Statistical framework for describing the assembly of microbial communities

Post-doctoral Researcher Guilhem Sommeria-Klein at the Academy of Finland is developing mathematical models of microbial communities.

Guilhem Sommeria-Klein wants to develop more efficient methods for analysing and interpreting the data. Rather than solely specialising in mathematics or biology, his research aims to connect various disciplines.

“This represents the core area of our research team focusing on computational analysis, and the work of Sommeria-Klein supports it well”, says Leo Lahti, Associate Professor in Data Science at University of Turku, whose team develops machine learning models for screening microbial communities.

“Microbial ecology is in desperate need of this type of basic computational studies. These models will help break complex microbial ecosystems down to a few basic structures. Ocean microbiome research could also be useful in monitoring the changes in the state of the Baltic Sea. Models based on statistical reasoning can take into account any prior information and describe the uncertainty in the results. The high-performance computing services of CSC – IT Center for Science are needed to fit these models to the data.”

In the future, Sommeria-Klein wants to continue studying ecosystems that differ from each other.

“We want the perspective on microbial ecology to be consistent across ecosystems, as it is of major importance for various societal issues, such as human health, the ocean food chain and the global carbon cycle.”

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