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Collective Behaviour and Swarm Intelligence in Slime Moulds FEMS Microbiology Reviews, fuw033, 40, 2016, 798–806 doi: 10.1093/femsre/fuw033 Advance Access Publication Date: 29 August 2016 Review Article REVIEW ARTICLE Collective behaviour and swarm intelligence Downloaded from https://academic.oup.com/femsre/article-abstract/40/6/798/2400841 by guest on 10 January 2019 in slime moulds Chris R. Reid1,2 and Tanya Latty2,∗ 1Department of Biological Sciences, Macquarie University, Sydney, NSW, 2109 and 2School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2015, Australia ∗Corresponding author: School of Life and Environmental Sciences, University of Sydney, Macleay Building A12, Sydney, NSW 2015, Australia. Tel: +61 2 8627 1122; E-mail: [email protected] One sentence summary: Slime moulds are powerful model systems for studying collective behaviour and swarm intelligence. Editor: Karine Gibbs ABSTRACT The study of collective behaviour aims to understand how individual-level behaviours can lead to complex group-level patterns. Collective behaviour has primarily been studied in animal groups such as colonies of insects, flocks of birds and schools of fish. Although less studied, collective behaviour also occurs in microorganisms. Here, we argue that slime moulds are powerful model systems for solving several outstanding questions in collective behaviour. In particular, slime mould may hold the key to linking individual-level mechanisms to colony-level behaviours. Using well-established principles of collective animal behaviour as a framework, we discuss the extent to which slime mould collectives are comparable to animal groups, and we highlight some potentially fruitful areas for future research. Keywords: Physarum polycephalum; Dictyostelium discoideum; unicellular communication; problem solving COLLECTIVE BEHAVIOUR AND SWARM pre-designed blueprints to guide their actions and individuals INTELLIGENCE possess only information from the local area around them (Camazine et al. 2001). Communication is the key that allows The field of collective behaviour aims to understand how the group to transfer information in order to collectively sense simple behavioural interactions between group members lead its environment, navigate through space and build structures to coherent, sophisticated behaviour at the collective level. in ways that would be impossible for individuals to achieve on Collective behaviour is apparent at all levels of biological organ- their own. isation; bacteria act together to form rafts (Kearns 2010), Swarm intelligence is the collective intelligence that emerges plague-locusts march cohesively in bands (Bazazi et al. at the group level from the interactions between individuals, al- 2008), tiny termites build immense, sophisticated structures lowing the group to generate solutions to cognitive problems (Bonabeau et al. 1997; King, Ocko and Mahadevan 2015), swarms that are not available to individuals (Bonabeau, Dorigo and of honeybees ‘vote’ democratically for the location of their new Theraulaz 1999; Krause et al. 2011). In swarm intelligent sys- home (Camazine et al. 1999; Seeley 2010; Diwold et al. 2011)and tems, problem solving and decision making are decentralised flocks of starlings collectively sense the direction of a predator’s – there is no centralised information processing centre. Swarm attack by compression waves propagating through the group intelligence has perhaps been best studied in the social insects (Procaccini et al. 2011). All of these groups are governed by where there are many examples of collective problem solving. the principles of self-organisation; control of the system is The example of an ant colony solving a maze is a perfect il- distributed, there are no leaders to command the group or lustration of swarm intelligence at work (see Box 1). The same Received: 15 March 2016; Accepted: 19 July 2016 C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected] 798 Reid and Latty 799 Box 1. Ant colonies solve mazes using swarm intelligence Swarm intelligence allows groups of organisms to solve problems that exceed the cognitive capabilities of individu- als. Consider the case of an ant colony solving a maze. Many ants begin to walk the maze at random, depositing a trail of volatile pheromones behind them. The ant traffic which happens to flow along the shortest path of the maze can make more return trips (and hence lay more pheromone) over the same time period than ants which happen to walk longer paths. Ants follow the strongest pheromone trail, de- Downloaded from https://academic.oup.com/femsre/article-abstract/40/6/798/2400841 by guest on 10 January 2019 positing their own trail as they go, leading to a process of positive feedback that focuses the colony’s traffic on the shortest path. At the same time, the volatile pheromone evaporates at a constant rate, allowing the colony to ‘forget’ the other paths in the maze. A single ant in a large maze is incapable of zeroing in on the shortest path—as it walks the maze randomly, its pheromone trail evaporates before it can be followed and reinforced by future visits (Sumpter 2006). Figure 1. Life cycle of a cellular slime mould such as D. discoideum. (a) Free-living, Even small colonies of ants can be unable to establish an individual haploid amoebae. (b) Aggregation of amoebae. (c) Mound formation. effective pheromone trail (Beekman, Sumpter and Ratnieks (d) Emergence and elongation of the tip section. Elongation continues until the 2001). Only by coordination as a large group, mediated structure falls over to form the migrating slug in (e). (f) ‘Mexican hat’. (g) Develop- ing fruiting body. (h) Mature fruiting body with live spore cells atop the dead cells by communication through chemical signals, can the ants forming the stalk. (i) Dispersing spores. (Drawing courtesy of E.J.T. Middleton) solve the problem, thereby exhibiting swarm intelligence. to communication and cooperation between parts of the same multi-nucleate cell. phenomenon is observed in slime moulds. The interest in slime moulds as model organisms for studies of collective behaviour and swarm intelligence initiated largely from work by Nakagaki, CELLULAR SLIME MOULDS (CLASS Yamada and Toth´ (2000) and Nakagaki (2001) demonstrating that DICTYOSTELIA) the slime mould Physarum polycephalum was capable of finding the shortest path through a labyrinth maze. When a large slime Cellular slime moulds spend the majority of their life cycle as mould was allowed to build a network covering all surfaces of a independent, single entities capable of foraging, growing and maze, with food sources placed at the entrance and exit of the dividing. If conditions deteriorate, individual cells aggregate to maze, the slime mould redistributed its biomass to allow it to ef- form a mobile multicellular ‘slug’ that can crawl to the soil ficiently colonise and exploit the resources. Via communication surface before undergoing a remarkable transformation into a between all of its parts, the slime mould withdrew from dead fruiting body containing many spores. Collective behaviour is ends and longer paths until a single tubule connected the food a vital component in the migration and reproduction of cellu- sources along the shortest path through the maze. This path was lar slime moulds. Formerly free-living individuals communicate one of the four possible solutions, and since Nakagaki and col- with each other and coordinate towards a specific goal: to find leagues pioneering work, subsequent studies have dramatically and sporulate in conditions favourable to spore survival and dis- increased the complexity of the maze problem. Using a Tow- persal. The process is coordinated primarily by cAMP signalling; ers of Hanoi-inspired maze of 32 678 unique solutions, only 2 although each cell is initially a free-living individual, waves of of which were optimal paths, Reid and Beekman (2013)showed cAMP coordinate and synchronise amoeba behaviour. that Physarum could find the optimal solution to mazes ofex- treme difficulty. In these cases just as in ant colonies, the slime PLASMODIAL SLIME MOULDS (CLASS mould as a whole begins with global knowledge of all paths MYXOGASTRIA) through the maze and settles on the maze solution by interac- tions between its constituent units. Though it has never been ex- Plasmodial slime moulds are multinucleate unicells capable of plicitly tested, it is unlikely that a small slime mould, in the ab- growing to over 900 cm2 in size during their vegetative life stage, sence of any navigational cues, could begin to explore the maze which is known as a plasmodium. These giant cells move in at random and eventually settle upon the shortest path. Thus, an amoeboid fashion, extending pseudopodia to explore the slime moulds too exhibit swarm intelligence. environment at speeds of up to 5 cm/hr (Kessler 1982). The Here we discuss collective behaviour and swarm intelligence general morphology of the most commonly studied plasmodial in two slime mould taxa: cellular and plasmodial slime moulds. slime mould Physarum polycephalum is shown in Figs 2 and 3, While both groups are formally classified as slime moulds where the exploratory ‘search front’ advances in a dense fan (Mycetozoa), they differ substantially in their organisation, com- shape (Dove and Rusch 1980). Cell morphology behind the search munication mechanisms and life history (each outlined in Figs 1 front, and between multiple colonised food sources, is self- and 2). They also differ in the way in which collective behaviour organised into a system of intersecting tubules where cytoplasm manifests. In the cellular slime moulds, collective behaviour is rhythmically streamed back and forth to circulate chemical refers to cooperation between individual cells within a swarm, signals, nutrients and information (Collins and Haskins 1972). while in plasmodial slime moulds, collective behaviour refers Plasmodial and cellular slime moulds offer insight into two 800 FEMS Microbiology Reviews, 2016, Vol. 40, No. 6 Downloaded from https://academic.oup.com/femsre/article-abstract/40/6/798/2400841 by guest on 10 January 2019 Figure 2. Life cycle of a plasmodial slime mould such as P. polycephalum. (a) Germinating spore.
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