Medical technology has seen great progress over the last centuries, developing increasingly more specialised therapy to benefit human health. The rise of the pharmaceutical industry and the research it drives are contributors to this success. However, in humanity's attempt to tackle more and more complex health issues, the conventional methods used require adaptation. This is also true in the case of neurological disorders and diseases, which stem from the body's nervous system. While the nervous system possesses high responsiveness to chemical devices, these same devices can often not target specific locations at the required time. Therefore, solely depending on chemical delivery stagnates progress in overcoming the ailments' negative effects on both health and quality of life. It is here that an argument is made for therapeutic delivery through another modality. Manufacturing such technology creates complex engineering challenges and demands close cooperation between engineers and clinicians.

In this thesis, fundamental research is done to aid the investigation of efficient acoustic delivery with focused ultrasound. It is part of an ongoing effort to make therapy for neurological disorders and diseases less invasive and more effective. The modality of ultrasonic waves promises great potential in this respect, being able to image and modulate neural activity.

The work presented here shows the development of a research platform to effectively study the biophysical mechanisms that are responsible for ultrasonic neuromodulation. Using microfabrication techniques and 3D printing the basic elements of the platform could be manufactured. During electrodeposition, silver layers were grown to construct the Ag/AgCl electrode and insight was gained into the process. Electrophysiological measurements show the platform's capability to measure bilayer lipid membranes, which were manually prepared and suspended.

During cancer development, the tumor cell population usually emerges from a single cell ancestor and can therefore be categorized as clonal. As a result of the genomic instability and selection within this clonal population, additional mutations take place and allow for the differentiation of cell lineages. This tumor heterogeneity can be captured by doing clonality analysis which stratifies distinct tumor cell populations into groups, called subclones. The process of estimating the subclonal composition of a tumor requires intricate algorithmic approaches. Due to the growing popularity of clonality analysis, many tools have been made available for the reconstruction of subclonal architectures. The majority of the tools use next generation sequencing data to infer aspects of the subclonal composition and its dynamics. The vastness of available tools calls for an unbiased comparative method between them. Here a novel framework is presented to satisfy this requirement. By integrating a selection of available tools into this framework and testing their response to different simulated data, some of the tool qualities can be identified. The achieved results show this method is able to compare the PhyloWGS and DPClust tools using in silico generated tumor samples. Next to this, the framework is capable of analyzing real data. Samples taken from metastatic sites of 8 bladder cancer patients will be discussed.