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

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field

Biochip-Integrable Microfluidic Particle Separation Techniques for Biomedical Use

Biochip-integrable sorting and separation of micron-sized particles have an increasing importance in biomedical diagnostics, biochemical analyses, food and chemical processing, and environmental assessment. By employing the unique characteristics of microscale flow phenomena, various techniques have been established for fast and accurate separation, and to sort cells or particles in a continuous manner. As in classical separation procedures, the biochip-integrable size-fractionation of particles or cells could be realized by passive or active way. Passive procedures, which do not require external force-field, utilize the interaction between particles-particle, flow-particle, and the channel structure-particle to separate different-sized particles. Meanwhile, the active separation techniques make use of external force-field in various forms. This doctoral thesis provides a novel biochip-integrable pathogen detection device (Flow Through Nematode Filter, FTNF), and a novel application of an asymmetric column structure, which called deterministic lateral displacement (DLD) device. The working principles are explained in detail, and performances of the devices are discussed with the results of the measurements. The main target of this represented work is applications in medicine and biomedical research but we are also open for other application areas. The use of these simple microfluidic devices will make it possible to extend the use of cell-sorting to the point of care, closer to the patient at the clinic or in the field

A Platform for Fast Detection of Let-7 Micro RNA Using Polyaniline Fluorescence and Image Analysis Techniques

The project describes a new strategy for transducing hybridization events through modulating intrinsic properties of the electroconductive polymer polyaniline (PANI). When DNA based probes electrostatically interact with PANI, its fluorescence properties are increased, a phenomenon that can be enhanced by UV irradiation. Hybridization of target nucleic acids results in dissociation of probes causing PANI fluorescence to return to basal levels. By monitoring restoration of base PANI fluorescence as little as 10-11 M (10 pM) of target oligonucleotides could be detected within 15 minutes of hybridization. Detection of complementary oligos was specific, with introduction of a single mismatch failing to form a target-probe duplex that would dissociate from PANI. Furthermore, this approach is robust and is capable of detecting specific RNAs in extracts from animals. This sensor system improves on previously reported strategies by transducing highly specific probe dissociation events through intrinsic properties of a conducting polymer without the need for additional labels. The change in fluorescence property of PANI by oligo immobilization and hybridization with mimic let-7 is measured by fluorescence microscope and the image analyzed by MATLAB. A heuristic algorithm determines color threshold of the fluorescent active image. This image segmentation helps to determine the average pixel intensity representing the active image foreground of PANI fluorescence triggered by DNA immobilization and hybridization process. This would help us to quantify response of PANI based biosensor for detecting micro RNA let-7

Micro/Nano Devices for Blood Analysis, Volume II

The development of micro- and nanodevices for blood analysis continues to be a growing interdisciplinary subject that demands the careful integration of different research fields. Following the success of the book “Micro/Nano Devices for Blood Analysis”, we invited more authors from the scientific community to participate in and submit their research for a second volume. Researchers from different areas and backgrounds cooperated actively and submitted high-quality research, focusing on the latest advances and challenges in micro- and nanodevices for diagnostics and blood analysis; micro- and nanofluidics; technologies for flow visualization and diagnosis; biochips, organ-on-a-chip and lab-on-a-chip devices; and their applications to research and industry

A Review of the Teaching and Learning on Power Electronics Course

—In this review, we describe various kinds of problem and solution related teaching and learning on power electronics course all around the world. The method was used the study of literature on journal articles and proceedings published by reputable international organizations. Thirtynine papers were obtained using Boolean operators, according to the specified criteria. The results of the problems generally established that student learning motivation was low, teaching approaches that are still teacher-centered, the scope of the curriculum extends, and the physical limitations of laboratory equipment. The solutions offered are very diverse ranging from models, strategies, methods and learning techniques supported by information and communication technology

BACTERIA ANALYSIS BY USING A SUPERVISED MACHINE LEARNING ALGORITHM BASED ON DROPLET MICROFLUIDICS

Sepsis is a major medical problem and massive resources have been invested in developing and evaluating alternative treatments. Statistics indicate that sepsis causes between one third and one half of all hospital deaths in the United States. Sepsis has a high impact on health care in the US, with direct sepsis costs in 2009 exceeding $15.4 billion. A research study found that a 1-hour delay in appropriate antimicrobial care resulted in a 7% - 10% rise in mortality. Several professional societies seek to reduce sepsis mortality by targeting the timely use of diagnostic tests and antimicrobial therapy. The diagnostic instruments available to clinicians to identify the suspected pathogen do not make a timely intervention possible. Up to 5 days of incubation are needed for blood cultures, the majority of bacteria being detected after 12–48 h. Therefore, fast and simple techniques are required for rapid bacterial cell detection and quantification. By using droplet microfluidics and a machine learning algorithm, the objective of this study was to propose a technology that analyzes images of bacterial cells by image processing and Support Vector Machines algorithm to classify droplets containing the bacteria. The accuracy of the proposed technology was 97.2 % for a trained SVM model and with the complete identification and classification of droplets

Micro/nano devices for blood analysis

[Excerpt] The development of microdevices for blood analysis is an interdisciplinary subject that demandsan integration of several research fields such as biotechnology, medicine, chemistry, informatics, optics,electronics, mechanics, and micro/nanotechnologies.Over the last few decades, there has been a notably fast development in the miniaturization ofmechanical microdevices, later known as microelectromechanical systems (MEMS), which combineelectrical and mechanical components at a microscale level. The integration of microflow and opticalcomponents in MEMS microdevices, as well as the development of micropumps and microvalves,have promoted the interest of several research fields dealing with fluid flow and transport phenomenahappening at microscale devices. [...

Digital Microfluidics for Isothermal Nucleic Acid Amplification: Exploring Sensing Methodologies

Digital Microfluidics (DMF) has recently emerged as a promising candidate for nucleic acid amplification for molecular diagnostics, by virtue of its precise control over unit droplets without the need of any propulsion devices, ease of integration with chemical/biological reac-tions and multiplex assay capabilities. Nevertheless, current scientific research is still far from accomplishing the full potential of the technique, so new, innovative nanotechnology/biotech-nology hybrid approaches are necessary. As such, the purpose of this work is to contribute for the paradigm shift of nucleic acid amplification from central laboratories to point-of-care (POC) by designing and fabricating DMF devices compatible with isothermal nucleic acid amplifica-tion (loop-mediated isothermal amplification - LAMP). For biological validation of the devices, detection of cancer biomarker c-Myc is performed, and further real-time amplification moni-toring is attempted through several methodologies, namely fluorescence, impedance and elec-trochemical measurements. The DMF devices produced herein enable optimal temperature control, crucial for LAMP reactions, and further allow for a novel methodology of reagent mix-ing, based on dual actuation with back-and-forth motion and actuation frequency tuning. Such innovations lead to successful amplification of 0.5 ng/μL or 90 pg of c-Myc in one hour, in line with the range reported in the literature, and further monitoring of the LAMP reaction profile by microscopy-based fluorescence measurements. Impedimetric and electrochemical method-ologies did not meet the tight criteria required for biomarker detection, yet the developments achieved herein open the path for other applications. Lastly, the dielectric layer (key element of a DMF device) was optimized to assure long reactions (up to two hours) without device degradation.A microfluídica digital (MFD) surgiu como uma tecnologia promissora para amplificação de ácidos nucleicos em diagnóstico molecular, permitindo controlo sobre gotas unitárias sem necessidade de dispositivos de propulsão, facilidade de integração com reações químicas/bi-ológicas e capacidade de realização de ensaios simultâneos. Contudo, a investigação científica atual ainda está longe de atingir o máximo potencial da técnica, pelo que são necessárias abordagens novas, inovadoras e híbridas de nanotecnologia e biotecnologia. Como tal, o pro-pósito deste trabalho é contribuir para a mudança de paradigma da amplificação de ácidos nucleicos de laboratórios centralizados para ponto-de-atendimento (PDA) através do desenho e fabricação de dispositivos de MFD compatíveis com amplificação isotérmica de ácidos nu-cleicos (loop-mediated istothermal amplification - LAMP). Para validação biológica dos dispo-sitivos, será detetado o biomarcador de cancro c-Myc, e testada a monitorização da amplifica-ção em tempo real através de várias metodologias, nomeadamente medidas de fluorescência, impedância ou medidas eletroquímicas. Os dispositivos MFD produzidos permitem um con-trolo ótimo da temperatura, crucial para reações LAMP, e introduzem uma metodologia para mistura de reagentes, com movimentos em vaivém e ajuste da frequência de atuação. Tais inovações conduziram à amplificação de 0.5 ng/μL ou 90 pg de c-Myc em uma hora, em linha com o intervalo relatado na literatura, permitindo ainda monitorização do perfil da reação LAMP através de medidas de fluorescência mediadas por microscopia. As metodologias impe-dimétricas e eletroquímicas não cumpriram os exigentes critérios requeridos para deteção de biomarcadores, no entanto, os desenvolvimentos alcançados abrem caminho para outras apli-cações. Por último, a camada dielétrica (elemento-chave de um dispositivo MFD) foi otimizada para assegurar reações mais longas (até duas horas) sem degradação do dispositivo