Abstract
Peripheral nerve tissue engineering has great promise for growing replacement tissues to treat peripheral nerve injuries. To date, the design of replacement tissues has focused on in vitro and in vivo experiments to characterize and test the impact of a vast range of materials, cells, and other factors, with limited translation to human trials. Here we propose and discuss the use of in silico modeling as a complementary approach to inform and accelerate the design of engineered replacement tissues.
Mathematical modeling in nerve regeneration is a small research field; however, there is a rich literature in using mathematical modeling to describe a host of highly relevant underpinning mechanisms (such as neurite growth, chemical diffusion, and angiogenesis) in different contexts. These models broadly fall into three categories: continuum (or averaged), discrete (or individual), and hybrid approaches, which combine the two. The key features and potential of these modeling categories are presented alongside a decision tree for matching a specific biological problem to the appropriate modeling approach. To be predictive, it is essential that mathematical models are benchmarked against experimental data. We present tools to efficiently fit models to such data (optimization) and investigate the reliability of model predictions (sensitivity analysis). Finally, this work concludes with two demonstrative case studies on the use of mathematical modeling (one continuum, one discrete) to tackle biological and design questions in peripheral nerve injury repair.
References
Allena R, Scianna M, Preziosi L (2016) A cellular Potts model of single cell migration in presence of durotaxis. Math Biosci 275:57–70
Anderson AR, Chaplain MAJ (1998) Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull Math Biol 60:857–899
Angius D, Wang H, Spinner RJ, Gutierrez-Cotto Y, Yaszemski MJ, Windebank AJ (2012) A systematic review of animal models used to study nerve regeneration in tissue-engineered scaffolds. Biomaterials 33:8034–8039. https://doi.org/10.1016/j.biomaterials.2012.07.056
Aubert M, Chaplain MAJ, McDougall SR, Devlin A, Mitchell CA (2011) A continuum mathematical model of the developing murine retinal vasculature. Bull Math Biol 73:2430–2451. https://doi.org/10.1007/s11538-011-9631-y
Azuaje F (2011) Computational discrete models of tissue growth and regeneration. Brief Bioinform 12:64–77. https://doi.org/10.1093/bib/bbq017
Barkefors I, Le Jan S, Jakobsson L, Hejll E, Carlson G, Johansson H, Jarvius J, Park JW, Jeon NL, Kreuger J (2008) Endothelial cell migration in stable gradients of vascular endothelial growth factor a and fibroblast growth factor 2 effects on chemotaxis and chemokinesis. J Biol Chem 283:13905–13912
Baroni G, Tarantola S (2014) A general probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: a hydrological case study. Environ Model Softw 51:26–34
Boas SEM, Jiang Y, Merks RMH, Prokopiou SA, Rens EG (2018) Cellular Potts model: applications to vasculogenesis and angiogenesis. In: Louis P-Y, Nardi FR (eds) Probabilistic cellular automata: theory, applications and future perspectives, emergence, complexity and computation. Springer International Publishing, Cham, pp 279–310. https://doi.org/10.1007/978-3-319-65558-1_18
Borgonovo E (2011) Sensitivity analysis in decision making. In: Wiley encyclopedia of operations research and management science. pp 1–11
Bower JM, Beeman D (2012) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System. Springer Science & Business Media
Boyd JG, Gordon T (2003) Neurotrophic factors and their receptors in axonal regeneration and functional recovery after peripheral nerve injury. Mol Neurobiol 27:277–324. https://doi.org/10.1385/MN:27:3:277
Breward CJW, Byrne HM, Lewis CE (2002) The role of cell-cell interactions in a two-phase model for avascular tumour growth. J Math Biol 45:125–152. https://doi.org/10.1007/s002850200149
Brown KS, Sethna JP (2003) Statistical mechanical approaches to models with many poorly known parameters. Phys Rev E 68:021904
Burova I, Wall I, Shipley RJ (2019) Mathematical and computational models for bone tissue engineering in bioreactor systems. J Tissue Eng 10:204173141982792. https://doi.org/10.1177/2041731419827922
Butt R (2009) Introduction to numerical analysis using MATLAB®. Jones & Bartlett Learning
Cattin A-L, Burden JJ, Van Emmenis L, Mackenzie FE, Hoving JJA, Garcia Calavia N, Guo Y, McLaughlin M, Rosenberg LH, Quereda V, Jamecna D, Napoli I, Parrinello S, Enver T, Ruhrberg C, Lloyd AC (2015) Macrophage-induced blood vessels guide Schwann cell-mediated regeneration of peripheral nerves. Cell 162:1127–1139. https://doi.org/10.1016/j.cell.2015.07.021
Chung AM (2018) Calcitonin gene-related peptide (CGRP): role in peripheral nerve regeneration. Rev Neurosci 29:369–376. https://doi.org/10.1515/revneuro-2017-0060
Cickovski T, Aras K, Swat M, Merks RMH, Glimm T, Hentschel HGE, Alber MS, Glazier JA, Newman SA, Izaguirre JA (2007) From genes to organisms via the cell: a problem-solving environment for multicellular development. Comput Sci Eng 9:50–60. https://doi.org/10.1109/MCSE.2007.74
Coelho RC, da Fontoura Costa L (1997) Morphologically realistic neural networks. In: Proceedings of the third IEEE international conference on engineering of complex computer systems (Cat. No. 97TB100168). IEEE, pp. 223–228
Coffey W, Kalmykov YP (2012) The Langevin equation: with applications to stochastic problems in physics, chemistry and electrical engineering. World Scientific
Coy RH (2020) Modelling the impact of cell seeding strategies on cell survival and vascularisation in engineered tissue. PhD Thesis, UCL (University College London)
Coy R, Al-Badri G, Kayal C, O’Rourke C, Kingham PJ, Phillips JB, Shipley RJ (2020) Combining in silico and in vitro models to inform cell seeding strategies in tissue engineering. J R Soc Interface 17:20190801. https://doi.org/10.1098/rsif.2019.0801
Croll TI, Gentz S, Mueller K, Davidson M, O’Connor AJ, Stevens GW, Cooper-White JJ (2005) Modelling oxygen diffusion and cell growth in a porous, vascularising scaffold for soft tissue engineering applications. Chem Eng Sci 60:4924–4934. https://doi.org/10.1016/j.ces.2005.03.051
Daly W, Yao L, Zeugolis D, Windebank A, Pandit A (2012) A biomaterials approach to peripheral nerve regeneration: bridging the peripheral nerve gap and enhancing functional recovery. J R Soc Interface 9:202–221. https://doi.org/10.1098/rsif.2011.0438
Deumens R, Bozkurt A, Meek MF, Marcus MAE, Joosten EAJ, Weis J, Brook GA (2010) Repairing injured peripheral nerves: bridging the gap. Prog Neurobiol 92:245–276. https://doi.org/10.1016/j.pneurobio.2010.10.002
Dickinson RB, Tranquillo RT (1993) A stochastic model for adhesion-mediated cell random motility and haptotaxis. J Math Biol 31:563–600
Dodla MC, Bellamkonda RV (2008) Peripheral nerve regeneration. In: Principles of regenerative medicine. Elsevier, pp 1270–1285. https://doi.org/10.1016/B978-012369410-2.50076-0
Dodla MC, Alvarado-Velez M, Mukhatyar VJ, Bellamkonda RV (2019) Chapter 69 peripheral nerve regeneration. In: Atala A, Lanza R, Mikos AG, Nerem R (eds) Principles of regenerative medicine (third edition). Academic Press, Boston, pp 1223–1236. https://doi.org/10.1016/B978-0-12-809880-6.00069-2
Doob JL (1942) The Brownian movement and stochastic equations. Ann Math 43:351–369
Dyson F (2004) A meeting with Enrico Fermi. Nature 427:297–297. https://doi.org/10.1038/427297a
Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Citeseer, pp 1942–1948
Faroni A, Mobasseri SA, Kingham PJ, Reid AJ (2015) Peripheral nerve regeneration: Experimental strategies and future perspectives. Adv Drug Deliv Rev 82–83:160–167. https://doi.org/10.1016/j.addr.2014.11.010. Regenerative medicine strategies in Urology
Fontana X, Hristova M, Da Costa C, Patodia S, Thei L, Makwana M, Spencer-Dene B, Latouche M, Mirsky R, Jessen KR, Klein R, Raivich G, Behrens A (2012) c-Jun in Schwann cells promotes axonal regeneration and motoneuron survival via paracrine signaling. J. Cell Biol 198:127–141. https://doi.org/10.1083/jcb.201205025
Frostick SP, Yin Q, Kemp GJ (1998) Schwann cells, neurotrophic factors, and peripheral nerve regeneration. Microsurgery 18:397–405
Georgiou M (2013) Development of a tissue engineered implantable device for the surgical repair of the peripheral nervous system. https://doi.org/10.21954/OU.RO.0000EEF7
Georgiou M, Bunting SCJ, Davies HA, Loughlin AJ, Golding JP, Phillips JB (2013) Engineered neural tissue for peripheral nerve repair. Biomaterials 34:7335–7343. https://doi.org/10.1016/j.biomaterials.2013.06.025
Grinsell D, Keating CP (2014) Peripheral nerve reconstruction after injury: a review of clinical and experimental therapies [WWW Document]. Biomed Res Int. https://doi.org/10.1155/2014/698256
Gu X, Ding F, Williams DF (2014) Neural tissue engineering options for peripheral nerve regeneration. Biomaterials 35:6143–6156. https://doi.org/10.1016/j.biomaterials.2014.04.064
Guenard V, Kleitman N, Morrissey T, Bunge R, Aebischer P (1992) Syngeneic Schwann cells derived from adult nerves seeded in semipermeable guidance channels enhance peripheral nerve regeneration. J Neurosci 12:3310–3320. https://doi.org/10.1523/JNEUROSCI.12-09-03310.1992
Guidolin D, Albertin G, Sorato E, Oselladore B, Mascarin A, Ribatti D (2009) Mathematical modeling of the capillary-like pattern generated by adrenomedullin-treated human vascular endothelial cells in vitro. Dev Dyn 238:1951–1963. https://doi.org/10.1002/dvdy.22022
Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007) Universally Sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 3:e189
Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9:1179–1209
Hirashima T, Rens EG, Merks RMH (2017) Cellular Potts modeling of complex multicellular behaviors in tissue morphogenesis. Develop Growth Differ 59:329–339. https://doi.org/10.1111/dgd.12358
Iooss B, Lemaître P (2015) A review on global sensitivity analysis methods. In: Uncertainty management in simulation-optimization of complex systems. Springer, pp 101–122
Jessen KR, Mirsky R (2016) The repair Schwann cell and its function in regenerating nerves. J Physiol 594:3521–3531. https://doi.org/10.1113/JP270874
Jurecka W, Ammerer HP, Lassmann H (1975) Regeneration of a transected peripheral nerve. An autoradiographic and electron microscopic study. Acta Neuropathol (Berl) 32:299–312
Kaplan HM, Mishra P, Kohn J (2015) The overwhelming use of rat models in nerve regeneration research may compromise designs of nerve guidance conduits for humans. J Mater Sci Mater Med 26:226. https://doi.org/10.1007/s10856-015-5558-4
Kim Y-P, Lee G-S, Kim J-W, Kim MS, Ahn H-S, Lim J-Y, Kim H-W, Son Y-J, Knowles JC, Hyun JK (2015) Phosphate glass fibres promote neurite outgrowth and early regeneration in a peripheral nerve injury model. J Tissue Eng Regen Med 9:236–246
Krause AL, Beliaev D, Van Gorder RA, Waters SL (2017) Lattice and continuum modelling of a bioactive porous tissue scaffold. ArXiv Prepr. ArXiv170207711
Lafosse A, Dufeys C, Beauloye C, Horman S, Dufrane D (2016) Impact of hyperglycemia and low oxygen tension on adipose-derived stem cells compared with dermal fibroblasts and keratinocytes: importance for wound healing in Type 2 diabetes. PLoS One 11:e0168058
Laranjeira S, Pellegrino G, Bhangra KS, Phillips JB, Shipley RJ (2022) In silico framework to inform the design of repair constructs for peripheral nerve injury repair. J R Soc Interface 20210824. https://doi.org/10.1098/rsif.2021.0824
Lewis MC, Macarthur BD, Malda J, Pettet G, Please CP (2005) Heterogeneous proliferation within engineered cartilaginous tissue: the role of oxygen tension. Biotechnol Bioeng 91:607–615. https://doi.org/10.1002/bit.20508
Liu H, Chen W, Sudjianto A (2006) Relative entropy based method for probabilistic sensitivity analysis in engineering design. J Mech Des 128:326–336
Lundborg G (2003) Richard P. Bunge memorial lecture. Nerve injury and repair – a challenge to the plastic brain. J Peripher Nerv Syst 8:209–226
Martens W, Sanen K, Georgiou M, Struys T, Bronckaers A, Ameloot M, Phillips J, Lambrichts I (2014) Human dental pulp stem cells can differentiate into Schwann cells and promote and guide neurite outgrowth in an aligned tissue-engineered collagen construct in vitro. FASEB J 28:1634–1643. https://doi.org/10.1096/fj.13-243980
McDougall SR, Anderson ARA, Chaplain MAJ, Sherratt JA (2002) Mathematical modelling of flow through vascular networks: implications for tumour-induced angiogenesis and chemotherapy strategies. Bull Math Biol 64:673–702. https://doi.org/10.1006/bulm.2002.0293
McDougall SR, Anderson AR, Chaplain MA (2006) Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: clinical implications and therapeutic targeting strategies. J Theor Biol 241:564–589
McDougall SR, Watson MG, Devlin AH, Mitchell CA, Chaplain MAJ (2012) A hybrid discrete-continuum mathematical model of pattern prediction in the developing retinal vasculature. Bull Math Biol 74:2272–2314. https://doi.org/10.1007/s11538-012-9754-9
Merks RMH, Brodsky SV, Goligorksy MS, Newman SA, Glazier JA (2006) Cell elongation is key to in silico replication of in vitro vasculogenesis and subsequent remodeling. Dev Biol 289:44–54. https://doi.org/10.1016/j.ydbio.2005.10.003
Metzcar J, Wang Y, Heiland R, Macklin P (2019) A review of cell-based computational modeling in cancer biology. JCO Clin Cancer Inform 3:1–13. https://doi.org/10.1200/CCI.18.00069
Mosahebi A, Woodward B, Wiberg M, Martin R, Terenghi G (2001) Retroviral labeling of Schwann cells: in vitro characterization and in vivo transplantation to improve peripheral nerve regeneration. Glia 34:8–17
Muheremu A, Ao Q (2015) Past, present, and future of nerve conduits in the treatment of peripheral nerve injury [WWW Document]. Biomed Res Int. https://doi.org/10.1155/2015/237507
Napoli I, Noon LA, Ribeiro S, Kerai AP, Parrinello S, Rosenberg LH, Collins MJ, Harrisingh MC, White IJ, Woodhoo A, Lloyd AC (2012) A central role for the ERK-signaling pathway in controlling Schwann cell plasticity and peripheral nerve regeneration in vivo. Neuron 73:729–742. https://doi.org/10.1016/j.neuron.2011.11.031
Nectow AR, Marra KG, Kaplan DL (2012) Biomaterials for the development of peripheral nerve guidance conduits. Tissue Eng Part B Rev 18:40–50. https://doi.org/10.1089/ten.TEB.2011.0240
Nguyen QT, Sanes JR, Lichtman JW (2002) Pre-existing pathways promote precise projection patterns. Nat Neurosci 5:861–867. https://doi.org/10.1038/nn905
Novosel EC, Kleinhans C, Kluger PJ (2011) Vascularization is the key challenge in tissue engineering. Adv Drug Deliv Rev 63:300–311
O’Dea R, Byrne H, Waters S (2012) Continuum modelling of in vitro tissue engineering: a review. In: Geris L (ed) Computational modeling in tissue engineering, studies in mechanobiology, tissue engineering and biomaterials. Springer, Berlin/Heidelberg, pp 229–266. https://doi.org/10.1007/8415_2012_140
Odedra D, Chiu LL, Shoichet M, Radisic M (2011) Endothelial cells guided by immobilized gradients of vascular endothelial growth factor on porous collagen scaffolds. Acta Biomater 7:3027–3035
Parrinello S, Napoli I, Ribeiro S, Wingfield Digby P, Fedorova M, Parkinson DB, Doddrell RDS, Nakayama M, Adams RH, Lloyd AC (2010) EphB signaling directs peripheral nerve regeneration through Sox2-dependent Schwann cell sorting. Cell 143:145–155. https://doi.org/10.1016/j.cell.2010.08.039
Patel NP, Lyon KA, Huang JH (2018) An update–tissue engineered nerve grafts for the repair of peripheral nerve injuries. Neural Regen Res 13:764
Peripheral nerve graft – Mayo Clinic [WWW Document] (n.d.). https://www.mayoclinic.org/diseases-conditions/peripheral-nerve-injuries/multimedia/img-20337149. Accessed 3 Nov 2021
Pianosi F, Wagener T (2015) A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environ Model Softw 67:1–11
Plischke E, Borgonovo E, Smith CL (2013) Global sensitivity measures from given data. Eur J Oper Res 226:536–550
Podhajsky RJ, Myers RR (1995) A diffusion-reaction model of nerve regeneration. J Neurosci Methods 60:79–88. https://doi.org/10.1016/0165-0270(94)00222-3
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57
Prokopiou SA, Owen MR, Byrne HM, Ziyad S, Domigan C, Iruela-Arispe ML, Jiang Y (2016) Integrative modeling of sprout formation in angiogenesis: coupling the VEGFA-Notch signaling in a dynamic stalk-tip cell selection. ArXiv Prepr. ArXiv160602167
Puy A, Piano SL, Saltelli A (2019) A sensitivity analysis of the PAWN sensitivity index. ArXiv Prepr. ArXiv190404488
Rabitz H (1989) Systems analysis at the molecular scale. Science 246:221–226
Razetti A, Descombes X, Medioni C, Besse F (2016) A stochastic framework for neuronal morphological comparison: application to the study of IMP knockdown effects in drosophila gamma neurons. In: International joint conference on biomedical engineering systems and technologies. Springer, pp 145–166
Razetti A, Medioni C, Malandain G, Besse F, Descombes X (2018) A stochastic framework to model axon interactions within growing neuronal populations. PLoS Comput Biol 14:e1006627
Rutkowski GE, Heath CA (2002) Development of a bioartificial nerve graft. II. Nerve regeneration in vitro. Biotechnol Prog 18:373–379. https://doi.org/10.1021/bp020280h
Saltelli A, Tarantola S, Chan K-S (1999) A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41:39–56
Saltelli A, Tarantola S, Campolongo F, Ratto M (2002) Sensitivity analysis in practice. Wiley, Chichester. https://doi.org/10.1002/0470870958
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181:259–270
Samsonovich AV, Ascoli GA (2005) Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: a hidden Markov model. Hippocampus 15:166–183
Schienbein M, Gruler H (1993) Langevin equation, Fokker-Planck equation and cell migration. Bull Math Biol 55:585–608
Segev R, Ben-Jacob E (2000) Generic modeling of chemotactic based self-wiring of neural networks. Neural Netw 13:185–199
Smith JT, Tomfohr JK, Wells MC, Beebe TP, Kepler TB, Reichert WM (2004) Measurement of cell migration on surface-bound fibronectin gradients. Langmuir 20:8279–8286
Stefanoni F, Ventre M, Mollica F, Netti PA (2011) A numerical model for durotaxis. J Theor Biol 280:150–158. https://doi.org/10.1016/j.jtbi.2011.04.001
Stokes CL, Lauffenburger DA (1991) Analysis of the roles of microvessel endothelial cell random motility and chemotaxis in angiogenesis. J Theor Biol 152:377–403
Tarantola S, Becker W (2016) SIMLAB software for uncertainty and sensitivity analysis. In: Ghanem R, Higdon D, Owhadi H (eds) Handbook of uncertainty quantification. Springer International Publishing, Cham, pp 1–21. https://doi.org/10.1007/978-3-319-11259-6_61-1
Tranquillo RT, Lauffenburger DA (1987) Stochastic model of leukocyte chemosensory movement. J Math Biol 25:229–262
Van Pelt J, Dityatev AE, Uylings HB (1997) Natural variability in the number of dendritic segments: Model-based inferences about branching during neurite outgrowth. J Comp Neurol 387:325–340
Vaz AIF, Vicente LN (2007) A particle swarm pattern search method for bound constrained global optimization. J Glob Optim 39:197–219
Vokes SA, Yatskievych TA, Heimark RL, McMahon J, McMahon AP, Antin PB, Krieg PA (2004) Hedgehog signaling is essential for endothelial tube formation during vasculogenesis. Development 131:4371–4380. https://doi.org/10.1242/dev.01304
Wang L, Sanford MT, Xin Z, Lin G, Lue TF (2015) Role of Schwann cells in the regeneration of penile and peripheral nerves. Asian J Androl 17:776–782. https://doi.org/10.4103/1008-682X.154306
Ward JP, King JR (1997) Mathematical modelling of avascular-tumour growth. IMA J Math Appl Med Biol 14:39–69
Wu JS, Luo L (2006) A protocol for mosaic analysis with a repressible cell marker (MARCM) in Drosophila. Nat Protoc 1:2583
Wu P, Fu Y, Cai K (2014) Regulation of the migration of endothelial cells by a gradient density of vascular endothelial growth factor. Colloids Surf B Biointerfaces 123:181–190
Zochodne DW (2008) Neurobiology of peripheral nerve regeneration by Douglas W. Zochodne [WWW Document]. Cambridge Core. https://doi.org/10.1017/CBO9780511541759
Zubler F, Douglas R (2009) A framework for modeling the growth and development of neurons and networks. Front Comput Neurosci 3. https://doi.org/10.3389/neuro.10.025.2009
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Laranjeira, S., Coy, R., Shipley, R.J. (2021). Mathematical Modeling for Nerve Repair Research. In: Phillips, J., Hercher, D., Hausner, T. (eds) Peripheral Nerve Tissue Engineering and Regeneration. Reference Series in Biomedical Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-06217-0_10-1
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