The ongoing development and improvement of novel measurement techniques for scientific research result in a huge amount of available data coming from hetero- geneous sources. Amongst others, these sources comprise diverse temporal and spatial scales including different omics levels. The integration of such multiscale and multi-omics data enables a comprehensive understanding of the complexity and dynamics of biological systems and their processes. However, due to the biologically and methodically induced data heterogeneity, the integration process is a well-known challenge in nowadays life science. Applying several computational integration approaches, the present doctoral thesis aimed at gaining new insights into the field of infection biology regarding host- pathogen interactions. In this context, the focus was on fungal pathogens causing a variety of local and systemic infections. Based on current examples of research, on the one hand, several well-established approaches for the analysis of multiscale and multi- omics data have been presented. On the other hand, the novel ModuleDiscoverer approach was introduced to identify regulatory modules in protein-protein interac- tion networks. It has been shown that ModuleDiscoverer effectively supports the integration of multi-omics data and, in addition, allows the detection of potential key factors that cannot be detected by other classical approaches. This thesis provides deeper insights into the complex relationships and dynamics of biological systems and, thus, represents an important contribution to the investigation of host-pathogen interactions. Due to the interactions complexity and the limitations of the currently available knowledge databases as well as the bioinformatic tools, further research is necessary to gain a comprehensive understanding of the complexity of biological systems.