AUTOMATIC 3D DEFORMED MIDSAGITTAL SURFACE LOCALIZATION BY CONSTRAINED MONTE CARLO OPTIMIZATION

AUTOMATIC 3D DEFORMED MIDSAGITTAL SURFACE LOCALIZATION BY CONSTRAINED MONTE CARLO OPTIMIZATIO

Natural product discovery and engineering at the protein, pathway, and genome scales

For centuries, mankind has turned to natural sources for cures, remedies, and ways to improve its quality of life. It was not until the discovery of the antibiotic penicillin in 1928, however, that natural products – small molecule secondary metabolites produced by a variety of organisms for a variety of purposes – were truly appreciated as the source of these beneficial properties. In the ensuing decades, research in the field of natural products boomed, and a number of discoveries were made that revolutionized global health. In the 1970s and 1980s, however, these discoveries started to become fewer and farther between, prompting pharmaceutical companies to turn away from natural products research in favor of synthetic chemistry approaches to drug discovery. Nevertheless, despite diminishing returns from traditional natural product discovery methods, modern genomics has in fact revealed that vast numbers of natural product gene clusters exist across all domains of life, far in excess of the number of known natural products and far surpassing any previous predictions. Thus, natural product discovery efforts to date have only scratched the surface of Nature’s true capabilities. In this work, we sought to leverage modern techniques for natural product discovery at the protein, pathway, and genome scales to tap into this biosynthetic potential. The past several years have seen the development of many tools for the manipulation of DNA with ease and precision far exceeding the standards set by traditional methods. These methods, combined with the ever-increasing number of putative natural product pathways revealed by genome sequencing efforts, open the door for new platforms of natural product discovery and engineering. At the protein level, we demonstrate the synthesis of novel derivatives of the antimalarial natural product FR-900098 by leveraging the substrate promiscuity of the native biosynthetic machinery. Through structural and biochemical characterization of the N-acetyltransferase FrbF and the corresponding target for FR-900098 inhibition, Dxr from Plasmodium falciparum, we show that the novel FR-900098 derivatives can serve as more potent inhibitors. A platform for their biosynthesis is also established in Escherichia coli. We additionally demonstrate a genome-mining platform for fungal polyketide synthases via the one-step assembly of expression-ready plasmids by homologous recombination in yeast. Through the evaluation of previously uncharacterized endogenous promoters from Saccharomyces cerevisiae, we demonstrate the heterologous production of polyketides from the dimorphic fungus Talaromyces marneffei. Extension of this approach to the pathway level is also demonstrated to assemble both a 6-gene resorcylic acid lactone cluster and a 13-gene phosphonic acid biosynthetic cluster. The latter case, in which all 13-genes from a phosphonic acid non-producing strain were placed under individual strong promoters, enabled the production of novel phosphonic acid compounds when heterologously expressed in Streptomyces lividans. At the genome scale, we developed a versatile system from genome engineering in a broad range of Streptomyces species. As the genus Streptomyces is by far the most important producer of pharmaceutical natural products to date, tools to facilitate their genetic engineering are of significant interest to aid discovery and improve production. Here, we show that the CRISPR/Cas9 system of S. pyogenes can be reconstituted in S. lividans for high-efficiency, multiplex editing of genomic loci. We show that deletions ranging from 20 bp to 31 kb can be successfully introduced in one step, and extend this approach to additional Streptomyces strains

Novel methods for biological network inference: an application to circadian Ca2+ signaling network

Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. For a biological process, two pieces of information are of most interest. For a particular species, the first step is to learn which other species are regulating it. This reveals topology and causality. The second step involves learning the precise mechanisms of how this regulation occurs. This step reveals the dynamics of the system. Applying this process to all species leads to the complete dynamical network. Systems biology is making considerable efforts to learn biological networks at low experimental costs. The main goal of this thesis is to develop advanced methods to build models for biological networks, taking the circadian system of Arabidopsis thaliana as a case study. A variety of network inference approaches have been proposed in the literature to study dynamic biological networks. However, many successful methods either require prior knowledge of the system or focus more on topology. This thesis presents novel methods that identify both network topology and dynamics, and do not depend on prior knowledge. Hence, the proposed methods are applicable to general biological networks. These methods are initially developed for linear systems, and, at the cost of higher computational complexity, can also be applied to nonlinear systems. Overall, we propose four methods with increasing computational complexity: one-to-one, combined group and element sparse Bayesian learning (GESBL), the kernel method and reversible jump Markov chain Monte Carlo method (RJMCMC). All methods are tested with challenging dynamical network simulations (including feedback, random networks, different levels of noise and number of samples), and realistic models of circadian system of Arabidopsis thaliana. These simulations show that, while the one-to-one method scales to the whole genome, the kernel method and RJMCMC method are superior for smaller networks. They are robust to tuning variables and able to provide stable performance. The simulations also imply the advantage of GESBL and RJMCMC over the state-of-the-art method. We envision that the estimated models can benefit a wide range of research. For example, they can locate biological compounds responsible for human disease through mathematical analysis and help predict the effectiveness of new treatments

Natural Product Biosynthesis In Uncultured Bacteria

A single gram of soil can contain thousands of unique bacterial species, only a small fraction of which is regularly cultured in the laboratory.Although the fermentation of cultured microorganisms has provided access to numerous bioactive secondary metabolites, with these same methods it is not possible to characterize the natural products encoded by the uncultured majority. The heterologous expression of biosynthetic gene clusters cloned from DNA extracted directly from environmental samples (eDNA) has begun to provide access to the chemical diversity encoded in the genomes of previously uncultured bacteria. The systematic exploration of natural product biosynthesis in uncultured bacteria, however, still faces several challenges that we sought to experimentally address. First, many natural product gene clusters cannot be detected in functional screens due to cloning and expression limitations of metagenomic library host strains. Second, the lack of robust and scalable gene cluster assembly methods precludes the functional characterization of a large number of natural product biosynthetic gene clusters from cosmid-based eDNA libraries. Third, the large-scale analysis of metagenomic natural product chemical diversity and the phylogenetic context it is found in were previously unaddressed due to the complexities of microbial communities. To address these questions, we have: 1) Demonstrated that sequence-based screens can be used to systematically discover a diverse range of natural product gene clusters by screening two of the largest recombinant eDNA libraries reported to date. This approach circumvents many of the challenges of using functional screens to discover novel biosynthetic gene clusters. (Chapter 2) 2) Shown that transformation associated recombination in S. cerevisiae can be used to functionally reassemble large natural product gene clusters that exceed conventional eDNA cloning limits. This approach overcomes a significant barrier which prevented the functional characterization of many natural product gene clusters from eDNA libraries. (Chapter 3) 3) Developed a high throughput sequencing analysis framework to characterize environmental biosynthetic capacity. These results suggest that the continued construction and screening of soil-based eDNA libraries should provide access to additional novel pools of biosynthetic enzyme diversity. (Chapter 4

Bayesian Inference of Gene Regulatory Networks : From Parameter Estimation to Experimental Design

To learn the structure of gene regulatory networks is an interesting and important topic in systems biology. This structure could be used to specify key regulators and this knowledge may be used to develop new drugs which affect the expression of these regulators. However, the inference of gene regulatory networks, especially from time-series data is a challenging task. This is due to the limited amount of given data which additionally contain a lot of noise. These data cause from the technical point of view for the parameter estimation procedure problems like the non-identifiability and sloppiness of parameters. To address these difficulties, in these thesis new methods for both, the parameter estimation task and the experimental design for gene regulatory networks, are developed for a non-linear ordinary differential equations model, which use a Bayesian procedure and generate samples of the underlying distribution of the parameters. These distributions are of high interest, since they do not provide only one network structure but give all network structures that are consistent with the given data. And all of these structures can then be examined in more detail. The proposed method for Bayesian parameter estimation uses smoothing splines to circumvent the numerical integration of the underlying system of ordinary differential equations, which is usually used for parameter estimation procedures in systems of ordinary differential equations. An iterative Hybrid Monte Carlo and Metropolis-Hastings algorithm is used to sample the model parameters and the smoothing factor. This new method is applied to simulated data, which shows that it is able to reconstruct the topology of the underlying gene regulatory network with high accuracy. The approach was also applied to real experimental data, a synthetic designed 5-gene network (the DREAM 2 Challenge #3 data) and outperforms other methods. For the Bayesian experimental design step, a full Bayesian approach was used which does not use any parametric assumption of the posterior distribution, nor linearizes around a point estimate. To make the full Bayesian approach computationally manageable, maximum entropy sampling is used together with a population-based Markov chain Monte Carlo algorithm. The approach was applied to simulated and real experimental data, the DREAM 2 Challenge #3 data, and outperforms the usage of random experiments and a classical experimental design method

Biophysical, biochemical and structural characterization of Poly(ADP-ribose) Polymerase-1 (PARP-1) and its complexes with DNA-damage models and chromatin substrates, The

2013 Spring.Includes bibliographical references.Eukaryotic DNA is highly dynamic and must be compacted and organized with the help of cellular machines, proteins, into 'heterochromatin' state. At its basic level, chromatin is comprised of spool-like structures of protein complexes termed histones, which bind and organize DNA into larger fibrous structures. Cellular processes like transcription and DNA-damage repair require that chromatin be at least partially stripped of its protein components, which in turn allows for complete accessibility by DNA-repair or transcription machinery. A number of protein factors contribute to chromatin structure regulation. Poly(ADP-ribose) Polymerase-1 (PARP-1) is one of these proteins that exists in all eukaryotic organisms except for yeast. In its inactive form, it compacts chromatin, but performs its chromatin-opening function by covalently modifying itself and other nuclear proteins with long polymers of ADP-ribose in response to DNA damage. Thus, it also serves as a first responder to many types of DNA damage. The highly anionic polymers serve to disrupt protein-DNA interactions and thus allow for the creation of a temporary euchromatin structure. Contained herein are investigations aimed at addressing key questions regarding key differences between the interactions of PARP-1 and chromatin and its DNA-damage substrates. Our experiments show that human PARP-1 interacts with and is enzymatically activated to a similar level by a variety of different DNA substrates. In terms of chromatin, it appears that PARP-1 fails to interact with nucleosomes that do not have linker DNA. PARP-1 most effectively interacts with chromatin by simultaneously binding two DNA strands through contacts made by its two N-terminal Zn-finger domains. Small-Angle X-ray (SAXS) and Neutron Scattering (SANS) and molecular dynamics (MD) experiments were combined with biophysical and biochemical studies to better describe the structural effects of DNA binding on PARP-1. The average solution structure of PARP-1 indicates that the enzyme is a monomeric, non-spherical, elongated molecule with a radius of gyration (Rg) of ~55Å. The DNA-bound form of PARP-1 is also monomeric and binding DNA causes the molecule to become more elongated with an average Rg of ~80Å

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function

2020 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2020 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works