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Rice Scholarship Home RICE UNIVERSITY By A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE APPROVED, THESIS COMMITTEE HOUSTON, TEXAS ABSTRACT Data-driven modeling to infer the function of viral replication in a counting-based decision by Seth Coleman Cells use gene regulatory networks, sets of genes connected through a web of bio- chemical interactions, to select a developmental pathway based on signals from their environment. These processes, called cell-fate decisions, are ubiquitous in biology. Yet efforts to study cell-fate decisions are often stymied by the inherent complexity of organisms. Simple model systems provide attractive alternative platforms to study cell-fate decisions and gain insights which may be broadly applicable. Infection of E. coli by the virus lambda is one such model system. The outcome of this viral infection is dependent on the number of initially coinfecting viruses (multiplicity of infection, or MOI), which the viral regulatory network appears to `count'. Yet precisely how the viral regulatory network responds to MOI is still unclear, as is how the system is able to achieve sensitivity to MOI despite viral replication, which quickly obfuscates initial viral copy number. In this thesis, I used mathematical modeling of the network dynamics, calibrated by experimental measurements of viral replication and gene ex- pression during infection, to demonstrate how the network responds to MOI and to show that viral replication actually facilitates, rather than hinders, a counting-based decision. This work provides an example of how complex behaviors can emerge from the interplay between gene/network copy number and gene expression, whose coupling iii cannot be ignored in developing a predictive description of cellular decision-making. iv I was born not knowing and have had only a little time to change that here and there. Richard Feynman Acknowledgements I would like to thank both of my advisers, Ido Golding and Oleg Igoshin. Ido's passion for biology was what originally drew me to this project. His tireless work ethic and uncompromising standards have provided a sterling example to aspire to. Oleg's insightful critiques over the years have helped hone my ability to formulate, present and analyze scientific arguments. His patient mentoring has enabled me to tackle complex problems like the one in this thesis. I am grateful to both for their time, the opportunity they provided me, and the many lessons they've imparted. To my friends at Rice University, thank you for keeping me sane throughout the years. Through trivia nights, D&D sessions, and pilgrimages through the Houston heat to Valhalla, your companionship has made this journey far more enjoyable than it had any right to be. I would like to thank my wonderful parents, Danny and Carla, without whom none of this would have been possible. I'm constantly amazed at how kind and giving you both are, and you have provided me both the strength and ability to pursue this dream. I want to thank my in-laws, the Bisseys, Caskeys, Randalls, and Turners. I have found in you a second home, and your company has brightened these last few years. I look forward to many more adventures with you all. My supervisors in Germany, Christian Gross and Carsten Klempt, also deserve my sincere thanks. You both set me on this path, and I have done my best to carry your lessons forward. You are both exceptional mentors. I'd also like to thank Jim Schemmer. The initial seed of this quest was planted by you. I still keep a copy of Euclid's Elements in my library. vi Lastly and most importantly, I want to thank my wife Alaina for putting up with all of this. You have shared my hopes, frustrations, and worries throughout this journey. Thank you for being my rock. Contents Abstract ii List of Illustrations xi List of Tables xxx 1 Introduction 1 1.1 Cells make information-based decisions . .1 1.2 Phage lambda is a model system for studying cell-fate decisions . .2 1.3 The lambda regulatory network drives a counting-based decision through unclear mechanisms . .3 1.4 Aims of this work . .4 2 Decision making in temperate phages 6 2.1 Introduction . .7 2.2 The post-infection decision of phage lambda . .9 2.3 Counting by infecting phages . 14 2.4 The view from the single cell . 19 2.5 The decision to remain dormant . 22 2.6 Conclusion . 28 3 Bacteriophage self-counting in the presence of viral repli- cation 31 3.1 Introduction . 32 3.2 Results . 33 viii 3.2.1 In the absence of viral replication, gene expression does not diverge into lytic and lysogenic trajectories . 33 3.2.2 Modeling network dynamics reveals that viral replication is required for a lysis-to-lysogeny transition . 37 3.2.3 CII activation of PRE defines a time window for the network's response to MOI . 40 3.2.4 Changes in viral copy number outside the CII activity window do not alter the decision . 42 3.2.5 Phage replication enables the lytic choice and lowers the MOI required for lysogeny . 44 3.3 Discussion . 47 4 Future Directions 50 4.1 Extending the model to capture stochasticity in gene expression . 50 4.1.1 A stochastic reformulation of the model deviates from deterministic predictions . 50 4.1.2 Estimated single-cell lysogenization frequencies fail to capture observed trends . 56 4.1.3 Possible explanations for deviations in predicted single-cell behavior . 57 4.2 A Q-based lytic decision yields alternative predictions of infection outcome at high MOI . 58 4.2.1 The role of Q during infection . 58 4.2.2 A toy module of Q regulation predicts only lysogeny at high MOI................................ 59 4.2.3 Experimental measurements of Q mRNA dynamics are needed to extend this analysis . 66 4.3 Viral replication may enable subcellular decision-making . 67 ix 4.3.1 The subcellular decision-making hypothesis . 67 4.3.2 Control of replication by initially infecting viruses is a candidate voting mechanism . 69 A Supplementary Information for Chapter 3 72 A.1 Experimental methods . 72 A.1.1 Growth media and conditions . 72 A.1.2 Bacterial strains and plasmids . 73 A.1.3 Phage construction . 73 A.1.4 Phage infection . 77 A.1.5 Single-molecule fluorescence in situ hybridization (smFISH) . 80 A.1.6 Microscopy . 80 A.1.7 Cell segmentation and spot recognition . 81 A.1.8 Data analysis following cell segmentation and spot recognition 83 A.1.9 DNA extraction and quantitative PCR (qPCR) . 84 A.2 Theoretical Methods . 85 A.2.1 Overview of the Governing Differential Equations . 85 A.2.2 Formulation of Regulatory Functions . 87 A.2.3 Infection Simulation Methods . 92 A.2.4 Parameter Fitting . 93 A.2.5 Decision Thresholds . 97 A.2.6 Modeling Bulk Lysogenization . 98 A.2.7 Theoretical Methods Tables . 99 A.3 Supplementary Figures . 106 A.4 Supplementary Tables . 122 B Detailed description of the stochastic model 133 B.1 Conversion of the deterministic model . 133 B.2 Reactions . 134 x B.3 Infection simulation methods . 135 B.4 Decision Thresholds . 137 C Detailed description of the Q module 138 C.1 Formulation of CII inhibition of Q . 138 C.2 Decision Thresholds . 139 Bibliography 140 Illustrations 2.1 The post-infection decision of bacteriophage lambda. Following infection of an E. coli cell, a binary choice is made between lysis, defined by rampant viral replication and cell death, and lysogeny, in which the viral DNA is integrated into the host's genome to become a dormant prophage. The lysogenic state is stably maintained, but a switch back to lysis can be induced by cellular damage. Adapted with permission from Golding, I., 2016. Single-cell studies of phage λ: Hidden treasures under Occam's rug. Annual Review of Virology 3 (1), 453{472, permission conveyed through Copyright Clearance Center, Inc. .8 2.2 Key lambda genes and host factors involved in the lysis/lysogeny decision. Regulatory interactions between the various nodes rely on diverse molecular mechanisms, at the level of phage DNA, mRNA and proteins. 11 2.3 The cascade of transcriptional events during the lambda lysis/lysogeny decision. Regulatory elements and their interactions are depicted on the relevant region of the lambda genome. The expression pattern of early genes is qualitatively similar irrespective of the eventual fate. After a decision has been reached, different sets of genes are expressed to execute the lysis/lysogeny choice. 12 xii 2.4 The dependence of lysogenization on the multiplicity of infection: bulk data. A known number of E. coli bacteria is infected with varying concentrations of lambda phage, and the number of resulting lysogens is measured using selection for an antibiotic marker that was engineered into the viral genome. The experimental trend is reproduced by a simple mathematical model, where infection by a single phage leads to lysis, whereas simultaneous infection by two or more phages results in lysogeny. 15 2.5 The lysis/lysogeny decision at the single-cell level. (A) Images from a live-cell movie following the fate of two E. coli cells, infected by fluorescently-labeled lambda phages. The upper cell, infected by a single phage, proceeds to produce new viral particles and undergo lysis. The lower cell, co-infected by three phages, enters lysogeny, as indicated by a fluorescent reporter for PRE activity, and proceeds to divide normally. (B) The fraction of cells undergoing lysogeny as a function of the multiplicity of infection, as measured from 41,000 infection events. In contrast to the original modeling of the bulk data (Fig. 2.4 above), the single-cell curve rises gradually, suggesting that the MOI dependence is probabilistic rather than deterministic.
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