Biologist Julia Engelmann has specialised in bioinformatics. ‘By definition, a biologist studies life. Early in my career, I discovered the beauty and, in particular, the enormous strength of “dry” mathematics as a tool for better understanding life. At NIOZ, I mainly apply that knowledge for studying the interactions between the smallest marine organisms, microorganisms, with a focus on the North Sea and the Wadden Sea.’
‘To study bacteria, algae and other microscopic life from the Wadden Sea, I allow a certain volume of seawater to pass through a filter that retains the smallest particles. I extract the DNA from those particles and subsequently examine which species are represented in that genetic material. When you compare a large number of samples from a certain area in this manner, you can find that species A always occurs with species B, for example, or that species C struggles more if species D is present. With this approach, I can use my computer to calculate which species are interacting. Or rather: which species could be interacting. Because to firmly conclude that C is eaten by D, for example, I would need to carry out experiments in the lab to actually demonstrate that.
These experiments are time-consuming, but with the help of the computational models we can design smarter experiments with higher chances of finding new relationships. Some of these relationships we could not have found with the naked eye or even with a microscope.’
‘Within NWO’s “Women In Science Excel” programme (WISE), I investigate the differences in the microbial communities of the North Sea and the Wadden Sea compared to the cold waters around Antarctica. This research will eventually teach us a lot about the foundations of marine communities and about the impact of climate change on microbial life in the oceans. That is because I not only include the species in my mathematical models but also factors such as temperature and CO2 concentrations, which means I can ultimately say something about the influence of rising temperatures and CO2 levels on microscopic sea life.’
Read more +
I use network inference methods to model interspecies interactions between marine microbial species and predict how marine microbial communities will be affected by global change.
To fully understand an ecological system, mere abundance data of individual species is insufficient. Recent global marine surveys indicate that interspecies interactions can have larger impacts on microbial community structure than environmental and geographic factors. But it is difficult to study these interactions because of the high complexity of many natural communities. On top of that, only a small fraction of the species in a natural community can be cultivated in a laboratory. I use mathematical models and computational approaches to learn species interactions from observational data. The natural variation in species' abundancies (e.g. over time) allows to infer species interaction networks and derive causal relationships. The nature of the interaction can be that one species produces a substrate for another or that species compete for nutrients, so the interactions are not necessarily physical.
How the direction of the interactions can be inferred by looking at more than two features at the same time and making use of specific characteristics of the data is illustrated below:
Imagine an organism that requires two other species to be present in the community to sustain its living. We call the ‘supporting’ species A and B and the dependent species C. When only one of the two ‘supporting’ species A or B are in the same community, the dependent organism C will never be found. Only when both A and B are present, species C will be found. That is, when the dependent species is present, we know that the other two must also be present, while when the dependent organism is absent, we know nothing about the other two. This phenomenon is called conditional independence. Dependent on the condition of C, A, and B are independent or not. This independence structure can be recovered from observational data and represented in a Bayesian network as arrows from A and B converging in C. Based on these structures, further parts of the interaction graph can be directed and allow to infer information flow.
My models can also be used for applied research. They can aid in the design of synthetic consortia for desired applications. For example, causal modeling can propose changes to the community that will improve the yield of useful metabolites or other natural products of interest. For example, communities that optimize degradation of plastic particles in the ocean.
I contribute sequence data analysis to marker gene studies of marine communities, e.g. amplicon-based rRNA gene locus analyses. I also use the abundance data to generate networks of species interactions.
I perform metagenomic sequence data analysis in collaboration with experimental marine scientists.