http://hdl.handle.net/1721.1/54827 Wed, 22 May 2013 11:27:21 GMT 2013-05-22T11:27:21Z http://hdl.handle.net/1721.1/77783 Integrative approaches for systematic reconstruction of regulatory circuits in mammals Santos Botelho Oliveira Leite, Ana Paula The reconstruction of regulatory networks is one of the most challenging tasks in systems biology. Although some models for inferring regulatory networks can make useful predictions about the wiring and mechanisms of molecular interactions, these approaches are still limited and there is a strong need to develop increasingly universal and accurate approaches for network reconstruction. This problem is particularly challenging in mammals, due to the higher complexity of mammalian regulatory networks and limitations in experimental manipulation. In this thesis, I present three systematic approachs to reconstruct, analyse and refine models of gene regulation. In Chapter 1, I devise a method for deriving an observational model from temporal genomic profiles. I use it to choose targets for perturbation experiments in order to determine a network controlling the responses of mouse primary dendritic cells to stimulation with pathogen components. In Chapter 2, I introduce the algorithm Exigo, for identifying essential interactions in regulatory networks reconstructed from experimental data where regulators have been silenced, using a network reduction strategy. Exigo outperforms previous approaches on simulated data, uncovers the core network structure when applied to real networks derived from perturbation studies in mammals, and improves the performance of network inference methods. Lastly, I introduce in Chapter 3 an approach to learn a module network from multiple highthroughput assays. Analysis of a diffuse large B-cell lymphoma dataset identifies candidate regulator genes, microRNAs and copy number aberrations with biological, and possibly therapeutic, importance. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 141-149). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/77783 2012-01-01T00:00:00Z http://hdl.handle.net/1721.1/77782 c-Myc regulates transcriptional pause release and is a global amplifier of transcription Lin, Charles Yang Elevated expression of the c-Myc transcription factor occurs frequently in human cancers and is associated with tumor aggression and poor clinical outcome. However, the predominant mechanism by which c-Myc regulates global transcription in both healthy and tumor cells is poorly understood. In this thesis, I present evidence that c-Myc is a global regulator of RNA Polymerase II (RNA Pol II) transcriptional pause release. Transcriptional pausing occurs when additional regulatory steps are required to promote elongation of genes after transcription has initiated. Chapter 2 identifies transcriptional pausing as a general feature of transcription by RNA Pol II in mammalian cells. c-Myc is identified as having a major role in promoting release from pause at its target genes. Chapter 3 finds in tumor cells with elevated c-Myc, the transcription factor binds to promoters and enhancers of most actively transcribed genes. The predominant effect of c-Myc binding is to produce higher levels of transcription by promoting RNA Pol II transcriptional pause release. Thus, c-Myc accumulates in the promoter regions of active genes across the cancer cell genome and causes transcriptional amplification, producing increased levels of transcripts within the cells gene expression program. These results imply that transcriptional amplification can reduce rate-limiting constraints for tumor cell growth and proliferation. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 203-226). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/77782 2012-01-01T00:00:00Z http://hdl.handle.net/1721.1/77778 Mechanistic analysis of polymer-attached inhibitors of influenza virus and their effect on minimizing drug resistance Lee, Chia Min (Jaimie Chia Min) With the emergence of the 2009 A(H1N1) pandemic influenza virus and the rapid spread of drug resistance in recent years, the need to develop new anti-influenza drugs that can reduce the emergence of new resistant viruses is both urgent and important. This thesis explores the use of polymer-attached inhibitors as a new approach in the development of anti-influenza drugs, with particular focus on polymer-attached zanamivir (ZA). We have previously shown that covalently conjugating multiple copies of ZA via a flexible linker to poly-L-glutamine greatly enhances antiviral potency. In the first study, we have elucidated the mechanism of this phenomenon. Like ZA itself, the polymer-attached inhibitor binds specifically to viral neuraminidase and inhibits both its enzymatic activity and the release of newly synthesized virions from infected cells. In contrast to monomeric ZA, however, the polymer-attached drug also synergistically inhibits virus-endosome fusion, thus contributing to the dramatically increased antiviral potency. Next, we went on to investigate polymer-attached ZA's effect on the emergence of drug resistance. We found that viruses adapted rapidly to growing in high concentrations of monomeric ZA, whereas viral growth remained inhibited by low concentrations of polymer-attached ZA even after 23 passages in cell culture. Sequencing analysis established the emergence of an amino acid substitution known to confer ZA resistance (E119G in neuraminidase) after 8 passages of monomeric ZA selection. In contrast, virus grown in polymer-attached ZA remained free of substitutions in E119, and other known resistance-associated residues. We instead found novel substitutions in hemagglutinin (R220G, D241G) and neuraminidase (G111D), which emerged during passages 14-17. Importantly, although the drug-selected variants were resistant to monomeric ZA, the viruses remained susceptible to low pM concentrations of polymer-attached ZA itself. Taken together, these data demonstrate that attaching the drug to a polymeric chain (i) confers a new mechanism of antiviral action; (ii) significantly delays the emergence of drug resistance; and (iii) enhances potency against the selected ZA-resistant variants. The studies presented in this thesis provide further impetus for the use of polymer-attached inhibitors as influenza therapy, and as tools for better understanding of influenza biology. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/77778 2012-01-01T00:00:00Z http://hdl.handle.net/1721.1/77640 Simultaneous computational discovery of DNA regulatory motifs and transcription factor binding constraints at high spatial resolution Guo, Yuchun I present three novel computational methods to address the challenge of identifying protein-DNA interactions at high spatial resolution from noisy ChIP-Seq data. I first present the genome positioning system (GPS) algorithm which predicts protein-DNA interaction events from ChIP-Seq data using a single-base resolution generative probabilistic model. Using synthetic and actual ChIP-Seq data, I show that GPS improves the effective spatial resolution and accuracy in resolving proximal binding events when comparing with existing methods. Second, I present the k-mer set motif (KSM) representation and the k-mer motif alignment and clustering (KMAC) method which discovers DNA-binding motifs from ChIP-Seq derived sequences. I demonstrate that the KSM model is more predictive than the widely used position weight matrix model, and that KMAC outperforms other existing motif discovery programs in recovering known motifs from a large collection of human ChIP-Seq experiments. Finally, I present an integrative method, genome wide event finding and motif discovery (GEM), which models ChIP data with explanatory motifs and binding events at high spatial resolution. The GEM model links binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. I show that GEM further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of proximal binding events. GEM enables a systematic analysis of in vivo transcription factor binding to discover hundreds of spatial binding constraints between factors in human and mouse cells, including known factor pairs and novel pairs such as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4a/FOXA1. I also discovered a complex spatial binding relationship involved 6 key regulatory factors in mouse embryonic stem (ES) cell that is likely to be functional in ES cell gene regulation. Such computational discoveries propose testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control. Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (p. 126-135). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/77640 2012-01-01T00:00:00Z