Fluid-to-cell assignment and fluid loading on programmable microfluidic devices for bioprotocol execution.

Being a structure like a two-dimensional (2D) array of microvalves and cells, Programmable Microfluidic Device PMD biochips have the characteristics of reconfigurability and flexibility unlike conventional flow-based microfluidic biochips. In recent years, several design automation techniques for PMD biochips have been reported. For automated control of the PMD chip implementing a bioprotocol, one of the important tasks is to minimize the number of fluid flows for loading the reactant fluids into specific cells before the bioprotocol is executed. In this work we intensively study the fluid loading problem for PMD chips and we propose a two-phase approach to solve this problem. First, we propose a constraint satisfaction problem (CSP) based method, called loading-aware fluid-to-cell assignment (LAFCA) in order to obtain a suitable fluid-to-cell assignment, which will be beneficial for fluid loading phase. Then we propose an exact method, called CSP-based loading algorithm (CSPLA) and a near-optimal heuristic method, called determining flows from the last (DFL), for determining a sequence of fluid flows required to load different fluids into the cells of a PMD chip. We formulate CSPLA as a single objective optimization problem to minimize the total number of flows. For the output as a sequence of fluid flows we define three loading parameters, the total number of flows (K), the total number of 90° bends in all flow paths (B), and the total flow path length (L). Simulation results confirm that LAFCA combined with CSPLA outperforms (K, B and L reduced by 63.9%, 31.7% and 59.9%, respectively) the state-of-the-art method fluid loading algorithm for PMD (FLAP) [Gupta et al., TODAES, 2019]. Whereas, LAFCA combined with DFL can reduce the loading parameters K, B and L by 61.2%, 20.8% and 57.4%, respectively over using only FLAP. Also from the overall simulation results we can conclude that for many testcases, DFL can find the near-optimal Ks.