Simon van Vliet
Research areaEcology, Evolution
PhD Systems Biology, ETH Zurich
MSc Applied Physics, Delft University of Technology
13.8 billion years ago the universe started as an unstructured ball of energy, yet today it is full of structure, occupied by complex self-replicating dynamical-systems (i.e. life), and home to self-conscious entities that can ponder the question of how all of this came into existence. I want to understand how this complexity evolved, both in the physical and biological sense of the word.
The physical laws and biological rules governing the evolution of complexity are highly divers, however in almost all cases complexity appears to be an emergent property: interactions between many relatively simple entities at one level of organization can give rise to much more complex dynamics at higher levels of organization. My primary research interest is to understand how such emergent behavior works and how it can evolve.
Initially this question drove me to study physics, however soon I realized that the study of emergent behavior is primarily a study of living systems. During my PhD I thus switched to studying simple microbial systems using a combination of single-cell experiments, quantitative data analysis, and modeling. Currently, I’m developing mathematical models to study the evolution of microbial communities.
Emergent behavior in microbial systems
One reason to study microbial systems is practical: due to their small size we can measure their properties at all levels from molecules to complex ecosystems in the lab, making them ideal systems to study emergent behavior. More importantly, microbes were the earliest life forms on our planet and studying them can help us understand how life got started. Furthermore, microbes are key players in many of earth’s biogeochemical cycles, they have colonized every multicellular organism (including us) and strongly affect the health of their hosts, and they are of mayor industrial and medical importance.
The reason that microbes can have such big impacts despite their tiny size is because they are team-players: they are part of large communities that can consist of many different species. Most properties of these communities cannot be explained by studying its members in isolation, rather they emerge from interactions between them. In my research I aim to understand how interactions between bacteria can give rise to emergent behavior at the group level and how selection can act on this emergent behavior. Understanding how microbial communities work and evolve is critical if we want to understand and/or manipulate the how these communities affect the planet, human health and disease, and industrial processes.
The role of multi-level selection in microbial communities
Many functions performed by microbial communities emerge from interactions between different member species, and are thus community level properties. Yet the genes coding for these properties are carried by the individual microbes in the community. This raises the question how these traits can evolve. Can selection act directly on community level traits, and should we thus study the evolution of microbial communities using a multi-level selection (MLS) framework? Or does selection only act on the level of individuals, and should see the evolution of communities as a co-evolutionary process?
For selection to be able to act at the level of the community it is essential that community level properties can be inherited. Whether this is the case depends on the dynamics of community assembly. For example, if host associated communities are vertically transmitted between host generations this could give rise to strong inheritance of community properties. However, if part of the community is randomly taken up from the environment this would break the inheritance. I’m currently extending a recently developed multi-level selection model to characterize if and when community assembly can maintain inheritance of community level properties. This will allow us to address the question if and when selection can act at the community level.
Emergent behavior in a simple two species cross-feeding community
Together with Alma Dal Co and Martin Ackermann (ETH Zurich) I’m developing a model to describe a simple synthetic community consisting of two amino-acid auxotrophs that can only grow by exchanging amino-acids. We have developed an individual based model that can explain the interaction range for the two cell types based on biochemical parameters of amino-acid exchange. We are currently extending this model to understand how the community equilibrium state emerges from these parameters. We can contrast the model with a rich dataset (collected by Alma Dal Co) consisting of single-cell resolution measurement of the temporal dynamics of these communities.
Emergent cross-feeding interactions in clonal populations
Together with Alma Dal Co and Martin Ackermann (ETH Zurich) we study clonal populations of E. coli growing in 2D microfluidic chambers. We found that the collective metabolic activity can give rise to emergent gradients in nutrient concentrations: cells close to the nutrient supply consume glucose while excreting acetate; as a consequence, glucose concentrations drop and acetate concentrations rise with increasing distance from the nutrient supply, eventually giving rise to an acetate consuming subpopulation of cells. We are studying how these emergent gradients can increase the phenotypic diversity in the population, how this can create a cross-feeding interaction, and how this affects the resilience of the community to environmental change.
Do cell-cell interactions allow bacteria to coordinate their activities with their neighbors?
During my PhD I studied very simple communities consisting of only a single species and used single-cell experiments and statistical analysis to show that bacteria can coordinate their activity with their neighbors. Specifically, we found that neighboring cells tend to have positive correlations in their gene expression levels and this is at least partly due to cell-cell interactions.
2018– now — Postdoc, UBC Vancouver, with Michael Doebeli
2017–2018 — Postdoc, ETH Zurich, with Martin Ackermann
2013–2017 — Ph.D. Systems Biology, ETH Zurich, with Martin Ackermann
2011–2012 — Research Associate, Delft University of Technology, with Juan Keymer
2005–2011 — M.Sc. Applied Physics, Delft University of Technology, with Juan Keymer