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The complex molecular machinery of the cell controls its response to various signals and environmental conditions. A natural approach to study these molecular mechanisms and cellular processes is with protein interaction networks. Due to the complexity of these networks, sophisticated computational techniques are required to extract biological insights from them. In this thesis, I develop and apply network-based algorithms for three different challenges. 1. I develop a novel, highly-scalable algorithm for network-based label prediction methods that enables the integration of functional annotations and interaction networks across many species in order to predict the functions of genes in newly-sequenced bacteria. 2. To overcome the limitations of experimental approaches to find human proteins and processes that are hijacked by SARS-CoV-2, I adapt network propagation approaches for predicting human interactors of the virus. 3. Large-scale experimental techniques to screen chemicals for toxicity have tested their effects on many individual proteins. I integrate human protein-protein interactions with this data to gain insights into the molecular networks those chemicals affect. For each of these research problems, I perform comprehensive evaluations and downstream analyses to demonstrate both the accuracy of our approaches and their utility in obtaining a broader understanding of the molecular systems in question. / Doctor of Philosophy / The functions of all living cells are governed by complex networks of molecular interactions. A major goal of systems biology is to understand the components of this machinery and how they regulate each other to control the cell's response to various conditions and signals. Advances in experimental techniques to understand these systems over the past couple of decades have led to an explosion of data that probe various aspects of a cell such as genome sequencing, which reads the DNA blueprint, gene expression, which measures the amount of each gene's products in the cell, and the interactions between those products (i.e., proteins). To extract biological insights from these datasets, increasingly sophisticated computational methods are required. A powerful approach is to model the datasets as networks where the individual molecules are the nodes and the interactions between them are the edges. In this thesis, I develop and apply network-based algorithms to utilize molecular systems data for three related problems: (i) predicting the functions of genes in bacterial species, (ii) predicting human proteins and processes that are hijacked by the SARS-CoV-2 virus, and (iii) suggesting cellular signaling pathways affected by exposure to a chemical. Developments such as those presented in these three projects are critical to obtaining a broader understanding of the functions of genes in the cell. Therefore, I make the methods and results for each project easily accessible to aid other researchers in their efforts.

Cancer is a very complex genetic disease driven by combinations of mutated genes. This complexity strongly complicates the identification of driver genes and puts enormous challenges to reveal how they influence cancerogenesis, prognosis or therapy response. Thousands of molecular profiles of the major human types of cancer have been measured over the last years. Apart from well-studied frequently mutated genes, still only little is known about the role of rarely mutated genes in cancer or the interplay of mutated genes in individual cancers. Gene expression and mutation profiles can be measured routinely, but computational methods for the identification of driver candidates along with the prediction of their potential impacts on downstream targets and clinically relevant characteristics only rarely exist. Instead of only focusing on frequently mutated genes, each cancer patient should better be analyzed by using the full information in its cancer-specific molecular profiles to improve the understanding of cancerogenesis and to more precisely predict prognosis and therapy response of individual patients. This requires novel computational methods for the integrative analysis of molecular cancer data. A promising way to realize this is to consider cancer as a disease of cellular networks. Therefore, I have developed a novel network-based approach for the integrative analysis of molecular cancer data over the last years. This approach directly learns gene regulatory networks form gene expression and copy number data and further enables to quantify impacts of altered genes on clinically relevant downstream targets using network propagation. This habilitation thesis summarizes the results of seven of my publications. All publications have a focus on the integrative analysis of molecular cancer data with an overarching connection to the newly developed network-based approach. In the first three publications, networks were learned to identify major regulators that distinguish characteristic gene expression signatures with applications to astrocytomas, oligodendrogliomas, and acute myeloid leukemia. Next, the central publication of this habilitation thesis, which combines network inference with network propagation, is introduced. The great value of this approach is demonstrated by quantifying potential direct and indirect impacts of rare and frequent gene copy number alterations on patient survival. Further, the publication of the corresponding user-friendly R package regNet is introduced. Finally, two additional publications that also strongly highlight the value of the developed network-based approach are presented with the aims to predict cancer gene candidates within the region of the 1p/19q co-deletion of oligodendrogliomas and to determine driver candidates associated with radioresistance and relapse of prostate cancer. All seven publications are embedded into a brief introduction that motivates the scientific background and the major objectives of this thesis. The background is briefly going from the hallmarks of cancer over the complexity of cancer genomes down to the importance of networks in cancer. This includes a short introduction of the mathematical concepts that underlie the developed network inference and network propagation algorithms. Further, I briefly motivate and summarize my studies before the original publications are presented. The habilitation thesis is completed with a general discussion of the major results with a specific focus on the utilized network-based data analysis strategies. Major biologically and clinically relevant findings of each publication are also briefly summarized.