Currently, microfluidic devices can produce millions of nanoliter-scale droplets allowing high throughput biological analysis. Given the multi-step nature of biological analysis, these droplets often need to be shuttled through a network of fluidic channels so that they can be merged with other reagent-loaded droplets, then sorted and eventually analyzed. Realizing this vision of an ultrafast fluidic bioprocessor in the laboratory is quite a daunting task because non-linearity in the motion of droplets through interconnected networks precludes full control over the position and timing of each and every droplet. The principal hypothesis of this work is that computational thinking approaches can lead to a paradigm shift in precision engineering of error-free fluidic processors by narrowing down the design space and yielding optimized solutions of network architecture. Preliminary data supports this hypothesis, motivating us to implement computational strategies to address this design challenge. First, predictive models of the basic fluidic components of a processor will be built. Second, predictive control strategies will be used to address the relative significance of passive approaches vis-à-vis active methods to regulate droplet trajectories in fluidic networks. Finally, specialized genetic algorithms (GAs) will be developed to optimize network architecture for desired processor functionality. Additional cyber aspects of the proposal include generation and analysis of tremendous amount of digital microscopy data capturing the non-linear dynamics of droplets in networks and efficient knowledge generation from this abundant data using the best available computational infrastructure. Thus, the proposed work cuts across several disciplines including control theory, systems engineering, computational science, non-linear dynamics, fluid mechanics, microfabrication and image processing.

The results from this study will not only advance scientific and engineering frontiers in a variety of disciplines but will also lead to transformative impact in applications related to biological analysis, material synthesis, biosensing and disease diagnostics. The computational tools being developed in this work can be adapted to analyze complex networks in natural systems including microcirculation and transportation systems. Educational component of the project includes drawing graduate and undergraduate students to the visually striking microfluidics research and providing state-of-the-art training in interdisciplinary areas - yielding a workforce that is uniquely trained. In addition to disseminating the results through conference presentations and publications, the digital movies generated during the project will be stored on dedicated network servers for access to other users to eventually build a digital library of fluidic processor architectures.

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Texas Tech University
United States
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