Morphogenetic systems: Models and experiments
M systems are mathematical models of morphogenesis developed to gain insights into its relations to phenomena such as self-assembly, self-controlled growth, homeostasis, self-healing and self-reproduction, in both natural and artificial systems. M systems rely on basic principles of membrane computing and self-assembly, as well as explicit emphasis on geometrical structures (location and shape) in 2D, 3D or higher dimensional Euclidean spaces. They can be used for principled studies of these phenomena, both theoretically and experimentally, at a computational level abstracted from their detailed implementation. In particular, they afford 2D and 3D models to explore biological morphogenetic processes. Theoretical studies have shown that M systems are powerful tools (e.g., computational universal, i.e. can become as complex as any computer program) and their parallelism allows for trading space for time in solving efficiently problems considered infeasible on conventional computers (NP-hard problems). In addition, they can also exhibit properties such as robustness to injuries and degrees of self-healing. This paper focuses on the experimental side of M systems. To this end, we have developed a high-level morphogenetic simulator, Cytos, to implement and visualize M systems in silico in order to verify theoretical results and facilitate research in M systems. We summarize the software package and make a brief comparison with some other simulators of membrane systems. The core of the article is a description of a range of experiments inspired by aspects of morphogenesis in both prokaryotic and eukaryotic cells. The experiments explore the regulatory role of the septum and of the cytoskeleton in cell fission, the robustness of cell models against injuries, and, finally, the impact of changing nutrient concentration on population growth.
Smolka, V., Drastík, J., Bradík, J., Garzon, M., & Sosík, P. (2020). Morphogenetic systems: Models and experiments. BioSystems, 198 https://doi.org/10.1016/j.biosystems.2020.104270