Conic Sampling: An Efficient Method for Solving Linear and Quadratic Programming by Randomly Linking Constraints within the Interior

Linear programming (LP) problems are commonly used in analysis and resource allocation, frequently surfacing as approximations to more difficult problems. Existing approaches to LP have been dominated by a small group of methods, and randomized algorithms have not enjoyed popularity in practice. This paper introduces a novel randomized method of solving LP problems by moving along the facets and within the interior of the polytope along rays randomly sampled from the polyhedral cones defined by the bounding constraints. This conic sampling method is then applied to randomly sampled LPs, and its runtime performance is shown to compare favorably to the simplex and primal affine-scaling algorithms, especially on polytopes with certain characteristics. The conic sampling method is then adapted and applied to solve a certain quadratic program, which compute a projection onto a polytope; the proposed method is shown to outperform the proprietary software Mathematica on large, sparse QP problems constructed from mass spectometry-based proteomics

Evoluce proteomu plastidu euglenidů

Vznik plastidů endosymbiózou a jejich horizontální šíření je široce rozšířený evoluční jev a jedna z významných hnacích sil evoluce eukaryot. Integrace nové organely je doprovázena změnami v její struktuře, genovém obsahu, biogenezi a importu proteinů a propojením jejích metabolických drah s drahami hostitele. Studium těchto procesů v různých skupinách sekundárních řas a srovnávání mezi nimi je důležité pro porozumění obecným principům evoluce plastidů. Krásnoočka (Euglenophyta) získala své plastidy od zelených řas po poměrně dlouhém období heterotrofie. V této práci jsem se podílela na analýze nově vygenerovaných sekvenčních datasetů: transkriptomů Euglena gracilis a Euglena longa a plastidového proteomu E. gracilis determinovaného pomocí hmotnostní spektrometrie, a to s ohledem na potenciální inovace související se získáním a integrací plastidu. Ve výsledných publikacích jsme se zaměřili zvláště na složení a evoluci systému pro targeting a import jaderně kódovaných proteinů do plastidu a zjistili, že plastidy krásnooček obsahují extrémně redukovaný TIC a zcela postrádají TOC komplex. Na základě plastidového proteomu jsme identifikovali několik nových potenciálních translokáz odvozených od proteinů endomembránového systému a popsali některé dříve nepovšimnuté vlastnosti N-terminálních...Endosymbiotic gain and transfer of plastids is a widespread evolutionary phenomenon and a major driving force of eukaryotic evolution. The integration of a new organelle is accompanied by changes in its structure, gene content, molecular mechanisms for biogenesis and transport, and re-wiring of the host and organelle metabolic pathways. To understand the course and underlying mechanisms of plastid evolution, it is important to study these processes in variety of secondary algae and notice their differences and similarities. Euglenophytes gained their plastids from green eukaryotic algae after a long history of heterotrophic lifestyle. In my thesis, I participated in analyses of newly generated sequence datasets: transcriptomes of Euglena gracilis and Euglena longa and mass spectrometry-determined proteome of E. gracilis plastid with especial regard to the potential novelties associated with plastid gain and incorporation. In the resulting publications we particularly focus on plastid protein import machinery and targeting signals and report extremely reduced TIC and completely absent TOC in euglenophyte plastid. Using the proteomic dataset, we predict potential novel plastid protein translocases recruited from ER/Golgi and re-analyze plastid signal domains, characterizing previously overlooked...Katedra parazitologieDepartment of ParasitologyPřírodovědecká fakultaFaculty of Scienc

Exploring the use of a blue pigment-producing NRPS as a tagging method to easily detect engineered NRPs

Nonribosomal peptide synthetases (NRPSs) are modular mega-enzymes found in bacteria and fungi that produce nonribosomal peptides (NRPs) in an assembly line fashion. Each module is in charge of adding a specific amino acid (AA) to the growing peptide chain. Three basic domains constitute one NRPS module: the adenylation (A), peptidyl carrier protein (PCP) and condensation (C) domains. The A domain recognizes and activates the AA. An external enzyme, the PPtase, attaches a phosphopantetheine (PPant) arm to the PCP domain which then picks up the activated AA and delivers it to the C domain. The C domain recognizes the growing peptide chain (donor) as well as the new AA (acceptor) and fuses the two together. A special feature of NRPSs is their ability to recognize and incorporate not only proteinogenic AAs, but also other building blocks like fatty acids (FAs) or non-proteinogenic AAs. All building blocks can be further modified through the action of additional domains: epimerization (E), methylation (M) and oxidation (Ox) domains, among others. In this manner a great variety of different NRPs can be synthesized, many of which are bioactive and exhibit anti-microbial or anti-cancer properties. Thus, it is highly desirable to understand how NRPS domains and modules function and find ways to genetically re-engineer them for custom NRP production. Since the discovery of NRPSs, many efforts have already been made to engineer these enzymes in order to create custom NRPs, but general design rules yet remain elusive. The successful attempts to re-create functional NRPS for the production of novel NRPs include: (i) mutations of the A domain, (ii) subdomain modifications and (iii) rearrangements on the module level. Yet, many engineered NRPSs exhibit only slow reaction rates and low product yields. In some cases, the desired NRP products cannot be detected at all, possibly due to additional control mechanisms that have not been taken into account during the engineering process, such as substrate specificity of C domains. Hence there is still a great need to identify the general rules for successful NRPS engineering in order to exploit the ever-growing molecular toolbox of newly discovered NRPSs for recombinant production of novel bioactive compounds. In this work I present my attempts to develop an approach to easily monitor the outcome of NRPS manipulation using a pigment-producing synthetase as a genetic tag. To this end, I first investigated two homologous synthetases, IndC and BpsA, which produce the blue pigment indigoidine, and mutants thereof and revised the proposed biosynthesis mechanism. I then created a series of fusion constructs between modules coming from different NRPSs and IndC/BpsA to test if indigoidine-tagged peptides could be produced. I identified a promising construct for which point mutations in the upstream module resulted in weaker or null pigment production. However, the expected indigoidine-tagged AA was not detectable, which could be due to the fact that indigoidine production inevitably leads to the separation of the donor AA. These results raised further questions as to whether in a native NRPS, the same modifications lead to congruent effects in neighboring modules. I addressed this question using a fragment of a non-engineered NRPS to monitor the activity of the native and modified versions in an in vitro assay, which I present in the last part of the results. Surprisingly, the effects of the same set of modifications on neighboring modules did not only differ between the engineered NRP-pigment synthetase and the native NRPS, but also between different modules within the native NRPS. These results hint at highly individual behavior of NRPS modules, depending on the context they are in

Computationally Comparing Biological Networks and Reconstructing Their Evolution

Biological networks, such as protein-protein interaction, regulatory, or metabolic networks, provide information about biological function, beyond what can be gleaned from sequence alone. Unfortunately, most computational problems associated with these networks are NP-hard. In this dissertation, we develop algorithms to tackle numerous fundamental problems in the study of biological networks. First, we present a system for classifying the binding affinity of peptides to a diverse array of immunoglobulin antibodies. Computational approaches to this problem are integral to virtual screening and modern drug discovery. Our system is based on an ensemble of support vector machines and exhibits state-of-the-art performance. It placed 1st in the 2010 DREAM5 competition. Second, we investigate the problem of biological network alignment. Aligning the biological networks of different species allows for the discovery of shared structures and conserved pathways. We introduce an original procedure for network alignment based on a novel topological node signature. The pairwise global alignments of biological networks produced by our procedure, when evaluated under multiple metrics, are both more accurate and more robust to noise than those of previous work. Next, we explore the problem of ancestral network reconstruction. Knowing the state of ancestral networks allows us to examine how biological pathways have evolved, and how pathways in extant species have diverged from that of their common ancestor. We describe a novel framework for representing the evolutionary histories of biological networks and present efficient algorithms for reconstructing either a single parsimonious evolutionary history, or an ensemble of near-optimal histories. Under multiple models of network evolution, our approaches are effective at inferring the ancestral network interactions. Additionally, the ensemble approach is robust to noisy input, and can be used to impute missing interactions in experimental data. Finally, we introduce a framework, GrowCode, for learning network growth models. While previous work focuses on developing growth models manually, or on procedures for learning parameters for existing models, GrowCode learns fundamentally new growth models that match target networks in a flexible and user-defined way. We show that models learned by GrowCode produce networks whose target properties match those of real-world networks more closely than existing models

Biomarkers of disease progression and chemotherapeutic resistance in canine osteosarcoma

2011 Fall.Includes bibliographical references.Osteosarcoma is the most common primary bone malignancy in both humans and dogs. Over 10,000 canine patients develop this highly aggressive cancer annually and many succumb to metastatic disease in less than a year. In recent years, canine osteosarcoma has been increasingly recognized as an excellent model for the disease in humans, especially with regard to the molecular biology of the disease. Thus, research targeted at canine osteosarcoma benefits not only dogs but the field of human oncology as well. Research into the genetic and molecular derangements of osteosarcoma in both species has identified a number of oncogenes and tumor suppressor genes that may contribute to tumorigenesis. Additionally, some mediators of invasion and metastasis have been recognized (e.g. Ezrin, matrix metallopeptidases). Despite this, only a limited number of studies have been performed that examine the molecular genetics of osteosarcoma in the context of patient outcome. Thus, with the aim of identifying new target genes and pathways that contribute to disease progression and chemoresistance in osteosarcoma, we first performed transcriptomic and genomic analyses of primary tumors from dogs that had experienced good or poor outcomes following definitive treatment for osteosarcoma. These broad survey experiments yielded a selection of targets for future investigation. To further focus in on the genes that were most deranged from "normal" expression patterns, we compared gene expression patterns from tumors to those of normal bone. This study provided valuable perspective on genes that were identified in the outcome-based experiments, allowing selection of four promising gene targets to pursue. We next set out to validate in vitro models of canine osteosarcoma so that mechanistic studies could be pursued. Assays to test species and short tandem repeat identity were adapted to cell lines in use in our facility and presumed osteosarcoma cell lines were verified to be bone-derived via PCR testing of a bone-specific marker. Additionally, four anti-human antibodies were validated for use in canine samples. Two genes whose expression progressively altered with increased tumor aggressiveness where chosen for further study: insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) and n-Myc downstream regulated gene 2 (NDRG2). IGF2BP1 has been identified as an oncofetal protein and its mRNA was strongly overexpressed in patients with the worst outcome while it was virtually undetectable in normal bone. We identified one possible mechanism for dysregulation of this gene in OSA and we also discovered that knock down of this gene in a canine osteosarcoma cell line inhibited cell invasion. NDRG2 has been dubbed a tumor suppressor in a number of different tumor types yet had not been previously investigated in osteosarcoma. We found NDRG2 mRNA to be underexpressed in all tumors relative to normal bone; patients with poor outcomes had the lowest expression levels. Multiple isoforms of the gene were found to be expressed in canine samples: these were cloned and transfected into a low-NDRG2-expressing cell line. Exogenous expression of NDRG2 in this in vitro system enhanced sensitivity to doxorubicin, one of the drugs most commonly used to treat osteosarcoma. Additionally, three possible mechanisms of dysregulation of this gene were identified. The studies presented herein progress from fact-finding surveys to in-depth functional examination of two genes that likely contribute to osteosarcoma invasion and chemoresistance. Furthermore, additional genes identified in our survey experiments offer promise for future studies into molecular mechanisms of osteosarcoma metastases and chemotherapeutic resistance. Finally, these studies have laid the groundwork for the development of gene-expression-based prognostic screens for dogs with osteosarcoma

Drug Delivery Technology Development in Canada

Canada continues to have a rich history of ground-breaking research in drug delivery within academic institutions, pharmaceutical industry and the biotechnology community

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here