Towards a microbial network inference and analysis platform

The group of organisms too small to observe with the naked eye are called microbes. Despite their small stature, they have an outsized impact on global health and are important participants in most biochemical processes that take place on the planet. Historically, microbiological studies focused on species and their ability to carry out certain functions, such as nitrogen fixation in the soil or their capacity to become human pathogens. However, it has become apparent that microbiomes, the complete collection of microbes in a system, are more than the sum of their parts. Their behaviour cannot be understood by a complete understanding of microbial behaviour in pure cultures; rather, interactions between microbes have a major impact on ecosystem functioning. In this context, microbiome research first enabled detailed quantitative studies of the role of microbes in their communities. Specifically, a range of computational methods has been developed to predict microbial interactions from sequencing data. These methods need to tackle a number of statistical issues, including compositionality (microbial counts are fractions) and sparsity (many microbes are rare). Because of these issues, the accuracy of microbial network inference methods has been found to be low. Yet, there is a more fundamental issue with this approach, which is that the associations found by microbial association network inference methods do not necessarily reflect interactions. Predatory interactions can lead to microbes occurring in the same ecosystems, but it may also lead to oscillatory patterns of abundances where one microbe is only abundant when the other is not. The same goes for other biotic interactions such as parasitism and amensalism. Another issue is that of abiotic interactions leading to associations which are then misinterpreted as possible biotic interactions. When species are responding to abiotic drivers of community structure, such as pH, this can drastically affect the structure of any inferred association network. Given these issues, there is a distinct difference between microbial association networks and most types of networks analyzed in other fields of science (Chapter 2). It therefore remains an open question whether methods developed to analyze those networks are applicable to microbial association networks. In this thesis, I therefore describe several computational methods that I developed to improve our understanding of microbial networks (Chapters 3-5). These