Mycelial Networks: Agent-Based Modelling of Growth and Interaction

Encadrants : 

Ada Diaconescu (4D49), Payam Zahadat, IT-University of Copenhagen, Kirstie Bellman, Topcy House Co.

Occurrences : 

2021

Nombre d'étudiants minimum: 

2

Nombre d'étudiants maximum: 

5

Nombre d'instances : 

2

Faisable à distance: 

Oui

The purpose of this project is to develop an agent-based model (ABM) that simulates the growth of mycelial networks, as well as their interactions with other networks (of the same species, or of different species).

Mycelium are the vegetative part of fungus, consisting of numerous, branching, thread-like hyphae. They are commonly known for their produced fruiting bodies, e.g., mushrooms. Underground, they form networks that range from sizes that are too small to see, all the way up to covering many square kilometres. During growth, hyphae may extend and branch from their tips, create lateral branching from existing hyphae, or reconnect with other hyphae tips or filaments (forming loops). The network may grow in an exploratory manner (e.g. symmetrically from a central point) in search for resources. Once a resource is found, the network may grow stronger in the direction of that resource and possibly shrink in other directions. When encountering other organisms (e.g. other mycelium or live plant roots), mycelium may connect to these and form cooperative, symbiotic or parasitic relations.

The project will proceed in several stages, with increasing degree of complexity:

1. Single mycelium, single resource: Simulate a single mycelium, growing from a single spore (e.g. at the centre of a 2D simulation space), where a constant food resource is placed. This involves modelling the basic growth behaviour (extending, branching, merging, halting). The simulated network should grow more or less symmetrically from the centre. Growth may slow down and halt with the network size, as resources from the centre are increasingly difficult to transport to the tips.

2. Single mycelium, several static persistent resources: Extend the simulation to include additional resources (e.g. food sources). When the mycelium encounters such sources, it grows more vigorously towards that source than in other directions. It may also halt growth in other directions, or even dismantle grown hyphae and reuse their resources. Compare the resulting network topology to the previous one (step 1).

3. Single mycelium, several dynamic transient resources: Extend the simulation to support resources appearing randomly and diminishing when being consumed, or over time.

4. Several mycelium, single species: Introduce several mycelial networks that grow simultaneously in the same environment. When they meet, they may avoid each other; or merge and exchange resources. As resources are temporary and randomly distributed, a comparison of the two cases - avoidance or collaboration - will indicate which strategy is more beneficial within each context.

5. Mycelium specialisation for several types of resources: Extend the above to include different types of resources, all needed for mycelium growth. Also include different mycelium species, each one being more efficient at extracting one resource type than other types. Carry-out the same comparison, between cooperative and avoiding strategies, employed by the mycelium species.

6. Tree symbiosis: Include tree roots as a new species, providing a new type of rare resource, in exchange for resources collected by mycelium.

NOTE: you may not have sufficient time to develop all steps above. The only important aspect is the quality of the simulation, experiments and analysis produced; for whatever number of steps above.

 

Potential applications of knowledge obtained from this kind of study include the construction, self-integration and self-adaptation of peer-to-peer (P2P) networks, depending on information, computing and communication resources. It may also apply for growing and evolving (artificial) neural networks and streaming systems.

Implementation platform: NetLogo or Java (e.g. with Processes graphical libraries).

References (available upon request):

Basic tree-growth model - consider reusing some of the equations: Payam Zahadat, Ada Diaconescu, "Reactive or Stable: A Plant-inspired Approach for Business Organisation Morphogenesis" Intl. Conf. on Artificial Life (ALife), 13-18 July 2020, Montreal, CA (link)

Basic model based on differential equations of 1D hyphae growth - may help comprehension and provide inspiration: Edelstein L. 1982. "The propagation of fungal colonies: a model for tissue-growth". J Theor Biol 98:679–701.

Comprehensive multi-scale study of mycelial network growth -- mostly relevant for the final sections discussing graph representations and other network models: Mark D. Fricker, Luke L. M. Heaton,

Nick S. Jones, and Lynne Boddy, "The Mycelium as a Network", in The Fungal Kingdom, Edited by J. Heitman, B. J. Howlett, P. W. Crous, E. H. Stukenbrock, T. Y. James, and N. A. R. Gow, 2018 American Society for Microbiology, Washington, DC, doi:10.1128/microbiolspec.FUNK-0033-2017

Example application of mycelial network growth and self-adaptation to P2P networks: Paul L. Snyder, Rachel Greenstadt and Giuseppe Valetto, "Myconet: A Fungi-inspired Model for Superpeer-based Peer-to-Peer Overlay Topologies", IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2009