Dr. Julia Engelmann

Tenure track Scientist

Expertise

Causal network modeling
High throughput ('omic') data analysis
Metagenomics of marine communities
Meta-barcoding (marker gene) analysis
Differential gene/transcript analysis

Research interests

Causal network modeling from observational data: which species have a causal effect on the presence of other species?

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.

Meta-barcoding of marine communities

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.

Metagenomic analyses of marine environmental samples

I perform metagenomic sequence data analysis in collaboration with experimental marine scientists.

Research experience

Since June 2017: Tenure-track Scientist in the Department of Marine Microbiology and Biogeochemistry (MMB) at NIOZ
Jan 2013- May 2017: Assistant Professor (‘akademische Rätin a.Z.’) in the Department of Statistical Bioinformatics, University of Regensburg, Germany
Oct 2008- Dec 2012: Postdoc in the Department of Computational Diagnostics, University of Regensburg, Germany
Apr 2008-Sep 2008: Trainee/visiting Post-Doc in the groups of Professor Rafael Irizarry and Dr. Srinivasan Yegnasubramanian, Departments of Biostatistics and Oncology, Bloomberg School of Public Health and Johns Hopkins Medical School, Baltimore, USA

Education

Jul 2004 -Mar 2008: PhD in Bioinformatics with Professor Thomas Dandekar and Dr. Tobias Müller, University of Würzburg, Germany. Thesis title: "DNA microarrays: Applications and novel approaches for analysis and interpretation."
Oct 1999-Jun 2004: Diploma in Biology at the Universities of Göttingen and Würzburg, Germany. Diploma thesis title: "Comparative Analysis of Gene Expression in Tumor and Reference Tissue of Arabidopsis thaliana"