When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
The field of biology and biochemistry relies heavily on efficient screening procedures to discover and develop new entities or optimize existing processes. However, traditional screening methods are time-consuming, labor-intensive, and expensive, requiring thousands to millions of experiments. Microfluidic and automation technologies offer a promising solution to this problem, enabling high-throughput screening (thousands of droplets in a few seconds) in a much faster and less expensive manner. In addition to accelerating the screening processes, microfluidic technologies can reduce reagent consumption and improve precision and control through automation and miniaturization of experimentation. However, droplet-in-channel microfluidic systems are limited in terms of fluidic operations as they manipulate droplets only by pressure-based flows. In contrast, digital microfluidics provides greater programmability by manipulating droplets using integrated electrodes. However, this precise control and manipulation significantly decreases the system throughput.
Therefore, this thesis aims to integrate droplet and digital microfluidic systems to offer valuable insights into the potential of microfluidic technologies for enhancing the efficiency of screening procedures in the field of single-cell analysis and biochemical synthesis. By integrating droplet and digital microfluidic systems, we propose new methods for single-cell studies, such as mammalian cells gene editing and monoclonal antibody discovery. Furthermore, the developed high throughput screening system can be used to screen large libraries of potential therapeutics and diagnostics, such as radiotracers for bioimaging applications. We also propose to leverage design-of-experiment methodologies and machine learning algorithms to enhance the efficiency of digital microfluidics for optimization of biochemical synthesis reactions. These works involve the development of new hardware and software, and integration of biological assays and biochemical reactions on these platforms. These systems can expand and improve the application of microfluidic and automation systems for biotechnology industries.