Microfluidic device to study virus transmission in human society

다국어 초록 (Multilingual Abstract)

Three-dimensional (3D) microfluidic devices have revolutionized biomedical research by enabling precise control of cellular microenvironments in a highly scalable and physiologically relevant manner. Beyond their utility in cancer and drug testing models, microfluidic systems offer new avenues for exploring spatial and behavioral dynamics within complex, human-like ecosystems. This study leverages the engineering flexibility of microfluidics to investigate how viruses interact with heterogeneous environments and how such interactions can be modulated or disrupted under varying conditions. Through the development of biomimetic platforms that simulate aspects of human society at the microscale, this research aims to provide new insights into the mechanisms of viral spread and intervention efficacy in both biological and social contexts.
Chapter 2 introduces the integration of organ-on-chip (OoC) technology with nanobiosensors for viral infection monitoring. It begins by outlining key biomarkers such as cytokines and viral components, followed by a discussion on the limitations of conventional biosensing platforms. The chapter further highlights the advantages of nanobiosensors in enhancing detection sensitivity and enabling real-time, in situ analysis. Additionally, it reviews various types of OoCs and explores recent advances in combining these systems with nanobiosensors to achieve dynamic and physiologically relevant infection models.
Chapter 3 presents a microfluidic platform composed of interconnected microchambers to model virus transmission dynamics in human society. It describes the preparation of various cell populations—including infected, low-motility, and unsusceptible cells—and the use of siRNA techniques to simulate behavioral interventions such as social distancing. The chapter details quantitative assays including qRT-PCR and western blot, as well as virus propagation experiments conducted both in well plates and on-chip. Furthermore, it introduces a mathematical model to calculate the reproduction number (R₀), enabling a quantitative understanding of how different social and biological parameters influence viral spread.
Chapter 4 summarizes the contributions of microfluidic platforms in modeling viral transmission and evaluating intervention strategies, emphasizing their effectiveness in simulating complex human-like environments such as varying susceptibility, population density, and social distancing. The chapter also discusses the potential for integrating more advanced sensing technologies and behavioral modeling frameworks in future studies, offering new directions for applying microfluidic systems in infectious disease research and public health policy evaluation.
Looking ahead, the application of microfluidic platforms in virology research offers exciting opportunities to expand both experimental resolution and biological relevance. Future studies may focus on integrating immune-responsive elements, engineering more physiologically accurate tissue models, and applying machine learning to analyze transmission patterns within microfluidic networks. These developments could open new avenues for decoding host–virus interactions and accelerating the discovery of next-generation antiviral therapies.

국문 초록 (Abstract)

3차원(3D) 미세유체 장치는 세포 미세환경을 정밀하게 제어할 수 있는 생리학적으로 유의미하고 확장 가능한 플랫폼으로, 바이오메디컬 연구의 패러다임을 변화시켜왔다. 기존의 암 모델이나 약물 평가 모델을 넘어, 미세유체 시스템은 인간 사회와 유사한 복잡한 생태계 내에서의 공간적·행동학적 전파 양상을 모사할 수 있는 새로운 가능성을 제공한다. 본 연구는 미세유체 공학의 유연성을 활용하여 바이러스가 이질적인 환경과 어떻게 상호작용하며, 이러한 상호작용이 다양한 조건에서 어떻게 변화하거나 차단될 수 있는지를 규명하고자 하였다. 이를 위해 인간 사회를 마이크로스케일에서 생체모사적으로 구현한 플랫폼을 개발하였으며, 이를 통해 바이러스 전파 메커니즘과 방역 개입 전략의 효과에 대한 새로운 통찰을 제시하고자 한다.
제2장은 장기모사칩(organ-on-chip, OoC) 기술과 나노바이오센서를 통합하여 바이러스 감염을 모니터링하는 접근법을 소개한다. 사이토카인 및 바이러스 유래 물질과 같은 주요 바이오마커를 개관하고, 기존 바이오센서 기술의 한계를 짚은 후, 나노바이오센서의 민감도 향상 및 실시간 현장 분석 능력에 대해 논의한다. 또한 다양한 OoC 유형과 이를 나노센서 기술과 융합한 최신 연구 동향을 다루며, 보다 생리학적으로 유효한 감염 모델 구현의 가능성을 모색한다.
제3장에서는 상호 연결된 마이크로챔버로 구성된 미세유체 플랫폼을 이용해 인간 사회 내 바이러스 전파를 모델링하였다. 감염세포, 이동성 저하 세포, 비감수성 세포를 포함한 다양한 세포군의 조성과 함께, siRNA를 활용한 사회적 거리두기 시뮬레이션이 기술된다. 또한 qRT-PCR, western blot을 포함한 정량 분석법과 well plate 및 칩 상에서 수행된 바이러스 확산 실험이 포함된다. 아울러, 실험 데이터를 기반으로 한 재생산지수(R₀) 수학적 모델링을 통해, 사회적 및 생물학적 요인이 바이러스 확산에 미치는 영향을 정량적으로 분석하였다.
제4장에서는 미세유체 플랫폼이 감염병 전파 모델링과 방역 개입 전략 평가에 기여한 내용을 종합하며, 감수성의 차이, 인구 밀도, 사회적 거리두기와 같은 인간 사회적 요인을 모사하는 데 있어 높은 효과성을 보였음을 강조한다. 또한 향후에는 감지 기술과 행동 기반 모델링 프레임워크의 통합 가능성도 논의하며, 감염병 연구 및 공중보건 정책 평가에서 미세유체 시스템이 갖는 확장 가능성과 적용 가치를 제시한다.
향후 연구에서는 미세면역반응 요소의 통합, 생리학적으로 정밀한 조직 모델의 구현, 인공지능 기반의 전파 양상 분석 등이 주목받을 것으로 예상된다. 이러한 발전은 바이러스-숙주 간 상호작용의 이해를 심화시키고, 차세대 항바이러스 치료 전략 개발을 앞당기는 데 크게 기여할 수 있을 것이다.

목차 (Table of Contents)

• Chapter 1. Introduction and Theoretical Background 1
• 1.1 Fundamentals of microfluidic systems 1
• 1.1.1 Definition and principles of microfluidics 2
• 1.1.2 Key components and fabrication techniques 3
• 1.1.3 Fluid dynamics of microscale 6
• 1.1.4 Applications in biomedical engineering and pathogen studies 7
• 1.2 Virus transmission 11
• 1.2.1 Overview of human viruses and classification 11
• 1.2.2 Viral infection mechanisms 13
• 1.2.3 Cellular transmission modes: free-virus vs. cell-to-cell 16
• 1.2.4 Environmental and social factors in viral spread 17
• 1.3 Integration of microfluidics in viral research 20
• 1.3.1 Conventional virus-on-a-chip platforms 20
• 1.3.2 Microfluidic modeling of hostpathogen interactions 23
• 1.3.3 Microenvironment simulation: tissue barriers, immune mimicry, and flow-based control 24
• 1.3.4 Examples of microfluidic models for coronavirus infection or evolution 26
• 1.4. Object of my study 35
• 1.4.1. Issues in studying virus transmission 35
• 1.4.2 Motivation 36
• 1.4.3 Purpose of this study 38
• Chapter 2. Organ-on-chip (OoC) integrated with nanobiosensors for viral infection monitoring 40
• 2.1 Introduction 40
• 2.2 Biomarkers for viral infection monitoring 43
• 2.2.1 The impacts of cytokine and cytokine storms on viral infection 43
• 2.2.2 The other biomarkers for viral infection 47
• 2.3 Nanabiosensors for viral infection monitoring 49
• 2.3.1 Limitations of conventional biosensors for viral infection detection 49
• 2.3.2 Mechanisms of nanobiosensors for viral infection 51
• 2.4 OoC technology for viral infection monitoring 56
• 2.4.1 What are the OoCs 56
• 2.4.2 Typical types of OoCs 57
• 2.5 Integration OoC and nanobiosensors for viral infection monitoring 63
• 2.5.1 Designing factors of integration of OoC and nanobiosensors 66
• 2.5.2 Recent advances of nanobiosensors-integrated OoCs 67
• 2.5.3 Future directions of nanobiosensor-integrated OoCs 71
• 2.6 Summary 76
• Chapter 3. Recapitulating virus transmission in human society on interconnected microchambers 77
• 3.1 Introduction 77
• 3.2 Materials and methods 81
• 3.2.1 Cell culture and viruses 81
• 3.2.2 Virus propagation and measuring 50% tissue culture infectious dose (TCID50) value 81
• 3.2.3 Test of cytopathic effects (CPE) with different TCID50 82
• 3.2.4 Preparation of initial infected, low motility, and unsusceptible cells 85
• 3.2.5 Quantitative real time-PCR (qRT-PCR) 89
• 3.2.6 Western blot 92
• 3.2.7 Virus transmission on the chip 94
• 3.2.8 Virus transmission on a well plate 99
• 3.2.9 Calculation of reproduction number (R0) 100
• 3.2.10 Mathematical model for virus transmission on the chip 100
• 3.3 Results and discussions 103
• 3.3.1 Effect of the susceptible population on virus transmission 103
• 3.3.2 Effect of the initial infected population on virus transmission 110
• 3.3.3 Effect of unsusceptible population on virus transmission 116
• 3.3.4 Effect of social distancing on virus transmission 122
• 3.4 Conclusion 125
• Chapter 4. Conclusions 136
• References 137
• Appendix 154
• 논문요약 179