Assignments

Mathematical Simulation of Mechanisms in Immune-mediated Polyneuropathies (Master Assignment)

Mathematical Simulation of Mechanisms in Immune-mediated Polyneuropathies (Master Assignment)

Background

Immune-mediated polyneuropathies are disorders in which the body generates antibodies against its own nerves. Because the nerves in arms and legs have to convey impulses to mediate muscle movement or skin sensation, the resulting nerve damage causes loss of muscle power, loss of sensation, or both. Although the antibodies play a role in the initial attack against nerves, the downstream mechanisms leading to the actual nerve dysfunction are not well understood. Gaining more knowledge about these mechanisms is essential because treatment directed against the antibodies themselves cannot prevent the gradual development of irreversible nerve damage and is not always successful. Thus, if a specific downstream mechanism is known, it might be modified by pharmacological treatment in order to prevent irreversible nerve damage. This project will explore the validity of biophysical mechanisms that are currently postulated in the neurological literature but for which evidence has not been, or cannot be obtained in experimental studies [1]. These putative mechanisms will be explored for two specific polyneuropathies: multifocal motor neuropathy (MMN) and anti-myelin associated neuropathy (anti-MAG neuropathy) [2].

Task

You will model biophysical characteristics of a single myelinated human axon. Model characteristics include: ionic concentration gradients, ionic fluxes through ion-channels and pumps in the axon membrane, and electrical conduction properties of the axon core and myelin sheath. First, the model described by Nygren and Halter [3] should be reproduced. Second, elements of the Stephanova models [4,5] are included in order to describe myelin sheath properties as accurately as possible. Third, additional axonal ion-channel properties are added which were not described in the Nygren and Halter model. After each of these three steps, the stability of the model is tested by assessing if it generates stable impulse propagation. The validity of the final model is assessed by application of various input currents to the axon model and whether it will reproduce experimental data of excitability studies in healthy humans and patients [6].

Personnel requested

For this assignment, we seek a master or PhD student with a background in Applied Physics, Applied Mathematics or Biomedical Engineering, with a strong interest in neuroscience, biophysics and computational modeling. The student should be familiar with the implementation of ordinary and partial differential equations in Matlab. This project has been initiated and proposed by dr Hessel Franssen, neurologist/clinical neurophysiologist, Maria Kovalchuk MD, and dr ir. Boudewijn Sleutjes, all from the University Medical Center Utrecht, and will be performed as a collaboration with the Clinical Neurophysiology group at the University of Twente.

Literature

  1. Franssen H, and Straver, D.C.G. Pathophysiology of immune-mediated demyelinating neuropathies – Part I: Neuroscience. Muscle & Nerve 48: 851 – 864, 2013.
  2. Franssen H, and Straver, D.C.G. Pathophysiology of immune-mediated demyelinating neuropathies – Part II: Neurology. Muscle & Nerve 49: 4-20, 2014.
  3. Nygren A. and Halter J.A. A General Approach to Modeling Conduction and Concentration Dynamics in Excitable Cells of Concentric Cylindrical Geometry, J theor Biol 199: 329-358, 1999.
  4. Stephanova D.I. and Bostock H. A distributed parameter model of the myelinated human motor nerve: temporal and spatial distributions of action potentials and ionic currents, Biol Cybern 73: 275-280, 1995.
  5. Stephanova D.I. and Bostock H. A distributed parameter model of the myelinated human motor nerve: temporal and spatial distributions of electrotonic potentials and ionic currents. Biol Cybern 74: 543-547, 1996.
  6. Cameron C et al. Modeling the Excitability of Mammalian Nerve Fibers: Influence of Afterpotentials on the Recovery Cycle, J Neurophysiol 87: 995-1006,2002.

For more information, please contact: H.Franssen@umcutrecht.nl, B.Sleutjes@umcutrecht.nl or m.j.a.m.vanputten@utwente.nl