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Mathematical Modeling for Nerve Repair Research

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Peripheral Nerve Tissue Engineering and Regeneration

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.

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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

    Article  MathSciNet  Google Scholar 

  • Anderson AR, Chaplain MAJ (1998) Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull Math Biol 60:857–899

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • Azuaje F (2011) Computational discrete models of tissue growth and regeneration. Brief Bioinform 12:64–77. https://doi.org/10.1093/bib/bbq017

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Borgonovo E (2011) Sensitivity analysis in decision making. In: Wiley encyclopedia of operations research and management science. pp 1–11

    Google Scholar 

  • Bower JM, Beeman D (2012) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System. Springer Science & Business Media

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • Brown KS, Sethna JP (2003) Statistical mechanical approaches to models with many poorly known parameters. Phys Rev E 68:021904

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Butt R (2009) Introduction to numerical analysis using MATLAB®. Jones & Bartlett Learning

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Coffey W, Kalmykov YP (2012) The Langevin equation: with applications to stochastic problems in physics, chemistry and electrical engineering. World Scientific

    Google Scholar 

  • Coy RH (2020) Modelling the impact of cell seeding strategies on cell survival and vascularisation in engineered tissue. PhD Thesis, UCL (University College London)

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dickinson RB, Tranquillo RT (1993) A stochastic model for adhesion-mediated cell random motility and haptotaxis. J Math Biol 31:563–600

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Dyson F (2004) A meeting with Enrico Fermi. Nature 427:297–297. https://doi.org/10.1038/427297a

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Citeseer, pp 1942–1948

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Frostick SP, Yin Q, Kemp GJ (1998) Schwann cells, neurotrophic factors, and peripheral nerve regeneration. Microsurgery 18:397–405

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9:1179–1209

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Krause AL, Beliaev D, Van Gorder RA, Waters SL (2017) Lattice and continuum modelling of a bioactive porous tissue scaffold. ArXiv Prepr. ArXiv170207711

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Liu H, Chen W, Sudjianto A (2006) Relative entropy based method for probabilistic sensitivity analysis in engineering design. J Mech Des 128:326–336

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Novosel EC, Kleinhans C, Kluger PJ (2011) Vascularization is the key challenge in tissue engineering. Adv Drug Deliv Rev 63:300–311

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Plischke E, Borgonovo E, Smith CL (2013) Global sensitivity measures from given data. Eur J Oper Res 226:536–550

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Puy A, Piano SL, Saltelli A (2019) A sensitivity analysis of the PAWN sensitivity index. ArXiv Prepr. ArXiv190404488

    Google Scholar 

  • Rabitz H (1989) Systems analysis at the molecular scale. Science 246:221–226

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Saltelli A, Tarantola S, Chan K-S (1999) A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41:39–56

    Article  Google Scholar 

  • Saltelli A, Tarantola S, Campolongo F, Ratto M (2002) Sensitivity analysis in practice. Wiley, Chichester. https://doi.org/10.1002/0470870958

    Book  MATH  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Samsonovich AV, Ascoli GA (2005) Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: a hidden Markov model. Hippocampus 15:166–183

    Article  Google Scholar 

  • Schienbein M, Gruler H (1993) Langevin equation, Fokker-Planck equation and cell migration. Bull Math Biol 55:585–608

    Article  Google Scholar 

  • Segev R, Ben-Jacob E (2000) Generic modeling of chemotactic based self-wiring of neural networks. Neural Netw 13:185–199

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Tranquillo RT, Lauffenburger DA (1987) Stochastic model of leukocyte chemosensory movement. J Math Biol 25:229–262

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Vaz AIF, Vicente LN (2007) A particle swarm pattern search method for bound constrained global optimization. J Glob Optim 39:197–219

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ward JP, King JR (1997) Mathematical modelling of avascular-tumour growth. IMA J Math Appl Med Biol 14:39–69

    Article  Google Scholar 

  • Wu JS, Luo L (2006) A protocol for mosaic analysis with a repressible cell marker (MARCM) in Drosophila. Nat Protoc 1:2583

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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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