Luận án tiến sĩ: Quantitative analysis of genetic expression responses to dynamic microenvironmental perturbation
Nghiên cứu tiến sĩ phân tích định lượng biểu hiện gen khi phản ứng với biến động môi trường vi mô. Làm sáng tỏ cơ chế điều hòa sinh học quan trọng.
University of California, San Diego
Bioengineering
Luan An
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I.Quantitative Analysis of Genetic Expression Responses
The study focuses on the quantitative analysis of genetic expression in response to dynamic microenvironmental perturbation. Understanding how living cells adapt to changing surroundings is critical in biology and medicine. This research investigates the intricate mechanisms driving cellular responses to external fluctuations. Traditional methods often provide only static snapshots, failing to capture the full complexity of dynamic interactions. This dissertation addresses that gap. It highlights the necessity for new approaches to monitor gene expression in real-time under precisely controlled, varying conditions. The work explores the profound impact of a dynamic environment on internal cellular machinery. It establishes a foundation for decoding adaptive strategies at the molecular level, moving beyond qualitative observations to robust, data-driven insights. The ultimate goal is to unravel the sophisticated network of gene regulation that underpins cellular resilience and survival.
1.1. Understanding Dynamic Environments and Cellular Responses
Living cells constantly face fluctuating conditions. They must adapt their internal states to survive. Microenvironmental perturbation can include changes in nutrient availability, pH, temperature, or the presence of signaling molecules. These dynamics trigger specific cellular responses. At the core of these responses is gene expression. Cells alter their protein production profile to match the new demands. This dissertation provides a framework for observing and quantifying these intricate adjustments. It details how the research systematically imposes controlled changes. The subsequent genetic expression patterns are then measured. The aim is to map the cause-and-effect relationships between external dynamics and internal biological processes. This understanding is vital for fields ranging from developmental biology to disease progression, where cellular adaptability plays a pivotal role.
1.2. Challenges in Monitoring Gene Expression
Monitoring gene expression in dynamic environments presents significant challenges. Conventional techniques often lack the temporal resolution needed to capture rapid cellular changes. They may also struggle with providing quantitative analysis across large cell populations over extended periods. Achieving precise control over the cellular microenvironmental perturbation is another hurdle. Replicating the complexity of in vivo conditions within a controlled experimental setup requires innovative engineering. Furthermore, the sheer volume of data generated by dynamic studies demands advanced bioinformatics and computational tools. This research directly confronts these limitations. It develops novel methodologies and platforms. These advancements enable accurate, real-time measurements of transcriptomics data. The focus is on overcoming technical barriers to yield unprecedented insights into dynamic gene regulation. The new tools facilitate a deeper, more comprehensive understanding of cellular adaptability.
II.Advanced Platform for Dynamic Microenvironmental Control
This research developed an advanced platform specifically designed for imposing and analyzing dynamic microenvironmental perturbation. The platform represents a significant leap in experimental capability. It allows for unprecedented control over the cellular surroundings. This precise control is crucial for inducing specific, reproducible changes. The system integrates innovative microfluidic technology. This technology enables the creation of highly controlled dynamic environments for living cells. The platform overcomes limitations of traditional batch culture methods, offering continuous, real-time observation. It facilitates the quantitative analysis of genetic expression under precisely modulated conditions. The entire system is engineered for long-duration experiments, capturing transient and sustained cellular responses. This holistic approach provides a robust framework for investigating complex biological phenomena. It pushes the boundaries of experimental biology, revealing new facets of gene regulation.
2.1. Novel Microfluidic Device Development
A cornerstone of this research is the development of novel microfluidic devices. These devices are meticulously engineered to create highly precise and controllable dynamic environments. The microfluidic chips allow for the rapid and accurate delivery of various chemical cues. They facilitate the creation of steep or gradual microenvironmental perturbation. Examples include the Tesla micro-Chemostat (TC) and Temporal Tesla micro-Chemostat (T2uC). These designs ensure uniform and reproducible conditions across cell populations. The devices minimize shear stress and maintain cell viability over extended experimental durations. Their compact nature allows for parallel experimentation, increasing throughput. This innovation in microfluidics is essential for generating reliable data on gene expression dynamics. It is a key enabler for performing sophisticated quantitative analysis of cellular adaptation. The designs offer versatility for a wide range of biological applications.
2.2. High Throughput Imaging and Data Processing
The platform integrates high-throughput imaging capabilities with advanced data processing. An automated microscopy system captures detailed images of cells within the microfluidic devices. This system performs long-duration time-lapse imaging, crucial for observing dynamic genetic expression. The reduction of thermally induced drift in the focal plane ensures image stability and quality. Subsequent image processing and analysis are performed using specialized software (e.g., IAISQT). This software segments individual cells, quantifies fluorescent signals (representing gene expression), and tracks cell trajectories. These tools transform raw image data into rich, quantitative analysis datasets. The robust methodologies ensure accuracy and reproducibility of the measurements. This integrated approach is vital for extracting meaningful biological insights from the complex dynamic responses. It provides a reliable pipeline for transcriptomics data acquisition and interpretation, critical for understanding cellular responses.
2.3. Controllable Dynamic Microenvironments
The platform achieves controllable dynamic microenvironments through sophisticated fluidic management. Driving and controlling fluid flow within the microfluidic devices is highly precise. This allows for the generation of specific, predefined microenvironmental perturbation waveforms. Nutrient concentrations, pH levels, or drug exposure can be rapidly switched or gradually ramped. This capability is essential for simulating physiological fluctuations and stress responses. The system ensures that cells experience reproducible and well-defined dynamic environments. This level of control is paramount for conducting rigorous quantitative analysis of gene expression. It enables researchers to isolate the effects of specific perturbations on cellular responses. The experimental setup is designed for consistency, ensuring that observed variations in genetic expression are directly attributable to the controlled environmental changes, leading to robust conclusions about gene regulation.
III.Metabolic Gene Regulation in Dynamic Cellular Environments
This section details the investigation into metabolic gene regulation within dynamic cellular environments. The research applies the developed platform to study specific biological systems. It focuses on how cells adjust their metabolic pathways in response to continuous environmental shifts. Understanding these adaptive mechanisms is crucial for comprehending cellular resilience and survival. The study provides concrete examples of genetic expression changes in key metabolic genes. It demonstrates how microenvironmental perturbation directly impacts the cellular machinery responsible for energy production and nutrient utilization. The quantitative analysis reveals specific patterns of gene expression triggered by varying external cues. These findings contribute significantly to the understanding of how cells maintain homeostasis and function under stress. The work illuminates the intricate connection between environmental dynamics and metabolic reprogramming, essential for cellular responses.
3.1. Investigating Metabolic Pathway Responses
The dissertation specifically investigates metabolic pathway responses to dynamic microenvironmental perturbation. A key focus is on the galactose utilization pathway, a well-characterized system. The study monitors the gene expression of components within this pathway. Cells are subjected to fluctuating galactose availability. This allows for direct observation of how metabolic genes are regulated in a dynamic environment. The quantitative analysis of reporter gene expression provides insights into pathway activation and repression kinetics. This helps to understand the speed and magnitude of cellular responses. The research identifies specific regulatory elements and their contribution to adaptive behavior. These detailed observations offer a mechanistic understanding of how metabolic systems cope with variability. The findings have implications for understanding metabolic diseases and developing engineered biological systems, expanding knowledge in genomics and transcriptomics.
3.2. Materials and Methods for Gene Expression Studies
Rigorous materials and methods underpin the gene expression studies. Microfluidic device fabrication follows precise protocols to ensure device reproducibility. Cell preparation and culture conditions are standardized for consistency. The choice of parent and fluorescent variant strains facilitates real-time monitoring of genetic expression. Steady-state expression characterization provides a baseline for dynamic experiments. The methodology includes detailed steps for confining microbial biofilms and microcolonies within the devices. This ensures consistent cell density and observation geometry. Advanced techniques are used for measuring reporter protein fluorescence, offering a direct readout of gene regulation. The entire experimental setup is designed to minimize external noise and maximize the accuracy of quantitative analysis. This meticulous approach ensures the reliability and validity of the observed cellular responses to microenvironmental perturbation.
IV.Computational Experimental Analysis of Gene Robustness
This section integrates computational and experimental analysis to assess gene robustness. The research explores how gene expression systems maintain stable function despite dynamic microenvironmental perturbation. Robustness is a critical property of biological systems, enabling survival in variable conditions. This dissertation uses a combined approach to quantify this resilience. Computational models are developed to simulate gene regulation networks under fluctuating inputs. These models are then validated against experimental data collected from the microfluidic platform. The quantitative analysis identifies mechanisms that confer stability to cellular responses. It investigates how transcriptional and translational processes contribute to system robustness. This interdisciplinary approach provides a comprehensive understanding of adaptive strategies, bridging theoretical predictions with empirical observations. The insights are vital for predicting cellular behavior in complex, real-world scenarios, leveraging bioinformatics for deeper understanding of genomics.
4.1. Robustness of Genetic Responses to Perturbation
The study specifically examines the robustness of genetic responses to environmental changes. Cells need to exhibit stable gene expression patterns, even with noisy or fluctuating inputs. This research quantifies how well cells buffer the effects of microenvironmental perturbation. It investigates whether specific gene regulation strategies contribute to this resilience. For example, the study assesses how feedback loops or redundant pathways enhance robustness. The quantitative analysis provides metrics for comparing the stability of different genetic circuits. This understanding is crucial for engineering more resilient synthetic biological systems. It also sheds light on evolutionary advantages of robust cellular responses. The findings demonstrate that dynamic environments often select for systems capable of maintaining essential functions despite external variability, a key aspect of genomics and transcriptomics.
4.2. Integrative Data Analysis and Modeling
Integrative data analysis and modeling are central to understanding gene robustness. Experimental data from the microfluidic platform are combined with computational simulations. Mathematical models of gene regulation are constructed. These models incorporate known biological interactions and parameters derived from experiments. The bioinformatics approach allows for the prediction of genetic expression dynamics under various dynamic environment scenarios. Discrepancies between model predictions and experimental observations guide further hypothesis testing. This iterative process refines the understanding of underlying biological mechanisms. The computational analysis helps to identify key control points and feedback mechanisms within complex networks. It provides a powerful tool for interpreting large datasets generated from quantitative analysis of cellular responses. This synthesis of theory and experiment offers a holistic view of dynamic gene expression.
V.Summarizing Insights and Future Research Directions
This section summarizes the key contributions and outlines future research directions. The dissertation presents significant advancements in the quantitative analysis of genetic expression under dynamic microenvironmental perturbation. It introduces a novel experimental platform and robust methodologies. These tools enable unprecedented insights into cellular responses and gene regulation. The research demonstrates how cells precisely adjust their gene expression profiles to adapt to fluctuating conditions. Key findings highlight the importance of understanding dynamics, not just static states. The work provides a strong foundation for future investigations into complex biological systems. It underscores the critical role of engineering in advancing fundamental biological understanding. The implications extend to fields requiring precise control over cellular behavior, from medical diagnostics to bio-production, impacting transcriptomics and genomics.
5.1. Key Discoveries in Genetic Expression Dynamics
Several key discoveries in genetic expression dynamics emerged from this research. The study successfully quantified gene expression changes in real-time within controlled dynamic environments. It revealed specific patterns of metabolic gene regulation in response to varying nutrient levels. The importance of robustness in cellular responses to microenvironmental perturbation was clearly demonstrated. The developed microfluidic platform proved effective for long-duration, high-resolution monitoring. Methodologies for image processing and quantitative analysis were validated, providing reliable data. The research also highlighted the power of integrating experimental data with computational modeling for understanding complex biological systems. These discoveries collectively advance the understanding of how cells sense, process, and respond to their ever-changing surroundings, contributing to the growing body of knowledge in genomics and bioinformatics.
5.2. Expanding the Scope of Microenvironmental Perturbation
Future research will involve expanding the scope of microenvironmental perturbation. The current platform can be adapted to explore a wider range of dynamic cues, beyond nutrient changes. This includes investigating responses to fluctuating pH, oxygen levels, mechanical stress, or combinations thereof. Applying these methods to different cell types, such as mammalian cells or multicellular systems, is another promising avenue. Further refinement of bioinformatics tools could enable even deeper quantitative analysis of transcriptomics and genomics data. Integrating multi-omics data, such as proteomics and metabolomics, would provide a more holistic view of cellular responses. The goal is to build increasingly sophisticated models of gene regulation that can predict cellular behavior in complex, heterogeneous environments. This ongoing work will continue to push the boundaries of understanding living systems in their natural, dynamic contexts.
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Tải xuống để đọc toàn bộUNIVERSITY OF CALIFORNIA, SAN DIEGO Quantitative analysis of genetic expression responses to dynamic microenvironmental perturbation A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioengineering by Wyming Lee Pang Committee in charge: Professor Jeff Hasty, Chair Professor Stuart Brody Professor David Gough Professor Alexander Hoffmann Professor Gabriel Silva 2007 UMI Number: 3245319 Copyright 2007 by Pang, Wyming Lee All rights reserved. INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted.
Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. ® UMI UMI Microform 3245319 Copyright 2007 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code.
ProQuest Information and Learning Company 300 North Zeeb Road P. Box 1346 Ann Arbor, MI 48106-1346 Copyright Wyming Lee Pang, 2007 All Rights Reserved The dissertation of Wyming Lee Pang is approved, and it is acceptable in quality and form for publication on microfilm. Chair University of California, San Diego 2007 ill For my wife Lisa Thanks for your patience 1V It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change. Charles Robert Darwin (1809-1882) English Naturalist Table of Contents Signature Page 2.
oe V Table of Contents ằŠa da. T Ặä vì List of si “ốc ga aađa ẶỪẶỪỪD. ằAẼ a ix List of Tables 2. vàn xii Acknowledgments 2.
2 cà ki kg kg va xiii Curriculum Vite 2. A4 xvi Abstract 2 0 gà kg kg gà gà kg gà k v x k ng xviii 1 Introduction. g q g kg Tà gà và vàng 1 I5.3 Dynamic environments and living cells.4 Current techniques for monitoring gene expression .1 Modeling on-chip pressures and flows.3 Driving and controlling fluid flow. ee 21 2 Platform Development and Methodology.1 Cells, Constructs, and Culture Conditions .1 Parent and fluorescent variant strains .3 Steady-state expression characterization.
Confining microbial biofilms and microcolonies .3 A quantitative, long-duration imaging platform .4 Controllable dynamic microenvironments .3 Reduction of thermally induced drift in focal plane.5 Image processing and analysis. c Q Q Q Q HQ ng ng v kg và và 71 vì 3 Metabolic gene regulation in a dynamically changing environment .2 Materials and methods.1 Microfluidic device fabrication .2 Cell preparation and culture .3 Results and Discussion. ee ee ee 80 3.3 Computational and experimental analysis of response robustness. cà gà kg kg kg ky 88 3.
v k kg xa 88 4 Summary and Future Directions. ——— 92 A MOCA: Microfluidic Open Circuit Analyzer.1 Basic description, requirements, and concepts. gà kg vn 97 A.1 Channels in parallel .2 Channels in series 2. Q Q Q Q ng nu na và Là lv à va 101 A.
c c Q ng vn vn và Là là và va 101 A.4 MOCA models for devices used in this work. 2 vo 102 “` van ee 102 `9 “4.5_ Complete source code ÍOr moca. c c c r Q ng va 109 B IAISQT: IÀIage Segmentor, Quantifer, lTacker. cv ng gà k VN cv k k k VN va 120 B.5 Regions of [nteresE.
cv ng gà kg va 122 B.7 Saving and Loading IMSQT sessions. HQ nạ vn k k v kia 125 B.2 Xlanual segmentation using SEGBDIE.1 Tracking Validation with TRACKEDIT. Trajectory Viewing with TRACKVIEW .6 Complete source code for DISQT main window.7 Complete source code for SEGEDIT .8 Complete source code for TRACKEDIT .9 Complete source code for TRACKVIEW.10 Complete source code for segmentor modules .11 Complete source code for library functions. 00 00 2 ee k kg kia 277 C.1 Automated microscopy platform.
cv nu kg KV 277 C.2 Generalized light paths .2 Waveform generation platform 2. eee ee 282 D Microfluidic Devices 2. Q Q Q LH ng ng kg kg kg kg KV k kg 290 D.1 TC: Tesla miero-Chemosflat. uc cu cu Q .Ặ(ẼẰ CC aaaa.2 Device Schematic and Port Âssignments.2 T2uC: Temporal Tesla micro-Chemostat.
cu kg ki Và V V g V V Và 292 D.22_ Device Schematic and Port Àssignments.3 Glial Network Stimulator. Quy vẻ vn 303 D. cu kg Vy V V g V V Và 303 D.2 Device Schematic and Port Âssignments.4 DynaGrad: Dynamic Chemical Gradient Device. ng ng k kg k k k k Nà 305 D.2 Device Schematic and Port Assignments.
305 E Computational code for T?C data simulations and analysis. Model simulation code. 329 vill List of Figures Figure 1.1 Cartoon diagram of the galactose utilization pathway .2 Node/Segment schematie of a fuidie “t”-junetion.3 Node/Segment schematic of a fluidic cross-junction .4 Node/Segment schematic of a fluidic h-cross .5 Microfuidic device fabricatlon DFOC@SS. uc cv cv ru el 18 Figure 2.1 GAL2 expression in S.
cerevisiae YPH499, YPH500, and K699.2 Yeast transformation using pKT derived fluorescent fusion protein vectors.3 Steady-state galactose induction for S. cerevisiae YPH499 and K699 30 Figure 2.4 Steady-state glucose repression for S. cerevisiae YPH499 and K699 .5 Schematic diagram of the “Sticky-Pad” device.6 Patterning of “Sticky-Pads®, 2.7 Cellular growth on “Sticky-Pads” 2. ee ee ee 40 Figure 2.8 Colony comets on a “Sticky-Pad” 2.9 The XLC growth chamber array, 2.11 TuwCadvection/diffusion analysis schematic .12 1D representation oŸ the 'ÏuC with difusive and advective transport 47 Eigure 2.13 Comparison of model and experimental ˆ[C large molecule transport ol Eigure 2.14 Analytical of time evolved 1D concentration profiles under various advective velocities Ặ HH ÊäšẶỪ.15 Comparison of model and experimental TC small molecule transport .16 Nutrient transport in a fully confluent TwC microchamber .17 Advantages of monolayer imaging.
eee ee ees 55 Figure 2.18 A dynamically controlled gradient profile device .19 The T?uC oe 58 Figure 2.20 Cartoon depiction of laminar interface guidance .21 Linearly graded mixing output .22 Characterization of on-chip waveform generation .23 Nutrient transport in a confluent T?wC growth chamber.24 Loading of the T?uC. Quy ky àa 64 Figure 2.25 Autofocus pattern using dry air chambers and fluorescent illumination.1 Simulated and experimental expression trajectories in response to sinusoidal perturbation at varying frequencies.2 Schematic of coupled galactose and glucose regulatory networks used to derive the computational model.3 Induction/repression dynamic response of S. cerevisiae YPH499 and K699 86 Figure 3.4 Amplitude ratio and phase shift profiles from experimental and simulated 610 TT.I Node/Segment schematic of a fluidic h-cross .2 Graphical output of a MOCA simulation of an h-cross microfluidic system. The tooltip is displayed when hovering the mouse pointer over the node label.
Similar tooltips are available for the segment labels.3 Graphical results for MOCA simulation of the TuwC microfluidic device 103 Figure A.4 Graphical results for MOCA simulation of the T?C microfluidic device 105 Figure B.1 The IMSQT main window .2 Regions of interest definition panel in the DISQT main window.3 Channels definition panel in the MISQT main window.4 Segmentation panel in IMSQT main window .5 SEGEDIT manual segmentation editor.6 SEGEDIT subpanels: (a) morphological operations panel, (b) object editing, and (c) morphological filtering, 2. ee va 130 Figure B.7 Object quantification panel in IMSQT main window .8 Object tracking panel in IMSQT main window .9 TRACKEDIT object tracking validation viewer .10A sample of TRACKEDIT object display.11The TRACKEDIT display control panel.12Default graphical output using TRACKVIEW.13Data smoothing using TRACKVIEW.1 Imaging optical train and light paths .2 Waveform generation svstem configuration .1 Device schematic for TwC. Inset displays a magnified view of the growth chamber, 2.2 Device schematic for T?C. Insets display magnified views of the growth chamber and on-chip media switch.3 Device schematic for glial network stimulation device.
Insets display mag- nified views of the growth chamber and on-chip media switch.4 Device schematic for dynamic gradient device. Inset displays magnified views of the on-chip media switch. 305 Xi List of Tables Table 2.1 Yeast parent strains 2.2 Excitation and emission spectral maxima of fluorescent proteins. kg kg kg KT V va 25 Table B.1 A sample trajectory link table.
Q ky và y vàna 142 Table D.1 Master mold feature height specifications. ‘Photoresists are SU-8 unless otherwise specified. *These layers were patterned additively (e. no development step between this and the prior layer).
ch va 290 Table D.2 Port assignments for TC.3 Master mold feature height specifications. ‘Photoresists are SU-8 unless otherwise specified. *These layers were patterned additively (e. no development step between this and the prior layer) 2.4 Port assignments for T7uC.
Q Q Q ng va 293 Table D.5 Master mold feature height specifications. ‘Photoresists are SU-8 unless otherwise specified.6 Port assignments for the glial network stimulation device.7 Master mold feature height specifications. ‘Photoresists are SU-8 unless otherwise specified. * Bacterial and yeast devices require patterning the gradient outflow channel with adhesion molecules such as polylysine (bacterial) or Conavalin- A (yeast), gà ngà gà gà và v g k k k xxx va 305 Table D.8 Port assignments for the dynamic gradient device.
305 Xl Acknowledgments It’s been a long six years and I’ve been fortunate to have met so many interesting people and had more than my fair share of unique and great experiences. I’m not going to say that the journey was easy, and I’m sure that no one in their right mind would have guaranteed that. In fact, some of the most difficult times I’ve had to date were during my graduate career. These are moments spent wondering if the sum of who you already are and who you want to be is worth the next step in the plans you’ve made for yourself.
It’s cliché I know, but I couldn’t have made it here with out the unrelenting support of my colleagues, friends, and family. First, I owe immense gratitude to my professional colleagues, without whom this work would not have been possible. Hasty for providing financial support and valuable research advice throughout my doctoral studies, and having the patience to see everything through. In addition, I’m fortunate to have been a part of the integrative, collab- orative, and supportive environment provided by the members of the Systems Biodynamics Lab, and likewise, I am indebted to Jesse, Chris, Mike, and Ben who helped me edit and refine this dissertation.
Jennifer and Natalie, your skills at the bench are as good as your scientific savvy, and I'll be forever thankful for all your assistance with getting my yeast variants made. Matt and Dmitri, you guys work in numbers, theory, and code like artists in fine oils, and are the best computational model builders I know. Moreover, I would also xiii like to thank Dr. Schmid-Schoenbein, who has always been both kind and supportive, and the first of many bioengineering faculty to get me truly excited in research and the hunt for scientific answers.
Lastly, I would like to thank Dr. Groisman, even though our relation- ship wasn’t the best, he gave me a solid, and reliable foundation in microfluidic design and fabrication, for which I am sincerely grateful. More than anything I'd like to thank my friends who were always there to rejoice in the successes and help through the unavoidable setbacks be they professional or personal. Jennifer, Chris, Lauren, and Jessica, you were always available at a moments notice for coffee, be it at CUPs for a short break, Mandeville for a walk and a talk, the “triple-S” for a quick snack, or Expresso happy hour at the Grove.
There’s no need to explain how these moments have helped keep me sane (as well as thoroughly awake).
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