P276 Simulation tools for reaction-diffusion modeling
Saana Seppälä*1, Laura Keto1, Derek Ndubuaku1, Annika Mäki1, Tuomo Mäki-Marttunen1, Marja-Leena Linne1, Tiina Manninen1
1Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
*Email: saana.seppala@tuni.fi
Introduction
With advancements in computing power and cellular biology, the simulation of reaction-diffusion systems has gained increasing attention, leading to the development of various simulation algorithms.While we have experience testing separate tools for cell signaling and neuronal networks [1–4], we have not extensively evaluated cell-level tools that integrate both reaction and diffusion algorithms or support co-simulation. This study aims to provide a comprehensive assessment of reaction-diffusion and co-simulation tools, including NEURON [5], ASTRO [6], and NeuroRD [7], to determine their suitability for our research needs.
Methods
Most available reaction-diffusion algorithms and tools are based on partial differential equations or the reaction-diffusion master equation simulated using an extended Gillespie stochastic simulation algorithm [8]. In this study, we implement identical diffusion, reaction, and reaction-diffusion models across selected tools, testing both simple and complex cell morphologies. We conduct simulations, compare results across different tools, and evaluate their consistency. Additionally, we assess the usability and suitability of each tool in various simulation settings, including ease of implementing cell morphologies and equations, computational efficiency, and support for co-simulation.
Results
The simulation algorithms and tools vary significantly in usability and functionality. For instance, some tools support realistic cell morphologies, while others are limited to simplified geometries such as cylinders. Additionally, not all tools allow implementation of reactions involving three reactants, restricting their applicability for certain biological simulations. Despite these differences, a comparison of simulation results across the tools reveals a high degree of similarity, indicating that the underlying models produce consistent outcomes. Furthermore, variations in computational efficiency and ease of implementation are observed, highlighting trade-offs between flexibility, accuracy, and usability across the tools.
Discussion
A thorough understanding of the properties and capabilities of different reaction-diffusion simulation tools is essential for developing more advanced and biologically accurate models. Evaluating these tools provides valuable insights into their strengths and limitations, facilitating the integration of multiple simulation approaches. In particular, this knowledge enables the development of co-simulations that combine reaction diffusion models with spiking network simulations, enhancing the accuracy and scope of computational neuroscience research.
Acknowledgements
This work was supported by the Research Council of Finland (decision numbers 330776, 355256 and 358049), the European Union's Horizon Programme under the Specific Grant Agreement No. 101147319 (EBRAINS 2.0 Project), and the Doctoral School at Tampere University.
References
1.https://doi.org/10.1093/bioinformatics/bti018
2.https://doi.org/10.1155/2011/797250
3.https://doi.org/10.3389/fninf.2018.00020
4.https://doi.org/10.1007/978-3-030-89439-9_4
5.https://doi.org/10.1017/CBO9780511541612
6.https://doi.org/10.1038/s41467-018-05896-w
7.https://doi.org/10.1371/journal.pone.0011725
8.https://doi.org/10.1021/j100540a008
