UNIVERSITY 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.
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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).