Nature’s networks: biodiversity’s bottom-up approach

Challenge: more efficient networks

Natural inspiration: slime moulds, plants and ants


bare tree branch © Andrew Dubock,



Networks are found in both nature and human societies.  They may be physical, like blood vessels in the body and railway networks; or virtual, like information flowing between animals in a population, or computers on the internet.  How well a network functions is a trade-off between the cost of moving objects or information through the system, the efficiency with which the network operates, and how well it copes with problems. 1


Traffic jam © Bud Adams,



Optimising the networks people use in everyday life could bring a host of benefits: reduced journey times on roads, faster internet connection and cheaper electricity supply to name but a few.  Improvements to networks would bring economic gains.  For example, in the UK road congestion is estimated to cost the economy £20 billion per year, and this is predicted to rise as road traffic grows faster than road capacity. 2 A better understanding of networks could enable planners to improve the efficiency of our road system, allowing it to carry more traffic without congestion.  The mathematics involved in designing networks is complex, and human designers do not always come up with the optimum solution. 



In nature networks are not designed, but develop organically in a ‘bottom up’ fashion, the components of the network finding their own place without any centralised control.  Evolution over the millennia has selected the best solutions out of all the possible ways of organising systems. 1 This is the equivalent of running our computer models and mathematical equations millions upon millions of times.  No wonder mathematicians and engineers are looking to diverse biological systems for design inspiration.  Answers have been found so far in three very different areas of biodiversity: slime moulds, plants and ants.



Slime mould © Lebrac, http://commons.wikimedia.orgSlime moulds are single-celled organisms that live on decaying matter and resemble fungi, although they are unrelated. 3 Each individual cell finds food by sending out a network of thin tendrils.  When one tendril finds a food source, it expands while all the others slowly disappear 4.  Scientists have recently carried out a clever demonstration of the efficiency of this method.  They took a map of Japan and placed food morsels on the major cities.  They then introduced a slime mould (Physarum polycephalum) at the point representing Tokyo.  The growing mould explored the map by sending tendrils in all directions, strengthening those that found food and terminating those that did not.  After 23 hours, the tendrils formed a network which closely followed Tokyo’s railway system: the mould had arrived at the same solution as the railway planners! 1 The railway planners set out to  connect Japan's cities as efficiently as possible; whereas the mould was just growing towards food by a process of trial and error.  The qualities of self-organisation, self-optimisation and self-repair exhibited by the mould are exactly what is needed to build robust technological networks 5.



leaf veins © Sebastian Fissore, www.sxc.huPlants demonstrate a variety of network patterns.  Mathematicians have shown that the most efficient network design resembles a tree.  A major route (like the trunk) splits off into smaller routes (branches), which themselves split again into even smaller routes (like the twigs), and so on.  However, this type of network does not cope well with problems: if one route is blocked, everything ‘downstream’ of it is cut off.  Imagine if the vessels in one of a tree's branches are damaged: all of the twigs growing from that branch will die.  Physicists noticed that the leaves of many plants have a different type of network: one which contains loops as well as branches.  They carried out mathematical modelling to discover why this might be.  The results showed that, as well as coping better with ‘blockages’ (there is always another way round if one route is blocked), these networks were more efficient at dealing with fluctuating loads. 6  This sort of design would clearly be best for systems such as road networks, where the amount of traffic varies greatly at different times, and parts of the system may be blocked at any given time (traffic accidents, roadworks etc).



Marching ants © Gabriel Doyle, www.sxc.huAnts are a highly successful family, being found everywhere in the world except Antarctica, although about 150 species of ant are now listed as “vulnerable” or worse on the IUCN Red List. 7  People have long been fascinated by how ant colonies function. These simple insects can perform incredible feats of co-operation, achieving tasks far beyond any of the individual ants. 8  Ants communicate with each other by depositing pheromones (chemicals that other ants can smell) on the ground.  The pheromone is a sign to other ants to follow the same route, leading to the familiar sight of ants marching in a line between the nest and a food source.  Experiments have shown that this simple rule –  choose the route that most ants chose before you, by following the strongest pheromone trail – allows ant colonies to solve a variety of problems.  For example, they can correctly identify the shortest route to a food source 8, and they can even gather up dead members of the colony into an ordered cemetery 9.  No one ant is aware of the whole task: they do not look around them or plan ahead.  Each ant simply follows the pheromone trail in front of it. 9  This method has advantages and disadvantages over the systems usually adopted by humans.  A foreman directing a team of human workers could probably get an equivalent job done more quickly than the ant colony – but if the foreman leaves, or makes poor decisions, the whole system collapses.  If the task is very complex, the foreman might not be able to plan out the most efficient way of performing it; but an ant colony would eventually arrive at the solution by trial and error.  The ants' bottom-up system is more robust because it does not rely on one central point of control.



Pilot in cockpit © Dominic Morel, www.sxc.huThis concept has proved so useful it has given rise to a branch of mathematics called “ant colony optimisation” (ACO) , invented in the 1990s 10.  In 2003, an academic paper was published that demonstrated how ACO could be used to design an optimal water distribution network.  The new ant-based mathematics produced a better solution than other methods. 11  Financial analysts have since used ACO to solve congestion problems at airports.  At a busy airport, planes may have to queue for airport gates, causing delays to flights.  Computer software designed using the ACO approach directs pilots to certain gates to ensure the system as a whole operates as efficiently as possible.  It can successfully predict queues before they even start to build up, and redirect pilots to avoid delays 12.  The same airline company used ACO to streamline its cargo operations, saving an estimated $10 million per year. 13  The way ant colonies operate also has lessons for how to manage personnel in a company: business consultants are increasingly using ant-based models to advise companies how to increase productivity. 13


Humans have only just begun to use the lessons we are learning from biodiversity.  The more we study nature, at every scale from the microscopic veins in a leaf to the 'super-organism' of an ant colony, the more new ideas we find to help human society become more productive and resource-efficient.


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  1. Tero, A. et al. (2010) Rules for Biologically Inspired Adaptive Network Design.  Science 22: 439 – 442
  2. Goodwin, P. (2004) The economic costs of road traffic congestion. The Rail Freight Group, London, UK.  Article available in full online.   Accessed March 2010.
  3. Introduction to the “Slime Molds”.  University of California Museum of Paleontology. Accessed March 2010.
  4. Mold May Help Design Future Transportation Routes. Treehugger, January 2010.  Accessed March 2010.
  5. Marwan, W. (2010) Amoeba-Inspired Network Design.  Science 22: 419 - 420
  6. Katifori, E. et al. (2010) Damage and Fluctuations Induce Loops in Optimal Transport Networks.  Physical Review Letters 104, 048704.
  7. IUCN Red List.  Accessed March 2010.
  8. Ant Colony Optimisation.  Marco Dorigo and Thomas Stützle (2004).  Massechusetts Institute of Technology.  Sample pages available online.  Accessed March 2010.
  9. Wokoma, I. et al. (2002).  Biologically Inspired Models for Sensor Network Design. Proceedings of LCS
  10. Dorigo, M. el al. (2005) Ant colony optimization theory: a survey.  Theoretical Computer Science 344: 243-278
  11. Maier, H.R. et al. (2003) Ant Colony Optimization for Design of Water Distribution Systems.  Journal of Water Resources Planning and Management 129: 200-209
  12. Planes, trains and ant hills. Science Daily, April 2008.  Accessed March 2010.
  13. Lessons from the Ant Farm. Chief Executive (US), January 2003.  Accessed March 2010.


Further reading