Faculty of IT
+61 3 990 20675
Falk Schreiber graduated (Computer Science with a minor in Medical Informatics), obtained a PhD (Computer Science / Bioinformatics) and a habilitation (Computer Science) from the University of Passau (Germany). He worked as a Research Fellow and Lecturer at the University of Sydney (Australia) and was head of a bioinformatics research group at the IPK Gatersleben (a Leibniz Institute in Germany). In 2007 he was appointed Professor of Bioinformatics at the Martin Luther University Halle-Wittenberg (Germany) and coordinator Bioinformatics Research at the IPK Gatersleben. He joined Monash University as a Professor in 2014.
Brief summary: my research is focused on
I'm experienced in a wide range of aspects of involving computer science in biological and medical research questions, and I'm particularly interested in application-oriented and interdisciplinary directions. Major areas are:
1) Analysis of the structure and dynamics of biological processes and networks
Networks can represent a diversity of information such as metabolism, neuronal connections, infection networks, etc. With growing amounts of data, methods for investigating structural network properties are increasingly important in order to understand their shape, central elements, motifs, and so on. I research algorithms ranging from the analysis of global properties of networks and network-related data; to centrality analysis; to network motifs. My current focus is on molecular biological networks with special interest in metabolism, where I have established a comprehensive pipeline of methods and tools ranging from databases for multilevel representation of metabolic pathways/models and related *omics data; to modelling, simulation, and evaluation of metabolic processes; to visualisation and visual analytics methods for exploring analysis results.
2) Interactive visual analysis and exploration of large biological data
Analytical reasoning facilitated by interactive visualisation helps in investigating complex biomedical data. My research in this area ranges from visual analysis of high-throughput data; to algorithms for visual exploration of combined networks, data and images; to methods for the automatic layout of biological networks. As network exploration is particularly challenging I work on automatic layout algorithms, on interactive exploration methods, as well as on graphical standards for knowledge exchange and visual representation of biological processes. Applying these methods has been very helpful in, for example, the investigation of high-throughput transcriptomics data in biological as well as in medical applications, the investigation of complex multimodal data sets, as well as the exploration of large numbers of models of biochemical processes.
3) Integrative analysis of *omics data
Experimental methods result in diverse data sets about the same biological object, where each data set gives a specific view of the biological system. The integration and analysis of such data is another research topic I'm interested in. The goal is to combine all the different data such as 3D volumes, images, *omics data, and networks together in one framework for further analysis. Therefore I work on methods for data integration and knowledge representation, and also on visual investigation of complex biological data. Some examples include the analysis of *omics data in the context of networks and ontologies, the investigation of tissue-specific data within segmented images, as well as ongoing work on the integrative analysis of transcriptomics, proteomics, and metabolomics data.
Modelling metabolism, visual analytics, standardised information representation, network analysis, computational systems biology, Biomedical informatics, data, modelling, visualisation