Stochastic Spatial Modelling of the Remyelination Process in Multiple Sclerosis Lesions

  • Ludovica Luisa VissatEmail author
  • Jane Hillston
  • Anna Williams
Part of the Computational Biology book series (COBO, volume 30)


Remyelination is a regenerative process that aims to repair damaged regions of the central nervous system, caused by demyelinating diseases, like multiple sclerosis. This process fails to completely repair the demyelinated lesions in many cases and the causes of the failures are not clear. Since many factors and complex mechanisms regulate the process, it is helpful to use high-level modelling languages to describe it and model checking techniques to perform the analysis. They allow us to describe and simulate this stochastic process, and to analyse its behaviour in different scenarios. This study will support neurologists to reason about the different factors that influence this complex process and to create new hypotheses to test through lab experiments. In this chapter, we introduce a novel process algebra called MELA that we used for modelling the remyelination process. We present a number of MELA models capturing different hypotheses about the functioning of remyelination, and their comparison. We perform the analysis of the spatio-temporal evolution of remyelination using Signal Spatio-Temporal Logic and Statistical Model Checking.


  1. 1.
    Boyd A, Zhang H, Williams A (2013) Insufficient OPC migration into demyelinated lesions is a cause of poor remyelination in MS and mouse models. Acta Neuropathol 125(6):841–859CrossRefGoogle Scholar
  2. 2.
    Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361CrossRefGoogle Scholar
  3. 3.
    Hughes EG, Kang SH, Fukaya M, Bergles DE (2013) Oligodendrocyte progenitors balance growth with self-repulsion to achieve homeostasis in the adult brain. Nat NeurosciGoogle Scholar
  4. 4.
    Justus CR, Leffler N, Ruiz-Echevarria M, Yang LV (2014) In vitro cell migration and invasion assays. J Vis Exp: JoVE, 88Google Scholar
  5. 5.
    Klann M, Paulevé L, Petrov T, Koeppl H, Dynamics Simulation Coarse-Grained Brownian, of Rule-Based Models, Computational Methods in Systems Biology (CMSB), (2013) Lecture notes in computer science, vol 8130. Springer, Berlin, HeidelbergCrossRefGoogle Scholar
  6. 6.
    Lecca P, Priami C, Quaglia P, Rossi B, Laudanna C, Constantin G (2004) A stochastic process algebra approach to simulation of autoreactive lymphocyte recruitment. SIMULATION 80(6):273–288CrossRefGoogle Scholar
  7. 7.
    Lucchinetti C, Brück W, Parisi J, Scheithauer B, Rodriguez M, Lassmann H (2000) Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann Neurol 47:707–717CrossRefGoogle Scholar
  8. 8.
    Maler O, Nickovic D (2004) Monitoring temporal properties of continuous signals, Springer, Berlin, Heidelberg, pp 152–166CrossRefGoogle Scholar
  9. 9.
    Nenzi L, Bortolussi L, Ciancia V, Loreti M, Massink M (2015) Qualitative and quantitative monitoring of spatio-temporal properties. In: Runtime verification - 6th international conference, RV, pp 21–37Google Scholar
  10. 10.
    Ouzounov DG, Wang T, Wang M, Feng DD, Horton NG, Cruz-Hernández JC, Cheng Y, Reimer J, Tolias A, Nishimura N, Xu C (2017) In vivo three-photon imaging of activity of GCaMP6-labeled neurons deep in intact mouse brain. Nat Methods 14CrossRefGoogle Scholar
  11. 11.
    Pennisi M, Rajput A-M, Toldo L, Pappalardo F (2013) Agent based modeling of tregteff cross regulation in relapsing-remitting multiple sclerosis. BMC Bioinformatics 14:Google Scholar
  12. 12.
    Piaton G et al (2011) Class 3 semaphorins influence oligodendrocyte precursor recruitment and remyelination in adult central nervous system. Brain 134(4):1156–1167CrossRefGoogle Scholar
  13. 13.
    Plotkin GD (2004) The origins of structural operational semantics. J Log Algebr Program 60:3–15MathSciNetCrossRefGoogle Scholar
  14. 14.
    Stacpoole SRL, Spitzer S, Bilican B, Compston A, Karadottir R, Chandran S, Franklin RJM (2013) High yields of oligodendrocyte lineage cells from human embryonic stem cells at physiological oxygen tensions for evaluation of translational biology. Stem Cell Rep 1(5):437–450CrossRefGoogle Scholar
  15. 15.
    Taschler B, Ge T, Bendfeldt K, Müller-Lenke N, Johnson TD, Nichols TE (2014) Spatial modeling of multiple sclerosis for disease subtype prediction. MICCAI 2014:797–804Google Scholar
  16. 16.
    Velez de Mendizabal N, Hutmacher MM, Troconiz IF, Goñi J, Villoslada P, Bagnato F, Bies RR (2013) Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach. PLOS ONE 9(1):1–11Google Scholar
  17. 17.
    Veloso M (2013) An agent-based simulation model for informed shared decision making in multiple sclerosis. Mult Scler Relat Disord 2:377–384CrossRefGoogle Scholar
  18. 18.
    Wilensky U, NetLogo Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL.
  19. 19.
    Williams A, Piaton G, Aigrot M, Belhadi A, Théaudin M, Petermann F, Thomas J, Zalc B, Lubetzki C (2007) Semaphorin 3A and 3F: key players in myelin repair in multiple sclerosis? Brain 130(10):2554–2565CrossRefGoogle Scholar
  20. 20.
    Zhang H, Jarjour A, Boyd A, Williams A (2011) Central nervous system remyelination in culture - a tool for multiple sclerosis research. Exp Neurol 230(1):138–148CrossRefGoogle Scholar
  21. 21.
    Zheng W, Li Q, Zhao C, Da Y, Zhang H, Chen Z (2018) Differentiation of glial cells from hiPSCs: potential applications in neurological diseases and cell replacement therapy. Front Cell NeurosciGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ludovica Luisa Vissat
    • 1
    Email author
  • Jane Hillston
    • 1
  • Anna Williams
    • 2
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.MRC-Centre for Regenerative MedicineUniversity of EdinburghEdinburghUK

Personalised recommendations