Software

STEPS big logo

STEPS

STEPS is a package for exact stochastic simulation of reaction-diffusion systems in arbitrarily complex 3D geometries. Our core simulation algorithm is an implementation of Gillespie's SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh.

While it was mainly developed for simulating detailed models of neuronal signaling pathways in stretches of dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role.

We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analysing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution.

An MPI-based parallel version is now available. More recently, we expanded STEPS to enable full nanoscale modeling of neurons and synapses on distributed meshes: electrophysiology (membrane potential), molecular reactions and support to model the vesicle cycle.

For more information and downloads, please visit our SourceForge site.

Pycabnn

Pycannbn

Pycabbn is an open-source software tool that is dedicated to generating an anatomical model, which serves as the basis of a full network model. In pycabnn, we implemented efficient algorithms for generating physiologically realistic cell positions and for determining connectivity based on extended geometrical structures such as axonal and dendritic morphology.

Pycabnn is efficient enough to carry out all the required tasks on a laptop computer within reasonable runtime, although it can also run in a parallel computing environment. Written purely in Python with limited external dependencies, pycabnn is easy to use and extend. Pycabnn can be downloaded here.

NeuroDevSim logo

NeuroDevSim

NeuroDevSim is a Python package to simulate neural development as the growth, migration, etc. of many  neuronal morphologies simultaneously in a microcircuit. For more information and downloads, please visit our github site. It is a more performant and easier to use successor to our NeuroMaC software described in Torben-Nielsen and De Schutter 2014, with many new features.

References

  1. I. Hepburn, S. Wils, W. Chen and E. De Schutter: STEPS: Efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Systems Biology 6: 36 (2012). 
  2. I. Hepburn, W. Chen and E. De Schutter: Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations. Journal of Chemical Physics 145: 054118 (2016).
  3. W. Chen and E. De Schutter: Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers. Frontiers in Neuroinformatics 11: 13 (2017).
  4. W. Chen, T. Carel, O. Awile, N. Cantarutti, G. Castiglioni, A. Cattabiani, B. Del Marmol, I. Hepburn, J.G. King, C. Kotsalos, P. Kumbhar, J. Lallouette, S. Melchior, F Schürmann and E. De Schutter: STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale. Frontiers in Neuroinformatics 16: 883742 (2022).
  5. I. Hepburn, J. Lallouette, W. Chen, A.R. Gallimore, S.Y. Nagasawa-Soeda and E. De Schutter: Vesicle and reaction-diffusion hybrid modeling with STEPS. Communications Biology 7: 573 (2024)
  6. F. Santamaria, S. Wils, E. De Schutter and G.J. Augustine: Anomalous diffusion in Purkinje cell dendrites caused by dendritic spines. Neuron 52: 635-648 (2006).
  7. A.R. Gallimore, I. Hepburn, S. Rizzoli and E. De Schutter: Dynamic Regulation of Vesicle Pools in a Detailed Spatial Model of the Complete Synaptic Vesicle Cycle. Preprint.
  8. A.R. Gallimore, T. Kim, K. Tanaka-Yamamoto and E. De Schutter: Switching on depression and potentiation in the cerebellum. Cell Reports 22: 722-733 (2018).
  9. E. De Schutter and J.M. Bower: An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice.  Journal of Neurophysiology 71: 375-400 (1994).
  10. V. Steuber, W. Mittmann, F.E. Hoebeek, R.A. Silver, C.I. De Zeeuw, M. Häusser and E. De Schutter: Cerebellar LTD and pattern recognition by Purkinje cells. Neuron 54: 121–136 (2007).
  11. Y. Zang, S. Dieudonné and E. De Schutter: Voltage- and Branch-specific Climbing Fiber Responses in Purkinje Cells. Cell Reports 24: 1536–1549 (2018). 
  12. Y. Zang, S. Hong and E. De Schutter: Firing rate-dependent phase responses of Purkinje cells support transient oscillations. eLife 9: 60692 (2020).
  13. Y. Zang and E. De Schutter: The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells. Journal of Neuroscience 41: 1850-1863 (2021).
  14. H. Anwar*,  I Hepburn*, H. Nedelescu, W. Chen and E. De Schutter: Stochastic calcium mechanisms cause dendritic calcium spike variability. Journal of Neuroscience: 33: 15848-15867 (2013).
  15. W. Chen and E. De Schutter: Time to bring single neuron modeling into 3D. Neuroinformatics 15: 1-3 (2017)
  16. A. Denizot, M. Arizono, V.U. Nägerl, H. Berry and E. De Schutter: Control of Ca2+ signals by astrocyte nanoscale morphology at tripartite synapses. Glia 70: 2378-2391 (2022).
  17. S. Hong, S. Ratté, S. Prescott and E. De Schutter:Single neuron firing properties impact correlation-based population coding. Journal of Neuroscience 32:1413–1428 (2012).
  18. S. Hong, E. De Schutter and S.A. Prescott: Impact of neuronal properties on network coding: Roles of spike initiation dynamics and robust synchrony transfer. Neuron 78: 758-772 (2013).
  19. E. De Schutter: Efficient simulation of neural development using shared memory parallelization. Frontiers in Neuroinformatics 17:1212384 (2023).
  20. B. Torben-Nielsen and  E. De Schutter: Context-aware modeling of neuronal morphologies. Frontiers in Neuroanatomy 8: 92 (2014).  
  21. M. Kato and E. De Schutter: Models of Purkinje cell dendritic tree selection during early cerebellar development. PLoS Computational Biology 19: e1011320 (2023).
  22. R. Maex and E. De Schutter: Resonant synchronization in heterogeneous networks of inhibitory neurons. Journal of Neuroscience 23: 10503-10514 (2003). 
  23. S.K. Sudhakar, S. Hong, I. Raikov, R. Publio, C. Lang, T. Close, D. Guo, M. Negrello and E. De Schutter: Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer. PLoS Computational Biology 13: e1005754 (2017). 
  24. S.-L. Shin, F.E. Hoebeek, M. Schonewille, C.I. De Zeeuw, A. Aertsen and E. De Schutter: Regular temporal patterns in cerebellar Purkinje cell simple spike trains. PLoS One 2: e485 (2007).
  25. P. Warnaar, J. Couto, M. Negrello, M. Junker, A. Smilgin, A. Ignashchenkova, M. Giugliano, P. Thier and E. De Schutter: Duration of Purkinje cell complex spikes increases with their firing frequency. Frontiers in Cellular Neuroscience 9: 122 (2015).
  26. S. Hong, M. Negrello, M. Junker, A. Smilgin, P. Thier and E. De Schutter: Multiplexed coding by cerebellar Purkinje neurons. eLife 5: e13810 (2016).
  27. A. Markanday*, S. Hong*, J. Inoue, E. De Schutter and P. Thier: Multidimensional cerebellar computations for flexible kinematic control of movements. Nature Communications 14: 2548 (2023).