282:77–97
    [Google Scholar]
  107. 107.
    Zmurchok C, Collette J, Rajagopal V, Holmes WR. 2020. Membrane tension can enhance adaptation to maintain polarity of migrating cells. Biophys. J. 119:1617–29
    [Google Scholar]
  108. 108.
    Kim M-C, Silberberg YR, Abeyaratne R, Kamm RD, Asada HH. 2018. Computational modeling of three-dimensional ECM-rigidity sensing to guide directed cell migration. PNAS 115:E390–99
    [Google Scholar]
  109. 109.
    Hinch R, Greenstein JL, Winslow RL. 2006. Multi-scale models of local control of calcium induced calcium release. Prog. Biophys. Mol. Biol. 90:136–50
    [Google Scholar]
  110. 110.
    Cortassa S, Aon MA, O'Rourke B, Jacques R, Tseng H-J et al. 2006. A computational model integrating electrophysiology, contraction, and mitochondrial bioenergetics in the ventricular myocyte. Biophys. J. 91:1564–89
    [Google Scholar]
  111. 111.
    Beard DA. 2005. A biophysical model of the mitochondrial respiratory system and oxidative phosphorylation. PLOS Comput. Biol. 1:e36
    [Google Scholar]
  112. 112.
    Rice JJ, Wang F, Bers DM, de Tombe PP. 2008. Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. Biophys. J. 95:2368–90
    [Google Scholar]
  113. 113.
    Colman MA, Pinali C, Trafford AW, Zhang H, Kitmitto A. 2017. A computational model of spatio-temporal cardiac intracellular calcium handling with realistic structure and spatial flux distribution from sarcoplasmic reticulum and t-tubule reconstructions. PLOS Comput. Biol. 13:e1005714
    [Google Scholar]
  114. 114.
    Ladd D, Tilūnaitė A, Roderick HL, Soeller C, Crampin EJ, Rajagopal V. 2019. Assessing cardiomyocyte excitation-contraction coupling site detection from live cell imaging using a structurally-realistic computational model of calcium release. Front. Physiol. 10:1263
    [Google Scholar]
  115. 115.
    Rajagopal V, Bass G, Walker CG, Crossman DJ, Petzer A et al. 2015. Examination of the effects of heterogeneous organization of RyR clusters, myofibrils and mitochondria on Ca2+ release patterns in cardiomyocytes. PLOS Comput. Biol. 11:e1004417
    [Google Scholar]
  116. 116.
    Soeller C, Jayasinghe ID, Li P, Holden AV, Cannell MB. 2009. Three-dimensional high-resolution imaging of cardiac proteins to construct models of intracellular Ca2+ signalling in rat ventricular myocytes. Exp. Physiol. 94:496–508
    [Google Scholar]
  117. 117.
    Thornburg ZR, Bianchi DM, Brier TA, Gilbert BR, Earnest TM et al. 2022. Fundamental behaviors emerge from simulations of a living minimal cell. Cell 185:345–60.e28
    [Google Scholar]
  118. 118.
    Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [cs.CV]
  119. 119.
    He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition770–78 Los Alamitos, CA: IEEE Comput. Soc.
  120. 120.
    Milletari F, Navab N, Ahmadi S-A. 2016. V-net: fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV)565–71 Los Alamitos, CA: IEEE Comput. Soc.
  121. 121.
    Ronneberger O, Fischer P, Brox T. 2015. U-net: convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention234–41 Cham, Switz: Springer
  122. 122.
    Haberl MG, Churas C, Tindall L, Boassa D, Phan S et al. 2018. CDeep3M—plug-and-play cloud-based deep learning for image segmentation. Nat. Methods 15:677–80
    [Google Scholar]
  123. 123.
    Khadangi A, Boudier T, Rajagopal V. 2020. EM-net: deep learning for electron microscopy image segmentation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR)31–8 Los Alamitos, CA: IEEE Comput. Soc.
  124. 124.
    Khadangi A, Boudier T, Rajagopal V. 2021. EM-stellar: benchmarking deep learning for electron microscopy image segmentation. Bioinformatics 37:97–106
    [Google Scholar]
  125. 125.
    Khadangi A, Hanssen E, Rajagopal V. 2019. Automated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data. BMC Med. Informat. Decis. Making 19:272
    [Google Scholar]
  126. 126.
    Heinrich L, Bennett D, Ackerman D, Park W, Bogovic J et al. 2021. Whole-cell organelle segmentation in volume electron microscopy. Nature 599:141–46
    [Google Scholar]
  127. 127.
    Ounkomol C, Seshamani S, Maleckar MM, Collman F, Johnson GR. 2018. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15:917–20
    [Google Scholar]
  128. 128.
    Karras T, Laine S, Aila T 2019. A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition4401–10 Los Alamitos, CA: IEEE Comput. Soc.
  129. 129.
    Khadangi A, Boudier T, Rajagopal V. 2021. CardioVinci: building blocks for virtual cardiac cells using deep learning. bioRxiv 2021.08.22.457257. https://doi.org/10.1101/2021.08.22.457257
    [Crossref]
  130. 130.
    Cuellar AA, Lloyd CM, Nielsen PF, Bullivant DP, Nickerson DP, Hunter PJ. 2003. An overview of CellML 1.1, a biological model description language. Simulation 79:740–47
    [Google Scholar]
  131. 131.
    Christie GR, Nielsen PM, Blackett SA, Bradley CP, Hunter PJ. 2009. FieldML: concepts and implementation. Philos. Trans. A 367:1869–84
    [Google Scholar]
  132. 132.
    Yu T, Lloyd CM, Nickerson DP, Cooling MT, Miller AK et al. 2011. The Physiome Model Repository 2. Bioinformatics 27:743–44
    [Google Scholar]
  133. 133.
    Garny A, Hunter PJ. 2015. OpenCOR: a modular and interoperable approach to computational biology. Front. Physiol. 6:26
    [Google Scholar]
  134. 134.
    Hucka M, Bergmann FT, Dräger A, Hoops S, Keating SM et al. 2018. The Systems Biology Markup Language (SBML): language specification for level 3 version 2 core. J. Integr. Bioinform. 15:120170081
    [Google Scholar]
  135. 135.
    Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L et al. 2010. BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol. 4:92
    [Google Scholar]
  136. 136.
    Waltemath D, Adams R, Bergmann FT, Hucka M, Kolpakov F et al. 2011. Reproducible computational biology experiments with SED-ML—the simulation experiment description markup language. BMC Syst. Biol. 5:198
    [Google Scholar]
  137. 137.
    Daly AC, Clerx M, Beattie KA, Cooper J, Gavaghan DJ, Mirams GR. 2018. Reproducible model development in the Cardiac Electrophysiology Web Lab. Prog. Biophys. Mol. Biol. 139:3–14
    [Google Scholar]
  138. 138.
    Cowan AE, Mendes P, Blinov ML. 2019. ModelBricks—modules for reproducible modeling improving model annotation and provenance. NPJ Syst. Biol. Appl. 5:37
    [Google Scholar]
  139. 139.
    Gawthrop PJ, Cursons J, Crampin EJ. 2015. Hierarchical bond graph modelling of biochemical networks. Proc. R. Soc. A. 471:20150642
    [Google Scholar]
  140. 140.
    Gawthrop PJ, Bevan GP. 2007. Bond-graph modeling. IEEE Control. Syst. Mag. 27:24–45
    [Google Scholar]
  141. 141.
    Argus FJ, Bradley CP, Hunter PJ. 2021. Theory and implementation of coupled port-Hamiltonian continuum and lumped parameter models. J. Elasticity 145:339–82
    [Google Scholar]
  142. 142.
    Gawthrop PJ, Crampin EJ. 2014. Energy-based analysis of biochemical cycles using bond graphs. Proc. R. Soc. A 470:20140459
    [Google Scholar]
  143. 143.
    Gawthrop PJ, Pan M, Crampin EJ. 2021. Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data. J. R. Soc. Interface 18:20210478
    [Google Scholar]
  144. 144.
    Pan M, Gawthrop PJ, Cursons J, Crampin EJ. 2021. Modular assembly of dynamic models in systems biology. PLOS Comput. Biol. 17:e1009513
    [Google Scholar]
  145. 145.
    Gawthrop PJ, Crampin EJ. 2016. Modular bond-graph modelling and analysis of biomolecular systems. IET Syst. Biol. 10:187–201
    [Google Scholar]
  146. 146.
    Pan M, Gawthrop PJ, Tran K, Cursons J, Crampin EJ. 2018. Bond graph modelling of the cardiac action potential: implications for drift and non-unique steady states. Proc. R. Soc. A 474:20180106
    [Google Scholar]
  147. 147.
    Gawthrop PJ. 2017. Bond graph modeling of chemiosmotic biomolecular energy transduction. IEEE Trans. Nanobiosci. 16:177–88
    [Google Scholar]
  148. 148.
    Gawthrop PJ, Pan M. 2021. Network thermodynamical modeling of bioelectrical systems: a bond graph approach. Bioelectricity 3:3–13
    [Google Scholar]
  149. 149.
    Diaz-Zuccarini V, Pichardo-Almarza C. 2011. On the formalization of multi-scale and multi-science processes for integrative biology. Interface Focus 1:426–37
    [Google Scholar]
  150. 150.
    Cudmore P, Pan M, Gawthrop PJ, Crampin EJ. 2021. Analysing and simulating energy-based models in biology using BondGraphTools. Eur. Phys. J. E 44:148
    [Google Scholar]
  151. 151.
    Shahidi N, Pan M, Safaei S, Tran K, Crampin EJ, Nickerson DP. 2021. Hierarchical semantic composition of biosimulation models using bond graphs. PLOS Comput. Biol. 17:e1008859
    [Google Scholar]
  152. 152.
    Pichersky E. 2005. Is the concept of regulation overused in molecular and cellular biology?. Plant Cell 17:3217–18
    [Google Scholar]
  153. 153.
    Rangamani P, Lipshtat A, Azeloglu EU, Calizo RC, Hu M et al. 2013. Decoding information in cell shape. Cell 154:1356–69
    [Google Scholar]
  154. 154.
    Roy B, Yuan L, Lee Y, Bharti A, Mitra A, Shivashankar GV 2020. Fibroblast rejuvenation by mechanical reprogramming and redifferentiation. PNAS 117:10131–41
    [Google Scholar]
  155. 155.
    Hartlmayr D, Ctortecka C, Seth A, Mendjan S, Tourniaire G, Mechtler K. 2021. An automated workflow for label-free and multiplexed single cell proteomics sample preparation at unprecedented sensitivity. bioRxiv 2021.04.14.439828. https://doi.org/10.1101/2021.04.14.439828
    [Crossref]
  156. 156.
    Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. 2017. Transcriptomics technologies. PLOS Comput. Biol. 13:e1005457
    [Google Scholar]
  157. 157.
    Johnson CH, Ivanisevic J, Siuzdak G. 2016. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17:451–59
    [Google Scholar]
  158. 158.
    Hedde PN, Cinco R, Malacrida L, Kamaid A, Gratton E. 2021. Phasor-based hyperspectral snapshot microscopy allows fast imaging of live, three-dimensional tissues for biomedical applications. Commun. Biol. 4:721
    [Google Scholar]
  159. 159.
    Wagner DE, Klein AM. 2020. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21:410–27
    [Google Scholar]
  160. 160.
    Wagner A, Regev A, Yosef N. 2016. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34:1145–60
    [Google Scholar]
  161. 161.
    Xu CS, Hayworth KJ, Lu Z, Grob P, Hassan AM et al. 2017. Enhanced FIB-SEM systems for large-volume 3D imaging. eLife 6:e25916
    [Google Scholar]
  162. 162.
    Xu CS, Pang S, Shtengel G, Muller A, Ritter AT et al. 2021. An open-access volume electron microscopy atlas of whole cells and tissues. Nature 599:147–51
    [Google Scholar]
  163. 163.
    Guck J, Ananthakrishnan R, Mahmood H, Moon TJ, Cunningham CC, Käs J. 2001. The optical stretcher: a novel laser tool to micromanipulate cells. Biophys. J. 81:767–84
    [Google Scholar]
  164. 164.
    Modi S, Gowrishankar S, Goswami D, Gupta GD, Mayor S, Krishnan Y 2009. A DNA nanomachine that maps spatial and temporal pH changes inside living cells. Nat. Nanotechnol. 4:325–30
    [Google Scholar]
  165. 165.
    Ma R, Kellner AV, Ma VP-Y, Su H, Deal BR et al. 2019. DNA probes that store mechanical information reveal transient piconewton forces applied by T cells. PNAS 116:16949–54
    [Google Scholar]
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