A simulation-based technology for stochastic modeling, sensitivity analysis and design optimization, aimed at development of next-generation micro-fluidic devices for biomedical applications.

During an epidemic, testing numerous patients puts a heavy burden on the healthcare sector, while infections continue to rise in the absence of a treatment. In such a scenario, micro-fluidics technology can be used to develop affordable point-of-care diagnostic tools for detecting infected patients early, and effective drug discovery platforms for synthesizing therapeutic drugs. The development of such devices is extremely challenging, needing expertise in multiple disciplines (e.g. physics, chemistry, biology), and understanding the interplay between variables that influence operating performance requires computational assistance. While advanced design software may be adopted to simulate the performance of such devices, precise knowledge of prediction reliability is of paramount importance to ensure their suitability for clinical decision-making. Therefore, the long term objectives of this project are to commercially introduce a new paradigm of digital engineering design that focuses on evaluating the fluctuations in performance outputs due to variability in input parameters and to demonstrate the relevance of such a simulation-based technology through specific application to development of micro-fluidic biomedical devices. The envisioned proof-of-concept is a modular computational system with practical commercial applications in the healthcare sector that demonstrates how scientific computing, numerical simulation andamp; artificial intelligence modeling approaches can lead to an increased understanding of the performance of a micro-fluidic system subject to operating uncertainty, and enable robust design optimization. The proposed approach is to employ innovative stochastic spectral methods andamp; advanced numerical schemes to conduct computationally efficient, high fidelity simulations involving uncertainty quantification andamp; propagation, model sensitivity analysis, and finite element analysis for the engineering evaluation of progressively complex micro-fluidic device designs, and to incorporate artificial intelligence based meta-modeling techniques to perform design space exploration for performance improvement of such devices. The Randamp;D efforts would establish the technical merits andamp; feasibility of a simulation- based technology for predictive stochastic analysis andamp; multi-disciplinary engineering evaluation of novel micro- fluidic devices that addresses the need for efficient andamp; accurate performance assessment of such devices in practical (often uncertain/variable) operating scenarios. It could subsequently be utilized by biomedical engineers to foster the rapid development of robust next-generation devices that operate reliably within desired operating performance specifications, such as diagnostic tools with improved detection sensitivity andamp; specificity, and drug discovery platforms with enhanced reconstitution of complex cellular interactions. These can play a crucial role in rapid short-term response to control the spread of infections andamp; to mitigate disease outbreaks, while also offering improved solutions for enhancing long-term access to primary healthcare andamp; comprehensive disease treatment, thereby significantly improving public health, particularly in resource constrained settings.Infectious ailments affect more than 400M people worldwide annually, accounting for almost 30% of disability- adjusted-life-years (DALYs, i.e. number of years lost due to ill-health, disability or early death), and for many of these conditions, bringing affordable diagnosis andamp; effective therapeutic drugs to the point-of-need is key to reducing morbidity and mortality. In this regard, micro-fluidics technology can be used to develop diagnostic tools for pathogen detection that offer quick turnaround, clinically relevant detection limit, andamp; portability, and drug discovery platforms for therapeutic drug synthesis that offer rapid screening, high throughput, andamp; reduced cost. The rising popularity of such technology and its growing demand is driving innovation toward devices that need to exhibit precision, durability, repeatability andamp; reliability, and the proposed stochastic simulation-based technology would support the development of such robust solutions, resulting in safer, more effective biomedical devices that improve public health globally.