COMPLEX SYSTEMS BIOLOGY OF MAMMALIAN CELL CYCLE SIGNALING IN CANCER by JAYANT AVVA Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Advisor: Dr. Sree N. Sreenath Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY May, 2011 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of JAYANT AVVA candidate for the PhD degree *. (signed) SREE N. SREENATH (chair of the committee) KENNETH A. LOPARO VIRA CHANKONG MIHAJLO D. MESAROVIC JAMES W. JACOBBERGER (date) 12/01/2010 *We also certify that written approval has been obtained for any proprietary material contained therein. Copyright © 2011 by Jayant Avva All rights reserved Table of Contents LIST OF TABLES .................................................................................................vi LIST OF FIGURES .............................................................................................. vii ACKNOWLEDGEMENTS .....................................................................................xi ABSTRACT ........................................................................................................ xiii 1. INTRODUCTION ....................................................................................... 1 1.1. Overview ................................................................................................. 1 1.2. Chapter Organization .............................................................................. 1 1.3. Motivation ................................................................................................ 1 1.3.1. Cell Cycle ...................................................................................... 5 1.4. Computational models ............................................................................. 6 1.4.1. Necessity of dynamics ................................................................... 6 1.4.2. Paucity of organized time profile data in cell signaling .................. 7 1.4.3. Extracting dynamics out of statically sampled data ..................... 11 1.5. State of the art ....................................................................................... 13 1.6. Thesis contribution ................................................................................ 16 1.7. Thesis organization ............................................................................... 18 2. BACKGROUND ....................................................................................... 20 2.1. Overview ............................................................................................... 20 2.2. Chapter Organization ............................................................................ 20 2.3. Introduction ............................................................................................ 21 2.4. Systems Biology .................................................................................... 21 2.4.1. Different types of System Biologies ............................................. 22 2.4.2. Complex Systems Biology ........................................................... 25 2.4.3. Hierarchical and multi-level paradigm- concepts and significance ... .................................................................................................... 27 2.5. Cross-level causality .............................................................................. 29 2.5.1. Multiscale modeling ..................................................................... 32 2.6. Modeling ................................................................................................ 33 2.6.1. Importance of modeling ............................................................... 33 2.6.2. Contextual view of types of models in cancer biology studies ..... 34 2.6.3. Mathematical and Computational Models .................................... 36 i 2.6.4. Phenomenological vs. Mechanistic Models ................................. 37 2.6.5. Static vs. Dynamic Models ........................................................... 37 2.6.6. Deterministic vs. Probabilistic Models.......................................... 38 2.6.7. Dominant relationship modeling: Unmodeled dynamics .............. 39 2.6.8. Modeling Approaches: How does one go about modeling ........... 40 2.6.9. Modeling errors and rectification .................................................. 42 2.6.10. Mathematical formalism ............................................................... 43 2.6.10.1. Use of mass action modeling: An example ............................. 44 2.6.10.2. General system modeling: Using mass action modeling ......... 46 2.6.11. Calibration and Validation ............................................................ 49 2.7. Role of Data in Building Predictive Models ............................................ 54 2.7.1. Data measurement introduction ................................................... 55 2.7.2. Data measurement processes ..................................................... 60 2.7.3. Western Blotting .......................................................................... 61 2.7.4. Flow Cytometry ............................................................................ 66 2.8. Data driven systems biology thinking .................................................... 71 2.8.1. In vivo data .................................................................................. 71 2.8.2. Ex vivo data ................................................................................. 72 2.8.3. In vitro data .................................................................................. 73 2.8.4. Measurement decisions ............................................................... 74 3. TIME PROFILE EXTRACTION FROM WET LAB DATA ......................... 79 3.1. Overview ............................................................................................... 79 3.2. Chapter Organization ............................................................................ 79 3.3. Introduction ............................................................................................ 80 3.4. Importance of cytometry data ................................................................ 81 3.5. State of the art ....................................................................................... 82 3.5.1. Classification/comparison of time profile data generation methods . .................................................................................................... 82 3.5.2. Need for our method .................................................................... 83 3.6. Dynamic time profile extraction methodology ........................................ 84 3.6.1. Generic methodology................................................................... 85 3.6.1.1. Experimental setup ................................................................... 85 3.6.1.2. Pre-processing .......................................................................... 87 3.6.1.3. Phase-specific processing ........................................................ 88 ii 3.6.1.4. Postprocessing ......................................................................... 89 3.6.1.5. Replicated filtered data ............................................................. 91 3.6.1.6. Testing the methodology for repeatability ................................. 91 3.6.2. Application to K562 cells .............................................................. 92 3.6.2.1. Experimental setup ................................................................... 92 3.6.2.2. Pre-processing .......................................................................... 94 3.6.2.3. Phase-specific processing ........................................................ 96 3.6.2.4. G1 and S Phase Time Course .................................................. 98 3.6.2.5. G2 Phase Time Course ............................................................ 99 3.6.2.6. M Phase Time Course ............................................................ 103 3.6.3. Postprocessing .......................................................................... 106 3.6.3.1. Single color correction ............................................................ 106 3.6.3.2. Practical issues ....................................................................... 109 3.6.3.3. Testing methodology for reproducibility .................................. 112 3.6.3.4. Testing methodology on MOLT4 cell line data ........................ 115 3.6.3.5. Reproduced filtered data ......................................................... 117 3.6.3.6. Theoretical formulation of data variation ................................. 119 3.7. CytoSys – a software for time profile extraction .................................. 122 3.7.1. Introduction ................................................................................ 122 3.7.2. Data Input .................................................................................. 124 3.7.3. Data Structure ........................................................................... 124 3.7.4. Processing protocol ................................................................... 125 3.7.5. File Structure in CytoSys ........................................................... 126 3.7.6. Salient Features ........................................................................ 127 3.7.6.1. Gaussian fits to data ..............................................................