Science Spotlight
The human body contains myriads of components that range from biomolecules to cells, tissues and organs. Yet, we are far more than the sum of our parts. The broad ambition of our research is to help us understand exactly how.
The key ingredient for the transition from individual components to collective function, from simple to complex, is the way in which the components interact with each other. We use tools and concepts from network theory to characterize these interaction patterns and interpret their biological implications. A particular focus of our work is the network of all interactions between human proteins. In analogy to the genome, the integrated protein interaction network is also termed interactome. Where the genome provides a blueprint for the individual components of the human body, the interactome provides a blueprint for the collective functions that emerge from their interactions.
Our projects address diverse questions ranging from fundamental organizational principles in biology to practical challenges in medicine. The vignettes below provide several examples, a complete list of publications can be found here.
Network cartographs for interpretable visualizations
Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node’s position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.
The VRNetzer platform enables interactive network analysis in Virtual Reality
Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.
Network analysis reveals rare disease signatures across multiple levels of biological organization
Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.
The regulatory network architecture of cardiometabolic diseases
Complex disease definitions often represent descriptive umbrella terms of symptoms rather than mechanistic entities. In this brief News & Views article we highlight a new study that shows how network-based approaches can help identify the mechanisms that link genes, cells, tissues and organs in cardiovascular diseases. We discuss how network medicine approaches can help accelerate the development of early and individualized diagnostics and therapeutics in the coming era of precision medicine.
Morphological profiling of human T and NK lymphocytes by high-content cell imaging
The immunological synapse is a complex structure that decodes stimulatory signals into adapted lympho- cyte responses. It is a unique window to monitor lymphocyte activity because of development of systematic quantitative approaches. Here we demonstrate the applicability of high-content imaging to human T and nat- ural killer (NK) cells and develop a pipeline for unbiased analysis of high-definition morphological profiles. Our approach reveals how distinct facets of actin cytoskeleton remodeling shape immunological synapse archi- tecture and affect lytic granule positioning. Morphological profiling of CD8+ T cells from immunodeficient in- dividuals allows discrimination of the roles of the ARP2/3 subunit ARPC1B and the ARP2/3 activator Wiskott- Aldrich syndrome protein (WASP) in immunological synapse assembly. Single-cell analysis further identifies uncoupling of lytic granules and F-actin radial distribution in ARPC1B-deficient lymphocytes. Our study pro- vides a foundation for development of morphological profiling as a scalable approach to monitor primary lymphocyte responsiveness and to identify complex aspects of lymphocyte micro-architecture.
