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Spatial Systems Biology


The research focus of my group is on quantitative and mechanistic understanding of regulatory circuits and synthetic engineering of biological networks. The research topics are interrelated and driven by the fundamental questions how complex networks are able to robustly and accurately carry out their physiological functions and how to obtain an understanding of the mechanistic relationship between genotype and phenotype. As we move forward with the maturation of systems and synthetic biology, the conceptual framework of Dynamic Systems Theory and idealized network motifs will be more and more useful in mechanistically deconstructing what are, for now, often impenetrably complex molecular networks. By this we do not view the complexity of biological networks as a source of confusion, but instead as a system that we have logical command of, and which we can tune in treating disease and solving other biotechnological challenges.
The analysis of dynamic networks is the core of my research. The link between a specific realization of a network, the genotype, and the function or behaviour of the network, the phenotype is what essential for the quantitative understanding of biology. Spatial coupling of cells to form a higher-order system like tissues, organs, or bacterial consortia are a particular interesting and challenging subject. Pattern formation in a developing structure requires the spatial coupling of intra-cellular processes. The coupling can be done via exchange of chemicals, but can also be via direct receptor-ligand or mechanical interactions. Through this coupling cells exchange information enabling coordinated growth and cell fate decisions. Growth of the developing structure and chemical patterning are often intimately related. One illuminating way to analyse and understand the complexity of developmental processes is to search for general principles that link network structures to biological functions. The intra-cellular regulatory networks comprise integrated modules or network motifs, which are spatially coupled to the network in neighbouring cells. Although the temporal dynamics of network motifs (e.g., feed-forward loops) have been previously discussed, the properties of spatially coupled network motifs are so far very little explored. I aim to understand how genetic networks are able to generate growth and patterning. Because mesoscopic intra-cellular processes result in macroscopic physiological structures our mathematical models will be always multi-scale. As my approach is data- driven I work on the pattering processes in collaboration with experimental partners.


Spatial structures of cell communities, such as biofilms or cell tissues are in many cases a consequence of an orchestrated dynamic self-organisational process. A particular difficulty in the investigation of spatial or temporal pattern formation is the inevitable variability in biological systems. As new quantitative data becomes available and experimental manipulations for the verification of theoretical predictions are feasible, the aspect of noise in pattern formation is receiving more attention. The obvious question arises: how is it possible to achieve from heterogeneous microscopic material a well- defined macroscopic structure? It is well appreciated that reaction-diffusion equations embodying a Turing mechanism can emulate spatial pattern formation in a wide variety of biological processes in developmental biology. This mechanism postulates that morphogenesis has a chemical basis but does not address the fact that the cells producing these chemicals are not identical and that hence this heterogeneity will also affect the final pattern. In order to make progress we will investigate spatial- temporal networks and there noise exploitation and attenuation characteristics. My general aim is to understand how stochasticity in single cells (e.g. gene expression) does affect variability on a higher organizational level (population, tissue) and vice versa. This will enhance our mechanistic understanding of the developmental processes that lead to the variability observed in experiments.


Very much related to the research described above but a more recent development is my interest in finding efficient methods to quantify uncertainty in predictive mathematical modelling in biology. This is of enormous relevance in Systems and Synthetic Biology. A core topic in the field of uncertainty quantification is the question how uncertainties in model inputs are propagated to uncertainties in model outputs. How can one characterize the distribution of model outputs, given the distribution of the model inputs? We are looking for efficient methods and algorithms to answer this question in the context of biological systems, in particular in Synthetic Biology (e.g., how to engineer in light of uncertainties?).


The simulation shows chemotactically active cells accumulating along activator peaks of a schnakenberg-model. The simulation was implemented in PhysiCell and enhanced by visualization in python.

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