Fault Modeling and Defect Analysis on Digital Microfluidics Based Biochips

An emerging technology Digital Microfluidics Biochips, an integrating concept of electronics and biology is anticipated to play an important role in the area of medical diagnostics, drug discovery, DNA sequencing, toxicity supervising and other bio-chemical applications. This bio-microelectromechanical system is supposed to have defects if it fails to accomplish the specified assignment dedicated to it. This paper focuses on different possible cases responsible for the misbehavior of the bio-MEMS

Fluigi: an end-to-end software workflow for microfluidic design

One goal of synthetic biology is to design and build genetic circuits in living cells for a range of applications with implications in health, materials, and sensing. Computational design methodologies allow for increased performance and reliability of these circuits. Major challenges that remain include increasing the scalability and robustness of engineered biological systems and streamlining and automating the synthetic biology workflow of “specify-design-build-test.” I summarize the advances in microfluidic technology, particularly microfluidic large scale integration, that can be used to address the challenges facing each step of the synthetic biology workflow for genetic circuits. Microfluidic technologies allow precise control over the flow of biological content within microscale devices, and thus may provide more reliable and scalable construction of synthetic biological systems. However, adoption of microfluidics for synthetic biology has been slow due to the expert knowledge and equipment needed to fabricate and control devices. I present an end-to-end workflow for a computer-aided-design (CAD) tool, Fluigi, for designing microfluidic devices and for integrating biological Boolean genetic circuits with microfluidics. The workflow starts with a ``netlist" input describing the connectivity of microfluidic device to be designed, and proceeds through placement, routing, and design rule checking in a process analogous to electronic computer aided design (CAD). The output is an image of the device for printing as a mask for photolithography or for computer numerical control (CNC) machining. I also introduced a second workflow to allocate biological circuits to microfluidic devices and to generate the valve control scheme to enable biological computation on the device. I used the CAD workflow to generate 15 designs including gradient generators, rotary pumps, and devices for housing biological circuits. I fabricated two designs, a gradient generator with CNC machining and a device for computing a biological XOR function with multilayer soft lithography, and verified their functions with dye. My efforts here show a first end-to-end demonstration of an extensible and foundational microfluidic CAD tool from design concept to fabricated device. This work provides a platform that when completed will automatically synthesize high level functional and performance specifications into fully realized microfluidic hardware, control software, and synthetic biological wetware

Optimisation of microfluidic experiments for model calibration of a synthetic promoter in S. cerevisiae

This thesis explores, implements, and examines the methods to improve the efficiency of model calibration experiments for synthetic biological circuits in three aspects: experimental technique, optimal experimental design (OED), and automatic experiment abnormality screening (AEAS). Moreover, to obtain a specific benchmark that provides clear-cut evidence of the utility, an integrated synthetic orthogonal promoter in yeast (S. cerevisiae) and a corresponded model is selected as the experiment object. This work first focuses on the “wet-lab” part of the experiment. It verifies the theoretical benefit of adopting microfluidic technique by carrying out a series of in-vivo experiments on a developed automatic microfluidic experimental platform. Statistical analysis shows that compared to the models calibrated with flow-cytometry data (a representative traditional experimental technique), the models based on microfluidic data of the same experiment time give significantly more accurate behaviour predictions of never-encountered stimuli patterns. In other words, compare to flow-cytometry experiments, microfluidics can obtain models of the required prediction accuracy within less experiment time. The next aspect is to optimise the “dry-lab” part, i.e., the design of experiments and data processing. Previous works have proven that the informativeness of experiments can be improved by optimising the input design (OID). However, the amount of work and the time cost of the current OID approach rise dramatically with large and complex synthetic networks and mathematical models. To address this problem, this thesis introduces the parameter clustering analysis and visualisation (PCAV) to speed up the OID by narrowing down the parameters of interest. For the first time, this thesis proposes a parameter clustering algorithm based on the Fisher information matrix (FIMPC). Practices with in-silico experiments on the benchmarking promoter show that PCAV reduces the complexity of OID and provides a new way to explore the connections between parameters. Moreover, the analysis shows that experiments with FIMPC-based OID lead to significantly more accurate parameter estimations than the current OID approach. Automatic abnormality screening is the third aspect. For microfluidic experiments, the current identification of invalid microfluidic experiments is carried out by visual checks of the microscope images by experts after the experiments. To improve the automation level and robustness of this quality control process, this work develops an automatic experiment abnormality screening (AEAS) system supported by convolutional neural networks (CNNs). The system learns the features of six abnormal experiment conditions from images taken in actual microfluidic experiments and achieves identification within seconds in the application. The training and validation of six representative CNNs of different network depths and design strategies show that some shallow CNNs can already diagnose abnormal conditions with the desired accuracy. Moreover, to improve the training convergence of deep CNNs with small data sets, this thesis proposes a levelled-training method and improves the chance of convergence from 30% to 90%. With a benchmark of a synthetic promoter model in yeast, this thesis optimises model calibration experiments in three aspects to achieve a more efficient procedure: experimental technique, optimal experimental design (OED), and automatic experiment abnormality screening (AEAS). In this study, the efficiency of model calibration experiments for the benchmarking model can be improved by: adopting microfluidics technology, applying CAVP parameter analysis and FIMPC-based OID, and setting up an AEAS system supported by CNN. These contributions have the potential to be exploited for designing more efficient in-vivo experiments for model calibration in similar studies