Regulatory activity revealed by dynamic correlations in gene expression noise

Gene regulatory interactions are context dependent, active in some cellular states but not in others. Stochastic fluctuations, or 'noise', in gene expression propagate through active, but not inactive, regulatory links^(1,2). Thus, correlations in gene expression noise could provide a noninvasive means to probe the activity states of regulatory links. However, global, 'extrinsic', noise sources generate correlations even without direct regulatory links. Here we show that single-cell time-lapse microscopy, by revealing time lags due to regulation, can discriminate between active regulatory connections and extrinsic noise. We demonstrate this principle mathematically, using stochastic modeling, and experimentally, using simple synthetic gene circuits. We then use this approach to analyze dynamic noise correlations in the galactose metabolism genes of Escherichia coli. We find that the CRP-GalS-GalE feed-forward loop is inactive in standard conditions but can become active in a GalR mutant. These results show how noise can help analyze the context dependence of regulatory interactions in endogenous gene circuits

A Performance evaluation of several ATM switching architectures

The goal of this thesis is to evaluate the performance of three Asynchronous Transfer Mode switching architectures. After examining many different ATM switching architectures in literature, the three architectures chosen for study were the Knockout switch, the Sunshine switch, and the Helical switch. A discrete-time, event driven system simulator, named ProModel, was used to model the switching behavior of these architectures. Each switching architecture was modeled and studied under at least two design configurations. The performance of the three architectures was then investigated under three different traffic types representative of traffic found in B-ISDN: random, constant bit rate, and bursty. Several key performance parameters were measured and compared between the architectures. This thesis also explored the implementation complexities and fault tolerance of the three selected architectures

The Architecture And Dynamics Of Gene Regulatory Networks Directing Cell-Fate Choice During Murine Hematopoiesis

Mammals produce hundreds of billions of new blood cells every day througha process known as hematopoiesis. Hematopoiesis starts with stem cells that develop into all the different types of cells found in blood by changing their genome-wide gene expression. The remodeling of genome-wide gene expression can be primarily attributed to a special class of proteins called transcription factors (TFs) that can activate or repress other genes, including genes encoding TFs. TFs and their targets therefore form recurrent networks called gene regulatory networks (GRNs). GRNs are crucial during physiological developmental processes, such as hematopoiesis, while abnormalities in the regulatory interactions of GRNs can be detrimental to the organisms. To this day we do not know all the key compo-nents that comprise hematopoietic GRNs or the complete set of their regulatory interactions. Inference of GRNs directly from genetic experiments is low throughput and labor intensive, while computational inference of comprehensive GRNs is challenging due to high processing times. This dissertation focuses on deriving the architecture and the dynamics of hematopoietic GRNs from genome-wide gene expression data obtained from high-resolution time-series experiments. The dissertation also aims to address the technical challenge of speeding up the process of GRN inference. Here GRNs are inferred and modeled using gene circuits, a data-driven method based on Ordinary Differential Equations (ODEs). In gene circuits, the rate of change of a gene product depends on regulatory influences from other genes encoded as a set of parameters that are inferred from time-series data. A twelve-gene GRN comprising genes encoding key TFs and cytokine receptors involved in erythrocyte-neutrophil differentiation was inferred from a high-resolution time-series dataset of the in vitro differentiation of a multipotential cell line. The inferred GRN architecture agreed with prior empirical evidence and pre- dicted novel regulatory interactions. The inferred GRN model was also able to predict the outcome of perturbation experiments, suggesting an accurate inference of GRN architecture. The dynamics of the inferred GRN suggested an alternative explanation to the currently accepted sequence of regulatory events during neutrophil differentiation. The analysis of the model implied that two TFs, C/EBPα and Gfi1, initiate cell-fate choice in the neutrophil lineage, while PU.1, believed to be a master regulator of all white-blood cells, is activated only later. This inference was confirmed in a single-cell RNA-Seq dataset from mouse bone marrow, in which PU.1 upregulation was preceded by C/EBPα and Gfi1 upregulation. This dissertation also presents an analysis of a high-temporal resolution genome-wide gene expression dataset of in vitro macrophage-neutrophil differentiation. Analysis of these data reveal that genome-wide gene expression during differentiation is highly dynamic and complex. A large-scale transition is observed around 8h and shown to be related to wide-spread physiological remodeling of the cells. The genes associated by myeloid differentiation mainly change during the first 4 hours, implying that the cell-fate decision takes place in the first four hours of differentiation. The dissertation also presents a new classification-based model-training technique that addresses the challenge of the high computational cost of inferring GRNs. This method, called Fast Inference of Gene Regulation (FIGR), is demonstrated to be two orders magnitude faster than global non-linear optimization techniques and its computational complexity scales much better with GRN size. This work has demonstrated the feasibility of simulating relatively large realistic GRNs using a dynamical and mechanistically accurate model coupled to high-resolution time series data and that such models can yield novel biological insight. Taken together with the macrophage-neutrophil dataset and the computationally efficient GRN inference methodology, this work should open up new avenues for modeling more comprehensive GRNs in hematopoiesis and the broader field of developmental biology

FUNCTIONAL STUDIES OF microRNAs IN DEVELOPMENT AND CANCER

MicroRNAs (miRNAs) COMPRISE a large family of small (~23 nucleotide in length), endogenous RNAs that regulate gene expression at the posttranscriptional level. Functional studies have indicated that miRNAs participate in the regulation of nearly all cellular processes investigated so far, including differentiation, apoptosis, and proliferation. Further, the deregulation of miRNA expression greatly contributes to human diseases, and is associated with many human pathologies, such as cancer. The studies in this thesis have focused on miRNA expression and regulation in various forms of malignancies. Specifically, we wanted to provide mechanistic insights into the role of miRNAs in tumorigenesis. In parallel, we hoped to discover new therapeutic targets that could be exploited clinically to treat childhood and adult cancer. In the work presented, we describe the functional consequences of miRNA perturbations in three distinct neoplasias: (1) chronic lymphocytic leukemia (CLL), the second most common type of blood cancer in adults; (2) neuroblastoma (NB), an embryonal malignancy of the sympathetic nervous system that is derived from primordial neural crest cells and occurs almost exclusively in infants and young children; and, (3) basal cell carcinoma (BCC), a basal cell-derived malignancy of the epidermis, which ranks as the most commonly diagnosed human cancer among fair-skinned individuals. Our CLL studies revealed that the DLEU2 transcript functions as a regulatory host gene for the miRNAs miR-15a and miR-16-1. These miRNAs were shown to target the G1 cyclins D1 and E1 for translational repression, resulting in a prominent cell cycle arrest. Further, ectopic expression of DLEU2 inhibited the colony-forming capacity of tumor cell lines, suggesting a tumor-suppressive function for miR-15a and miR-16-1. We also demonstrate that DLEU2 is transcriptionally regulated by the oncoprotein c-MYC, providing a novel mechanism by which MYC can regulate the G1 cyclins in a posttranscriptional manner. Functional loss of DLEU2 may thus constitute an important step in CLL tumorigenesis and various c-MYC-dependent cancers. In our analysis of MYCN-amplified neuroblastoma (NB), we investigated the molecular consequences and functional outcome of abnormal miRNA regulation and discovered that miR-17~92 cluster-derived miRNAs potentiate the tumorigenic behavior of this childhood cancer. Importantly, we could show that miR-18a and miR-19a target and repress the expression of estrogen receptor-α (ESR1), a ligand-inducible transcription factor implicated in neuronal differentiation. We propose that ESR1 represents a previously undescribed MYCN target in NB and demonstrate a unique oncogenic circuitry in which the repression of ESR1 through MYCN-regulated miRNAs may play a fundamental role in NB tumorigenesis. Finally, based on our genome-wide miRNA expression analysis of a non-melanoma skin cancer, we found that the skin-specific miRNA, miR-203, is preferentially lost in BCC. Functional analyses demonstrated that the inappropriate activation of the Hedgehog and MAPK pathways in BCCs may contribute to cancer progression via severely reduced expression of miR-203, which dramatically facilitates the misexpression of genes involved in the regulation of cell proliferation and cell cycle, including c-JUN and c-MYC. In this respect, miR-203 constitutes a gatekeeper miRNA controlling keratinocyte proliferation. The molecular reconstitution of miR-203 could therefore serve as a novel therapeutic strategy in the treatment of BCC tumors

Biological Networks

Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems