Sara Mitri



The focus of my current work is on one of the research priorities in biology: the study of social interactions in microbial communities and how they evolve. I am approaching this problem by developing computational and mathematical models whose predictions I test experimentally in the lab.

My research has always been at the intersection between the computational sciences and biological evolution. My early research applied evolutionary thinking to the design of computational algorithms for automated games and to improve image processing algorithms in robots. In my PhD work, I used robots as models of individual animals to study the evolution of cooperative communication.

The evolution of bacterial communities

Many bacteria live at high density in complex communities containing members of the same and different species. Within these communities, bacterial cells can strongly affect the growth and survival of neighbouring cells, which are social traits in an evolutionary sense. For example, microbes secrete compounds that promote the growth of neighbouring cells, such as enzymes that break down complex proteins into nutrient sources. Other secretions, such as toxins, inhibit the growth of surrounding cells.  Understanding these interactions and how they evolve over time has many important applications such as the control of microbial infections in humans and the engineering of fertilisers to increase crop efficiency.

An individual-based computer simulation of bacterial communities has been developed in the Foster lab to explore how spatial patterns within these communities influence the evolution of social phenotypes. In agreement with social evolution theory, the amount of mixing between different bacterial strains has been found to strongly predict whether social phenotypes will spread in a population [1]. My current work involves (1) testing these predictions in the laboratory and (2) extending the computational model - and eventually the experimental system - to include multiple interacting species.

To test how spatial organisation within bacterial groups affects the evolution of social phenotypes, I am conducting experiments using fluorescently-labelled strains of a pathogenic bacterium, Pseudomonas aeruginosa. As well as expressing a number of social phenotypes, including the secretion of signalling molecules, colonies of P. aeruginosa display interesting spatial patterns (see image above).

In parallel to this work, I have been exploring the evolution of multispecies interactions using computer simulations. A first step in extending the existing model is to understand how the social phenotypes of one species can evolve in the presence of a second clonal species under varying ecological conditions. Consistent with previously published experimental data [2], the model predicts that under high competition for nutrients, the addition of new species can reduce selection for cooperative phenotypes within the focal species. However, the model also identifies conditions where the opposite effect is predicted. Under high nutrient conditions, additional species can “protect” secretors from non-secretors and allow them to thrive. Finally, I have used the model to study the evolution of cooperation among species (mutualism) whereby each species secretes something that benefits the other. This revealed a number of important constraints on the evolution of such mutualism among species. In particular, among-species cooperation is only likely when secretions are cost-free or whenever the two species do not compete for the same nutrients [3].


The evolution of communication in robots

In my PhD work [4], I used groups of foraging robots that could emit and perceive light to communicate, to explore how different evolutionary conditions can determine the level of reliability of evolving signals. In agreement with evolutionary theory, the reliability of the resulting communication system was found to depend on the level of relatedness between robots in a group and the level at which they were selected. Robots that were highly related or selected at the group level evolved reliable signals, despite the competition. In contrast, when relatedness between robots in a group was low and selection was acting at the level of the individual, robots were selected to suppress the inadvertent cues produced while foraging. However, because of the effect of mutations, these cues were never completely suppressed and some variability in signalling was maintained.

The PhD thesis resulted in three main contributions: Firstly, because similar co-evolutionary processes should be common in natural systems, the findings provide a possible explanation as to why communicative strategies are so variable in many animal species when interests between them conflict. Secondly, they predict that the level at which selection operates will play an important role in the evolution of signal reliability in natural systems of communication. Finally, the work illustrates how evolutionary robotic systems can be used to explore issues that cannot easily be studied experimentally with living organisms, such as the evolution of cooperative behaviour, and thus contribute to our understanding of biological systems.

For more information, check my publications.


  1. 1.Nadell CD, Xavier JB, Foster KR (2008) Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comp Biol 6:e1000716.

  2. 2.Harrison F, Paul J, Massey RC, Buckling A (2008) Interspecific competition and siderophore-mediated cooperation in Pseudomonas aeruginosa. ISME J 2:49-55.

  3. 3.S. Mitri, J. Xavier, K. R. Foster (2011) Social Evolution in Multispecies Biofilms. PNAS 108:10839-46.

  4. 4.S. Mitri (2009) The Evolution of Communication in Robot Societies. PhD thesis, Lausanne.