Analysis and Control of Transcription Regulatory Networks in Mammalian Cells
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The Open University Degree of Doctor of Philosophy PhD Thesis Analysis and control of transcription regulatory networks in mammalian cells Director of Studies: Candidate: Dr. Diego di Bernardo Chiara Fracassi Supervisor: Prof. Mario di Bernardo ARC: Telethon Institute of Genetics and Medicine Years 2010/2014 ii "[...]any replay of the tape [of life] would lead evolution down a path- way radically different from the road actually taken. But the consequent differences in outcome do not imply that evolution is senseless, and without meaningful pattern; the divergent route of the replay would be just as in- terpretable, just as explainable after the fact, as the actual road. But the diversity of possible itineraries does demonstrate that eventual results can- not be predicted at the outset. Each step proceeds for cause, but no finale can be specified at the start, and none would ever occur a second time in the same way, because any pathway proceeds through thousands of improbable stages. Alter any early event, ever so slightly and without apparent import- ance at the time, and evolution cascades into a radically different channel." Stephen Jay Gould Wonderful Life: The Burgess Shale and the Nature of History Contents LIST OF TABLES vii LIST OF FIGURES vii 1 Transcriptional network motifs 1 1.1 Discerning molecular interactions: biological networks . .1 1.2 Transcription regulatory networks . .3 1.2.1 Motifs . .5 1.2.2 Modularity of networks . .9 1.3 Network dynamics . 10 1.3.1 Mathematical representation of network dynamics: ODEs 12 1.4 Translating molecular mechanisms to physiological properties through modelling . 16 2 Introduction to Control Theory 18 2.1 The Control problem . 18 2.2 Input/Output systems . 20 iii CONTENTS iv 2.2.1 Feedback control . 22 2.2.2 Relay controller . 23 2.2.3 PID controller . 25 2.3 Applications of Control Theory to biological systems . 30 3 The Positive Feedback Loop 33 3.1 Construction of a model Positive Feedback Loop . 34 3.2 Experimental investigation of the dynamic behaviour of the PFL and NoPFL networks . 41 3.2.1 Derivation of model parameters form experimental data 43 3.3 Implications for dynamic properties of the PFL . 46 4 Noise and robustness in gene networks: the role of miRNAs 50 4.1 Positive and Negative Feedback Loop: an unexpected topology 51 4.2 Construction of the PNFL genetic network . 53 4.2.1 Mathematical model of the PNFL network . 58 4.3 Dynamical properties of the PNFL network . 60 4.4 Role of miRNAs in buffering noise . 66 5 Control of gene expression in mammalian cells 73 5.1 The cellular model: Tet-OFF system . 74 5.1.1 Mathematical model of tetO7 -YFP cell line . 75 5.2 Calibration experiments . 77 5.2.1 Doxycycline and Tetracycline in dynamical control . 82 CONTENTS v 5.2.2 Calibration experiments using Tetracycline . 87 5.3 Automatic control of gene expression . 91 5.3.1 Implementation of the control scheme . 92 5.3.2 In silico set point experiments: Relay controller . 95 5.3.3 In vivo set point experiments: Relay controller . 96 6 Towards the analysis and control of endogenous networks 105 6.1 Dynamical analysis of gene expression: the hes1 example . 106 6.2 hes1 mRNA reporters . 108 6.2.1 Estimation of reporters' half-lives . 110 6.2.2 Generation of stable clones for hes1 mRNA reporters . 111 6.3 Hes1 protein reporters . 117 7 Concluding remarks 122 7.1 Analysis of synthetic and endogenous biological networks . 122 7.2 Control of gene expression in mammalian cells . 125 A Materials and Methods 128 A.1 Generation of genetic circuits and reporter vectors . 128 A.1.1 Construction of the PFL, NoPFL and PNFL genetic circuits . 128 A.1.2 Construction of the hes1 reporters . 131 A.2 Cell culture procedures . 135 A.2.1 Lentiviral transduction . 136 CONTENTS vi A.2.2 Generation of clones by stable transfection . 137 A.2.3 Phenotypic analysis of fluorescence by FACS analysis . 138 A.2.4 Determination of reporter protein's half-lives . 139 A.3 Real-time PCR procedures . 140 A.3.1 Extraction and analysis of genomic DNA of clones . 140 A.3.2 Expression analysis of mRNA levels . 141 A.4 Microfluidics procedures . 143 A.4.1 Microfluidic device fabrication protocol . 143 A.4.2 Microfluidic device cell loading protocol . 144 A.5 Fluorescence time-lapse microscopy experiments . 145 A.5.1 Switch-off experiments of PFL and NoPFL . 146 A.5.2 Doxycycline treatment of PFL and PNFL clones in the microfludic device . 146 A.5.3 Calibration and control experiments in the microfluidic device . 147 B Software 148 B.1 Image processing . 148 B.1.1 Image processing with CellProfiler2.0 . 149 B.2 Control algorithm . 149 C Plasmids maps 159 List of Tables 3.1 Parameters identified for the PFL and NoPFL models: para- meters values and relative standard deviations are reported for each parameter. 45 4.1 Parameters for the PNFL system. 60 5.1 Parameters values for the tetO7 -YFP mathematical model. 77 vii List of Figures 1.1 Levels of organization of transcription regulatory networks. .4 1.2 Examples of the most common transcriptional motifs. .6 1.3 Phase plane of a positive feedback loop. 15 2.1 Classical closed-loop control scheme. 20 2.2 Scheme of the relay controller. 24 2.3 Scheme of the relay controller with hysteresis. 25 2.4 Operating principle of the Pulse-Width Modulator (PWM) using the intersective method. 29 3.1 Scheme of the Positive Feedback Loop cassette. 36 3.2 Scheme of the NoPFL circuit. 36 3.3 Screening of PFL and NoPFL clones. 39 3.4 Determination of d2EYFP half-life. 40 3.5 Switch off time-course of the PFL and NoPFL cell populations. 42 3.6 Analysis of the switch off time for the PFL and NoPFL networks. 48 viii LIST OF FIGURES ix 4.1 Scheme of the PFL and PNFL circuits. 55 4.2 Scheme of PFL and PNFL clones. 56 4.3 Screening of PFL and PNFL clones. 57 4.4 Study of the level of tTA mRNA as a function of the strength of the miRNA . 62 4.5 Experimental validation of the bistable behaviour of the PFL. 65 4.6 The PNFL circuit is a bistable switch with faster dynamics. 67 4.7 Long term robustness analysis of the PNFL toggle switch. 68 4.8 Noise assessment of PFL and PNFL systems. 71 5.1 Scheme of the tetO7 -YFP transcriptional unit. 75 5.2 Time profile of fluorescence expression in tetO7 -YFP cells. 79 5.3 Comparison of the growth rate of CHO cells in the microfluidic device and standard plastic treated culture dishes. 80 5.4 Response of tetO7 -YFP cells to a time-varying exposure to 1 µg/ml of Doxycycline. 81 5.5 Response of the tetO7 -YFP cassette to different concentra- tions of Doxycycline. 83 5.6 Doxycycline and Tetracycline affect tetO7 -YFP expression with different dynamics. 86 5.7 Response of the tetO7 -YFP system to 1 ng/ml of Tetracycline in microfluidic culture. 88 LIST OF FIGURES x 5.8 Response of the tetO7 -YFP system to 100 ng/ml of Tetracyc- line, provided as a temporary pulse of 700 minutes. 89 5.9 Implementation of the control platform. 94 5.10 In silico simulation of the performance of the relay controller. 97 5.11 In vivo set point control of the tetO7 -YFP system using a relay controller. 99 5.12 In vivo set point control of the tetO7 -YFP system using a relay controller. 101 5.13 In vivo set point control of the tetO7 -YFP system using a relay controller. 103 6.1 Fluorescent reporters for in vivo visualization of hes1 activity. 110 6.2 Fitting of Cycloheximide data of three different fluorescent reporter proteins to infer their degradation rates. 112 6.3 mRNA expression of endogenous hes1 and reporter proteins in 5 selected clones at the steady state. 114 6.4 In vivo time-lapse microscopy of hes1 -VNP clone #23. 115 6.5 mRNA expression of endogenous hes1 and reporter proteins in hes1 -d2EYFP and -VNP clones after a serum shock. 116 6.6 The Hes1 protein reporter. 118 6.7 FACS analysis of the N-box reporter shows specificity of the Hes1 protein for the N-box sequence. 119 6.8 Time-course profile of the N-box reporter activity. 120 LIST OF FIGURES xi C.1 Map of the PFL lentiviral vector. ................. 160 C.2 Map of the NoPFL lentiviral vector. ................ 161 C.3 Map of the NFL lentiviral vector. ................. 162 C.4 Map of the hes1 -VNP reporter vector. ............... 163 C.5 Map of the hes1 -UbVenus reporter vector. ............. 164 C.6 Map of the hes1 -d2EYFP reporter vector. ............. 165 C.7 Map of the hes1 -UbV76GFP reporter vector. ........... 166 C.8 Map of the Nbox-UbVenus reporter vector. ............ 167 Chapter 1 Transcriptional network motifs In this chapter, I will introduce the concepts of a biological network, mod- ularity of networks, network dynamics and network motifs; I will describe how to study the behaviour of biological networks in time by using Ordinary Differential Equations; I will then discuss, as examples, the most common network motifs, specifically positive and negative autoregulation. 1.1 Discerning molecular interactions: biolo- gical networks The minimal unit of life, the cell, no matter how simple, is a very complex and dynamical environment. At the molecular level, all chemical reactions 1 1.1 Discerning molecular interactions: biological networks 2 and physical interactions are determined by the laws of thermodynamics and by stochasticity.