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Many crucial cell physiological parameters associated with cell death susceptibility are currently primarily described in terms of morphology, with less focus on extracting numerical data based on that very morphology. Hence, we approach our research questions, where suitable, with an additional interdisciplinary angle with expertise in biophysics, theoretical physics, mathematical modeling and systems biology, to fill the gaps that classical cell physiology is less able to address.


"Push past possible" is our group motto, which applies to our microscopes as well. In order to fully understand molecular behaviour within cells and tissues, we utilise microscopy techniques such as live cell imaging, fluorescence resonance energy transfer (FRET), fluorescence recovery after photobleaching (FRAP) or super-resolution structured illumination microscopy (SR-SIM) to generate data that can be utilized for statistical analysis. SIM, which allows us to achieve a resolution of 80 nm, is thereby uniquely positioned to generate data that cannot be resolved through normal confocal imaging. Furthermore, we have recently started to utilize cutting edge techniques such Photoactivation Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM), to resolve structural detail up to 20 nm and Correlative Light and Electron Microscopy (CLEM) for immensely accurate localisation data.


By exploiting the functional aspects of fluorochromes, their spectral sensitivity in a defined environment (pH, voltage across membranes etc) we are able to tighten the gap between light microscopy and electron microscopy.


This is an extremely exciting and new field that we are currently expanding in collaboration with Prof. Kristain Müller-Nedebock (Department of Physics, Stellenbosch University), so that aspects can tailor-fit into unresolved questions of the above core focus areas. The major strength of such nano-biophysics  approach lies in its capabilities to draw onto numerical data within given constraints, to generate and calculate statistics on patterns and behaviors. If linked to cellular or organelle function and tested on real experimental data, powerful predictions on cellular outcome can be established.  We are here particularly interested in the emerging field of organelle network analysis related to properties such as elasticity, connectivity and efficiency that report on molecular interactions and cellular function. Current projects address fusion dynamics between autophagosomes and lysosomes, mitochondrial network connectivity and actin-cytoskeletal stiffness utilizing super-resolution structured illumination imaging and analysis. 


We have learnt in the last few years that the “magic bullet” approach for the treatment of complex diseases such as AD or cancer has most often failed. One of the argued reasons is the revealed interplay and control of the genome read-out as a function of inscriptions that are based on an active physiological process (histone code, methylation, metabolic and environmental epigenetics). The reductionist causal chain from genotype to phenotype is currently increasingly completed by numerous downward forms of causation. This demands a shift in our research approach. Systems biology is here uniquely equipped to address questions central to cellular physiology, such as structural complexity, emergent properties and complex network regulatory mechanisms. Our group includes therefore a systems biology approach, where suitable, to unravel the complex control of function. In particular, we currently focus here on the flux assessment and control analysis of protein degradation through autophagy. In order to exploit the autophagic machinery in the clinical setting, reproducible models with modular approaches based upon data and modeling standards are required. In collaboration with the Centre for Studies in Complexity, formerly headed up by Prof Jannie Hofmeyr (Stellenbosch University),  we are developing tools to understand the control of autophagic flux (as opposed to its regulation), under utilization of powerful mathematical in silico modeling approaches.


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