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Be your own guide: How cells solve mazes

Updated: Dec 19, 2022

London’s Hampton Court boasts one of the most famous mazes in the world, but did you know that when recreated at microscopic scale, it can be solved by single-celled organisms? Remarkably, cells can solve this and other complex maze designs, often without making wrong turns, using an elegant path-finding mechanism which allows them to effectively see around corners. This phenomenon may lend insight into the way in which cells interpret the world around them, and move where they need to go.

Chemotaxis: why, how and where cells move

Consider a white blood cell hunting down an invading pathogen, stem cells relocating to target sites to establish new tissues, or an aggressive melanoma spreading from the skin to another part of the body. Nature has these and many other examples of cells migrating in a specific direction, so it is no surprise that understanding why, how and where cells move has been the subject of many scientific studies (1,2). The directed migration of cells in response to chemical cues, termed chemotaxis, poses questions of how cells sense the environment around them, then use this information to self-organise and alter their behaviour so that they can move efficiently towards their target.

Chemotaxis can be split into three separate but linked processes (1):

  1. Gradient sensing

  2. Polarisation

  3. Motility

The latter two points concern the mechanics of how cells move. Polarisation describes the rearrangement of cell components to break the symmetry of the cell, generating a leading edge and a trailing edge, essentially the ‘front’ and ‘back’ of the cell as it moves. Motility describes the actual locomotion of cells, and is driven by various appendages or protrusions, depending on the cell type. For example, sperm cells and many bacteria use tail-like structures called flagella and cilia to propel them, while amoebae and human blood cells extend protein-rich protrusions which allow them to ‘crawl’ forwards (3). Gradient sensing on the other hand crucially underpins not just how but why and where cells move. This requires cells to interpret and amplify information from their environment.

Sensing gradients – and creating your own

Gradient sensing can be seen fundamentally as a question of information. The fact that cells can migrate specifically towards a target, and are not restricted to simply moving randomly, shows that they can read information about their surroundings (2) – but where does this information come from, and how do cells harness it to migrate efficiently? Molecularly speaking, the information is a concentration gradient of chemoattractant – any molecule which binds to receptors on the surface of the cell and stimulates migration towards it (1). Cells migrate up the chemoattractant gradient, and so the creation of this gradient is pivotal to their chemotaxis. It was previously assumed that cells simply ‘read’ a static gradient which is established independently by the source of their attractant. Indeed, modelling shows that over short distances cells can effectively interpret a static gradient of chemoattractant and move toward the source (4). However, using a static gradient, migration efficiency decreases significantly as cells approach the source of chemoattractant, because the direction of the gradient becomes harder to interpret (2). What’s more, many cell types must migrate over very long distances, and beyond the scale of 1-millimetre these static gradients can become too shallow for cells to detect.

The solution to this problem? Cells can in fact create their own gradient to follow. By breaking down the molecule to which they are attracted, cells moving towards the chemoattractant source ensure that the concentration in front of them is very high in contrast to all other directions (5, 6, 7). In doing so, the cells maintain a steep local gradient which continues to guide their migration towards the source of the attractant molecule. The cells create the gradient and move up it, continuing to shape the gradient as they follow it. Like a herd of wildebeest grazing on the plain, they deplete the local resource and move towards more abundant areas, constantly chasing the sharp boundary between abundance before them and scarcity behind. Computational modelling and experiments with different motile cell types have found that by utilising a self-generated gradient, as opposed to a static one, cells can move more efficiently and over longer distances towards a chemoattractant source (8).

Solving mazes and seeing around corners

Besides facilitating migration over long distances, perhaps the most striking implication of these dynamic gradients is that they allow cells to solve mazes. A group from the Beatson Institute in Glasgow created a computer model in which cells could break down an attractant to create their own local gradient, then placed these cells in a 2D maze with two branches prompting a binary decision between two routes (7). When each route ended in a source of attractant, the cells split 50/50 between the upper and the lower branch. However, when the maze was altered so that the upper branch resulted in a dead end, a greater proportion of the total cells made the correct decision and took the lower branch leading to the attractant source. This is because the small proportion of cells which initially chose the upper branch depleted the limited supply of attractant stored there, making the choice clearer for the other cells, which followed the steep gradient provided by the lower branch leading to the source. The shorter the upper branch, the higher the proportion of cells which made the correct choice, as the attractant reservoir within the dead end was depleted more rapidly (Figure 1). What’s more, when the upper branch was sufficiently short, cells cleared this reservoir before reaching the decision point, and therefore never made the wrong turn. Thus, by using their self-generated gradient to interpret the space in front of them, cells could essentially see around corners and make the right choice without needing to test the wrong path.

Figure 1 - Simulated decision-making by cells

Remarkably, when this model was recreated physically by constructing maze designs in a silicon-based polymer, then placing amoebae or cancer cells at one end and a chemoattractant source at the end of one of the branches, the behaviour of the cells was extremely similar to what was predicted computationally (7). Cells were able to complete mazes of increasing complexity, with success rates which were influenced by how extensive the dead end branches were. When introduced to a microscopic representation of London’s Hampton Court Palace maze, amoeba cells were able to navigate and solve its iconic twists and turns. What’s more, when a novel short-cut was carved into the design around the half-way point, the cells identified this as the new quickest route, and followed it to reach the exit. If you have ever found yourself angrily retracing your steps in a maze of hedges, or desperately seeking a short-cut to escape IKEA, you will surely agree – that’s not bad for a soil-dwelling slime mould.

From mazes to the body – the physiological relevance

So, cells can guide themselves over long distances using self-generated gradients to solve complex mazes and see around corners, all in a manner which was accurately predicted by computer modelling. While extraordinary in isolation, this also gives us fascinating clues about the pathfinding skills required of various cell types in different contexts. A white blood cell hunting down a pathogen must navigate the maze-like structure of the vessels in our circulatory system, and stem cells relocating during development must seek out distant target tissues. The use of self-generated gradients helps to explain how cells maximise the information available to them, and lead themselves where they need to go.


1. ​Iglesias, P.A. and Devreotes, P.N. (2008) ‘Navigating through models of chemotaxis’, Current Opinion in Cell Biology, 20(1), pp. 35–40. Available at:

2. ​’Hot Topic 2019: Creating a path’ - YouTube (2019). Available at:

3. Li, X. et al. (2020) ‘Excitable networks controlling cell migration during development and disease’, Seminars in Cell & Developmental Biology, 100, pp. 133–142. Available at:

4. Tweedy, L. et al. (2016) ‘Self-Generated Chemoattractant Gradients: Attractant Depletion Extends the Range and Robustness of Chemotaxis’, PLOS Biology, 14(3), p. e1002404. Available at:

5. Muinonen-Martin, A.J. et al. (2014) ‘Melanoma Cells Break Down LPA to Establish Local Gradients That Drive Chemotactic Dispersal’, PLOS Biology, 12(10), p. e1001966. Available at:

6. Scherber, C. et al. (2012) ‘Epithelial cell guidance by self-generated EGF gradients’, Integrative biology : quantitative biosciences from nano to macro, 4(3), p. 259. Available at:

7. Tweedy, L. et al. (2020) ‘Seeing around corners: Cells solve mazes and respond at a distance using attractant breakdown’, Science, 369(6507). Available at:

8. Tweedy, L. and Insall, R.H. (2020) ‘Self-Generated Gradients Yield Exceptionally Robust Steering Cues’, Frontiers in Cell and Develop

This article was specialist edited by Batiste Boeda and copy edited by Maureen Wentling.


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