Ecological systems are difficult to understand, let alone predict. The reason is their enormous complexity that arises from numerous organisms interacting with each other and their environment in a multitude of ways. However, this understanding is crucial to secure a plentitude of services that are provided by ecological systems. A substantial proportion of these services are carried out by microorganisms such as bacteria, fungi, and archaea. For example, microorganisms contribute to degradation of organic matter, water purification, and even regulation of the global climate. Therefore, a thorough understanding of the ecology of microorganisms is particularly relevant for our future well-being.
While microorganisms are comparatively well-suited for experimental studies, owing also to recent technological advances in molecular biology, it is necessary to apply theory and modeling in order to fully benefit from the empirical data. A widely used theoretical method in microbial ecology is individual-based modeling, in which population or community dynamics emerge from the behavior and interplay of individual entities that are simulated according to a predefined set of rules. However, existing individual-based models of microbial communities are often specialized on particular research questions or require proficiency in specific programming languages or software. These limitations can be hampering for a broad and systematic application of individual-based modeling in microbial ecology.
For this thesis, McComedy, a framework and software tool for the creation and running of individual-based models of microbial consumer-resource systems, was developed. It allows for fast and user-friendly model development by flexibly combining pre-implemented building blocks that represent physical, biological, and evolutionary processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems was demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature.
McComedy was furthermore applied to study the evolution of metabolic interactions between bacteria. More specifically, it was assessed whether cooperative exchange of costly metabolites can evolve in bacterial multicellular aggregates. The results indicate that this is in principle possible, however, it depends on the mechanism by which the metabolites are exchanged. If metabolites are exchanged via diffusion through extracellular space, cooperation is not expected to evolve. On the other hand, if metabolites are transferred by contact-dependent means, for instance via intercellular nanotubes, cooperation is likely to evolve.
Overall, contributions from this thesis comprise, first, a user-friendly modeling tool that can be used by microbial ecologists, second, insights into the evolution of metabolic interactions in bacterial systems, and, third, awareness of how the mechanistic consideration of a process can drastically affect the outcome of a modeling study.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:osnadocs.ub.uni-osnabrueck.de:ds-202207227244 |
| Date | 22 July 2022 |
| Creators | Bogdanowski, André |
| Contributors | Prof. Dr. Karin Frank, Prof. Dr. Christian Kost, Prof. Dr. Florian Centler |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf, application/zip |
| Rights | Attribution 3.0 Germany, http://creativecommons.org/licenses/by/3.0/de/ |
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