Novel computational modeling and experimentation approaches for the study of biological dynamics

Systems biology is an interdisciplinary field that aims at understanding the dynamic behavior of biological systems, utilizing various mathematical and experimental approaches. In this dissertation, it is demonstrated that well-established engineering tools such as statechart and linear control theory approaches (transfer function and state-space methods) can be applied to model the dynamics of gene networks. These approaches commonly exploit engineering principles and enable us to model large complex gene networks in an efficient and structured way. It is also shown that machine vision-based digital microfluidics can be a novel platform for the study of the dynamics of biomolecular interactions. A colorimetric enzymatic reaction-based glucose assay was utilized to show the feasibility of using EWOD (ElectroWetting-On-Dielectric)-based digital microfluidics devices. Integrated with novel computational approaches, it is expected that machine vision-based digital microfluidics will provide an innovative platform for carrying out research on the dynamics of biological processes.