How Do Slime Mould Search?

Research areas

Temporary Supervisor

Professor Sylvie Thiebaux


This is a joint project with Dr. Tanya Latty, entomologist at the University of Sydney. We are interested in understanding how a slime mould navigate and searches for food in a complex environment. Slime moulds are giant single cells amoeba which move through their environments as flowing networks of slime. Despite lacking a brain, slime moulds are capable of surprisingly complex behaviors such as finding the shortest path through a maze and making tradeoffs between food quality and risk. Little is known about the mechanisms that underlie the ability of slime moulds to navigate in environments that can contain barriers and even dead-ends, more than one food source, food of variable quality, and regions of various costs (e.g. illuminated regions which are damaging to them).


The goal of this project will be to discover the strategies used by slime moulds. We will start by using reinforcement learning, search and/or optimisation to investigate whether known search strategies in the field of artificial intelligence (or their combination) can explain the behaviors that we observe experimentally. We will be able to set up live experiments with these weird things to test and further our hypotheses.


This project is best suited to a student with a computer science or software engineering background, with a keen interest in biology and artificial intelligence. Alternatively, it could be suitable for a biology student with a strong computer science course component (e.g. via a double degree) and adequate progamming skills.

Background Literature

(None of this is required reading before commencing the project) (extended list available upon request) Background on artificial intelligence and search: Russel, S. and Norvig, P. (2009) Artificial Intelligence: A Modern Approach. Pearson Education Ltd. Edelkamp, S. and Schroedl, S. (2012) Heuristic Search: Theory and Applications. Elsevier. Background on slime moulds navigation and decision making: Ma, Qi, et al. "Current-reinforced random walks for constructing transport networks." Journal of The Royal Society Interface 10.80 (2013): 20120864. Nakagaki, T., Yamada, H. & Tóth, Á. (2000) Maze-solving by an amoeboid organism. Nature, 407, 470. Reid, C.R., Latty, T., Dussutour, A. & Beekman, M. (2012) Slime mold uses an externalized spatial “memory” to navigate in complex environments. Proceedings of the National Academy of Sciences, 109, 17490-17494. <http:"" content="" 109="" 43="" 17490.abstract"="">


The student will gain an understanding of search strategies in artificial intelligence, of certain optimisation methods, and of navigation and decision-making by simple biological organisms. Moreover, the student will have the possibility of interacting with partners at University of Sydney.

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