Recent advances in microfluidic engineering allow the creation of microenvironments in which human cells can be cultured under (patho-)physiological conditions with greater reality than standard... Show moreRecent advances in microfluidic engineering allow the creation of microenvironments in which human cells can be cultured under (patho-)physiological conditions with greater reality than standard plastic tissue culture plates. Microfluidic devices, also called Organs-on-Chip (OoC), allow complex engineering of the cellular compartment, yielding designs in which microfluidic flow can be precisely controlled. However, it is important that cellular physiology is not only controlled but can also be monitored in these devices. Here, we integrated oxygen and pH sensors into microfluidics, allowing close monitoring of the extracellular flux from the cells, enabling constant assessment of features such as glycolysis and mitochondrial oxidative phosphorylation in situ. Using human -induced pluripotent stem cells (hiPSCs) as an exemplar of a highly metabolic and relatively challenging cell type to maintain, we showed that monitoring the extracellular environment allowed rapid optimization of the seeding protocol. Based on the measurements, we implemented earlier and more frequent media refreshment to counteract the rapid acidification and depletion of oxygen. The integrated sensors showed that hiPSCs in the devices exhibited mitochondrial and glycolytic capacity similar to that measured with the Seahorse extracellular flux system, the most widely used standard for these types of assays in conventional cell culture. Under both conditions, hiPSCs showed greater reliance on glycolysis than mitochondrial OXPHOS and the absolute values obtained were similar. These results thus pave the way for the assessment of cell metabolism in situ under con-ditions of fluidic flow with the same precision and relevance as current standard static cell cultures. Show less
Today, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new... Show moreToday, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new insight. This thesis shows how complex systems often exhibit diverse behavior at different temporal scales, and that data mining methods should be able to cope with the multiple resolutions (scales) at the same time in order to fully understand the data at hand and extract useful information from it. Under these assumptions, we introduce novel data mining and visualization methods for large time series data collected from complex physical systems. In particular, we focus on three fundamental problems: the detection of multi-scale patterns, the recognition of recurrent events, and the interactive visualization of massive time series data. We evaluate our methods on a real-world scenario provided by InfraWatch, a Structural Health Monitoring project centered around the management and analysis of data collected by a large sensor network deployed on a Dutch highway bridge. The application of our methods resulted in the identification of the relevant scales of analysis in the InfraWatch data (and other datasets), the detection of the different recurring motifs and the visualization of terabytes of time series data interactively. Show less
