Phase transitions in gene networks evolved under different selection rules

Mathematical models of gene regulation are a powerful tool for understanding the complex features of genetic control. While various modeling efforts have been successful at explaining gene expression dynamics, much less is known about how evolution shapes the structure of these networks. An important feature of gene regulatory networks is their stability in response to environmental perturbations. Regulatory systems are thought to have evolved to exist near the transition between stability and instability, in order to have the required stability to environmental fluctuations while also being able to achieve a wide variety of functions (corresponding to different dynamical patterns). We study a simplified model of gene network evolution in which links are added via different selection rules. These growth models are inspired by recent work on `explosive' percolation which shows that when network links are added through competitive rather than random processes, the connectivity phase transition can be significantly delayed, and when it is reached, it appears to be first order (discontinuous, e.g., going from no failure at all to large expected failure) instead of second order (continuous, e.g., going from no failure at all to very small expected failure). We find that by modifying the traditional framework for networks grown via competitive link addition to capture how gene networks evolve to avoid damage propagation, we also see significant delays in the transition that depend on the selection rules, but the transitions always appear continuous rather than `explosive'.

FUNCTIONAL CHARACTERIZATION OF THE LINK BETWEEN CARBOHYDRATE METABOLISM AND THE PATHOGENESIS OF THE INVASIVE M1T1 GROUP A STREPTOCOCCUS

The Group A Streptococcus (GAS), or Streptococcus pyogenes, is a strict human pathogen that colonizes a variety of sites within the host. Infections can vary from minor and easily treatable, to life-threatening, invasive forms of disease. In order to adapt to niches, GAS utilizes environmental cues, such as carbohydrates, to coordinate the expression of virulence factors. Research efforts to date have focused on identifying how either components of the phosphoenolpyruvate-phosphotransferase system (PTS) or global transcriptional networks affect the regulation of virulence factors, but not the synergistic relationship between the two. The present study investigates the role of a putative PTS-fructose operon encoded by fruRBA and its role in virulence in the M1T1 strain 5448. Growth in fructose resulted in induction of fruRBA. RT-PCR showed that fruRBA formed an operon, which was repressed by FruR in the absence of fructose. Growth and carbon utilization profiles revealed that although the entire fruRBA operon was required for growth in fructose, FruA was the main fructose transporter. The ability of both ΔfruR and ΔfruB mutants to survive in whole human blood or neutrophils was impaired. However, the phenotypes were not reproduced in murine whole blood or in a mouse intraperitoneal infection, indicating a human-specific mechanism. While it is known that the PTS can affect activity of the Mga virulence regulator, further characterization of the mechanism by which sugars and its protein domains affect activity have not been studied. Transcriptional studies revealed that the core Mga regulon is activated more in a glucose-rich than a glucose-poor environment. This activation correlates with the differential phosphorylation of Mga at its PTS regulatory domains (PRDs). Using a 5448 mga mutant, transcriptome studies in THY or C media established that the Mga regulon reflects the media used. Interestingly, Mga regulates phage-encoded DNases in a low glucose environment. We also show that Mga activity is dependent on C-terminal amino acid interactions that aid in the formation of homodimers. Overall, the studies presented sought to define how external environmental cues, specifically carbohydrates, control complex regulatory networks used by GAS, contribute to pathogenesis, and aid in adaptation to various nutrient conditions encountered.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.