This image shows Thomas Klotz

Thomas Klotz

Dr.-Ing.

Research Associate

Contact

+49 711 685 66216

Pfaffenwaldring 5a
70569 Stuttgart
Deutschland
Room: 02.015

Subject

  • Analysis of muscel signals, e.g., electromyography or magnetomyography
  • Multiscale modelling of the neuromuscular system
  • Applications in neuromuscular phyisiology

Summary

Motion is a defining feature of human life. The complex interplay of the central nervous system, sensory organs, and contracting skeletal muscles enables the amazing variability of human motion. However, existing methodologies (both experimental and theoretical) provide only limited insights for uncovering the function of the neuromuscular system. This also limits the availability and development of treatments for the diverse class of neuromuscular disorders.

My research focuses on integrating experimental and computational methods to study the neuromuscular system. This includes the development of new computer simulation tools that allow testing hypotheses on neuromuscular (patho)physiology, enriching experimental data through model predictions, and developing new methods to analyze muscle signals.

 

Publications:
  1. Schmid, L., Klotz, T., Röhrle, O., Powers, R. K., Negro, F., & Yavuz, U. Ş. (2024). Postinhibitory excitation in motoneurons can be facilitated by hyperpolarization-activated inward currents: A simulation study. PLOS Computational Biology, 20, Article 1. https://doi.org/10.1371/journal.pcbi.1011487
  2. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2024). OpenDiHu: an efficient and scalable framework for biophysical simulations of the neuromuscular system. Journal of Computational Science, 79, 102291.
  3. Klotz, T., Lehmann, L., Negro, F., & Röhrle, O. (2023). High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ace7f7
  4. Saini, H., Klotz, T., & Röhrle, O. (2023). Modelling motor units in 3D: Influence on muscle contraction and joint force via a proof of concept simulation. Biomechanics and Modeling in Mechanobiology, 22, Article 2. https://doi.org/10.1007/s10237-022-01666-2
  5. Zhang, C., Zhang, J., Widmann, M., Benke, M., Kübler, M., Dasari, D., Klotz, T., Gizzi, L., Röhrle, O., Brenner, P., & Wrachtrup, J. (2023). Optimizing NV magnetometry for Magnetoneurography and Magnetomyography applications. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.1034391
  6. Klotz, T., Gizzi, L., & Röhrle, O. (2022). Investigating the spatial resolution of EMG and MMG based on a systemic multi-scale model. Biomechanics and Modeling in Mechanobiology, 21, 983–997. https://doi.org/10.1007/s10237-022-01572-7
  7. Schmid, L., Klotz, T., Yavuz, U. Ş., Maltenfort, M., & Röhrle, O. (2022). Spindle Model Responsive to Mixed Fusimotor Inputs: an updated version of the Maltenfort and Burke (2003) model. Physiome. https://doi.org/10.36903/physiome.19070171.v2
  8. Klotz, T., Bleiler, C., & Röhrle, O. (2021). A Physiology-Guided Classification of Active-Stress and Active-Strain Approaches for Continuum-Mechanical Modeling of Skeletal Muscle Tissue. Frontiers in Physiology, 12, 1–13. https://doi.org/10.3389/fphys.2021.685531
  9. Emamy, N., Litty, P., Klotz, T., Mehl, M., & Röhrle, O. (2020). POD-DEIM Model Order Reduction for the Monodomain Reaction-Diffusion Sub-Model of the Neuro-Muscular System. IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22--25, 2018, 177–190. https://doi.org/10.1007/978-3-030-21013-7_13
  10. Klotz, T., Gizzi, L., Yavuz, U., & Röhrle, O. (2020). Modelling the electrical activity of skeletal muscle tissue using a multi-domain approach. Biomechanics and Modelling in Mechanobiology, 19, 335–349. https://doi.org/10.1007/s10237-019-01214-5
  11. Schmid, L., Klotz, T., Siebert, T., & Röhrle, O. (2019). Simulating electromechanical delay across the scales--relating the behavior of single sarcomers on a sub-cellular scale and the muscle-tendon system on the organ scale. Pamm, 19, Article 1. https://doi.org/10.1002/pamm.201900312
  12. Schmid, L., Klotz, T., Siebert, T., & Röhrle, O. (2019). Characterization of electromechanical delay based on a biophysical multi-scale skeletal muscle model. Frontiers in Physiology, 10, 1270. https://doi.org/10.3389/fphys.2019.01270
  13. Röhrle, O., Yavuz, U. S., Klotz, T., Negro, F., & Heidlauf, T. (2019). Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, e1457. https://doi.org/10.1002/wsbm.1457
  14. Bradley, C. P., Emamy, N., Ertl, T., Göddeke, D., Hessenthaler, A., Klotz, T., Krämer, A., Krone, M., Maier, B., Mehl, M., Rau, T., & Röhrle, O. (2018). Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems. Frontiers in Physiology, 9. https://doi.org/10.3389/fphys.2018.00816
  15. Saini, H., Altan, E., Ramasamy, E., Klotz, T., Gizzi, L., & Röhrle, O. (2018). Predicting Skeletal Muscle Force from Motor-Unit Activity using a 3D FE Model. Pamm, 18, Article 1.
  16. Heidlauf, T., Klotz, T., Rode, C., Siebert, T., & Röhrle, O. (2017). A continuum-mechanical skeletal muscle model including actin-titin interaction predicts stable contractions on the descending limb of the force-length relation. PLoS Computational Biology, 13, Article 10. https://doi.org/10.1371/journal.pcbi.1005773
  17. Heidlauf, T., Klotz, T., Rode, C., Siebert, T., & Röhrle, O. (2016). Force enhancement and stability of finite element muscle models. Pamm, 16, Article 1.
  18. Heidlauf, T., Klotz, T., Rode, C., Altan, E., Bleiler, C., Siebert, T., & Röhrle, O. (2016). A multi-scale continuum model of skeletal muscle mechanics predicting force enhancement based on actin--titin interaction. Biomechanics and Modeling in Mechanobiology, 15, Article 6. https://doi.org/10.1007/s10237-016-0772-7
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