Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees

Abstract

Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.

Synthesis of biochemical applications on digital microfluidic biochips with operation execution time variability

“…For example, in the case of sample preparation, the reagents degenerate fast, affecting the efficiency of the entire bioassay [8,24]. We assume that we know for each operation O i its wcet C i and best-case execution time (bcet).…”

“…There is a significant amount of work on the synthesis of DMBs [4,[7][8][9][10], which typically consists of the following tasks: modeling of the biochemical application functionality and biochip architecture, allocation, during which the needed modules are selected from a module library, binding the selected modules to the biochemical operations in the application, placement, during which the positions of the modules on the biochip are decided, scheduling, when the order of operations is determined and routing the droplets to the needed locations on the biochip. The output of these synthesis tasks is the "electrode actuation sequence", applied by a control software to run the biochemical application.…”

An open-source compiler and PCB synthesis tool for digital microfluidic biochips

“…In Table 1, the term CV is shorthand for concentration value, i.e., the desired concentration of a target droplet. Six of the algorithms that we have implemented generate a dilution tree that produces one droplet of a desired CV, using different optimization strategies [11,19,29,65,66,72]; four more algorithms generate multi-output DAGs (sometimes, but not always, a forest of trees) that produce multiple droplets of desired CVs [4,28,30,54]. The aforementioned algorithms all assume that one sample fluid is diluted with a buffer (a non-reactive fluid that maintains the pH of the sample fluid during dilution).…”

Algorithmic Challenges in Digital Microfluidic Biochips: Protocols, Design, and Test

“…Using the method REMIA [42], a wider class of exponential dilution gradient generation scheme is proposed recently [43]. An on-chip implementation of an exponential gradient generator on a DMF platform is shown in Fig.…”

“…mixture preparation. Subsequently, a few other dilution and mixing algorithms were reported with various optimization goals [13,41,42,[46][47][48].…”