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Post-doc project My research combines empirical datasets and mathematical approaches to study the structure, resistance, and resilience of microbial communities. My environments of predilection are oxygen-depleted and extreme environments. Why? Because they hold so much information, they are very sensitive to environmental changes and are exciting! Combining omics datasets with metabolic models allows for studying microbial communities through new lenses and developing theoretical microbial ecology concepts.
Microbial metabolism is central to community structure and functionality, yet study of environmental community metabolism remains challenging. This can be addressed by integrating omics data to characterise communities and construct metabolic models to simulate community metabolism. The goals are: (1) to analyse communities living in Baltic Sea oxygen-depleted environments, present and past, using omics-based approaches; (2) to develop metabolic models capable of simulating complex communities; (3) to simulate community metabolism and test their resistance and resilience against stressors.
Oxygen-depleted environments, while naturally present on Earth, are expanding due to human activity and climate change, creating oxygen minimum zones (OMZ). The absence of oxygen dramatically alters ecosystems functioning, restricting habitats for most aquatic organisms except specific group of microorganisms. These microorganisms present unique metabolic capability not relying on oxygen while also producing greenhouse gases. Hence, the expansion of OMZs makes their study increasingly critical.
OMZs microbial communities are traditionally studied using metagenomics and metatranscriptomics approaches, providing information on community structure, functional potential and genes expression. However, these approaches can't capture community metabolic capability. Metabolisms are one of the microbial community structure driving forces as well as the direct representations of the interactions between the community and its environment. Unfortunately, the methods required to study the community metabolisms are not yet possible to be applied on environmental community due to the sheer complexity of them.
Metabolic modelling offers a powerful alternative by enabling simulation of microbial metabolisms, based on genomic data collected from the environment, under controlled conditions in a virtual environment. Hence, when studies on community structural stability are highly difficult to carry with traditional methods, metabolic models, in combination with multi-omics datasets, can provide new approaches to observe community stability and resilience to environmental stressors.
My research vision is to develop microbial ecology through an interdisciplinary approach, merging traditional microbial ecology tools with mathematics simulation. Not only can it provide new ways of performing microbial ecology studies, the field of theoretical microbial ecology also has much to gain with the development of more accurate ecological concepts and models.
