Advances in digital-microfluidic biochips have led to miniaturized platforms that can implement biomolecular assays. However, these designs are not adequate for running multiple sample pathways because they consider unrealistic static schedules; hence runtime adaptation based on assay outcomes is not supported and only a rigid path of bioassays can be run on the chip. We present a design framework that performs fluidic task assignment, scheduling, and dynamic decision-making for quantitative epigenetics. We first describe our benchtop experimental studies to understand the relevance of chromatin structure on the regulation of gene function and its relationship to biochip design specifications. The proposed method models biochip design in terms of real-time multiprocessor scheduling and utilizes a heuristic algorithm to solve this NP-hard problem. Simulation results show that the proposed algorithm is computationally efficient and it generates effective solutions for multiple sample pathways on a resource-limited biochip. We also present experimental results using an embedded microcontroller as a testbed.