Metabolic pathway analysis via integer linear programming

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The understanding of cellular metabolism has been an intriguing challenge in classical cellular biology for decades. Essentially, cellular metabolism can be viewed as a complex system of enzyme-catalysed biochemical reactions that produces the energy and material necessary for the maintenance of life. In modern biochemistry, it is well-known that these reactions group into metabolic pathways so as to accomplish a particular function in the cell. The identification of these metabolic pathways is a key step to fully understanding the metabolic capabilities of a given organism. Typically, metabolic pathways have been elucidated via experimentation on different organisms. However, experimental findings are generally limited and fail to provide a complete description of all pathways. For this reason it is important to have mathematical models that allow us to identify and analyze metabolic pathways in a computational fashion. This is precisely the main theme of this thesis. We firstly describe, review and discuss existent mathematical/computational approaches to metabolic pathways, namely stoichiometric and path finding approaches. Then, we present our initial mathematical model named the Beasley-Planes (BP) model, which significantly improves on previous stoichiometric approaches. We also illustrate a successful application of the BP model to optimally disrupt metabolic pathways. The main drawback of the BP model is that it needs as input extra pathway knowledge. This is especially inappropriate if we wish to detect unknown metabolic pathways. As opposed to the BP model and stoichoimetric approaches, this issue is not found in path finding approaches. For this reason a novel path finding approach is built and examined in detail. This analysis serves us as inspiration to build the Improved Beasley-Planes (IBP) model. The IBP model incorporates elements of both stoichometric and path finding approaches. Though somewhat less accurate than the BP model, the IBP model solves the issue of extra pathway knowledge. Our research clearly demonstrates that there is a significant chance of developing a mathematical optimisation model that underlies many/all metabolic pathways.Funding was obtained from the University of Navarra

Metabolic pathway analysis via integer linear programming

The understanding of cellular metabolism has been an intriguing challenge in classical cellular biology for decades. Essentially, cellular metabolism can be viewed as a complex system of enzyme-catalysed biochemical reactions that produces the energy and material necessary for the maintenance of life. In modern biochemistry, it is well-known that these reactions group into metabolic pathways so as to accomplish a particular function in the cell. The identification of these metabolic pathways is a key step to fully understanding the metabolic capabilities of a given organism. Typically, metabolic pathways have been elucidated via experimentation on different organisms. However, experimental findings are generally limited and fail to provide a complete description of all pathways. For this reason it is important to have mathematical models that allow us to identify and analyze metabolic pathways in a computational fashion. This is precisely the main theme of this thesis. We firstly describe, review and discuss existent mathematical/computational approaches to metabolic pathways, namely stoichiometric and path finding approaches. Then, we present our initial mathematical model named the Beasley-Planes (BP) model, which significantly improves on previous stoichiometric approaches. We also illustrate a successful application of the BP model to optimally disrupt metabolic pathways. The main drawback of the BP model is that it needs as input extra pathway knowledge. This is especially inappropriate if we wish to detect unknown metabolic pathways. As opposed to the BP model and stoichoimetric approaches, this issue is not found in path finding approaches. For this reason a novel path finding approach is built and examined in detail. This analysis serves us as inspiration to build the Improved Beasley-Planes (IBP) model. The IBP model incorporates elements of both stoichometric and path finding approaches. Though somewhat less accurate than the BP model, the IBP model solves the issue of extra pathway knowledge. Our research clearly demonstrates that there is a significant chance of developing a mathematical optimisation model that underlies many/all metabolic pathways.EThOS - Electronic Theses Online ServiceUniversity of NavarraGBUnited Kingdo

Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks

On the athermal character of structural phase transitions

The significance of thermal fluctuations on nucleation in structural first-order phase transitions has been examined. The prototype case of martensitic transitions has been experimentally investigated by means of acoustic emission techniques. We propose a model based on the mean first-passage time to account for the experimental observations. Our study provides a unified framework to establish the conditions for isothermal and athermal transitions to be observed.Comment: 5 pages, 4 figures, accepted in Phys. Rev. Let

La importancia de la sesión grupal en la clase inversa: Trabajos colaborativos en una asignatura de Matemáticas de Grado durante el curso 2016-2017

En este trabajo se describen algunos de los trabajos realizados durante el curso 2016-2017 por los estudiantes del Grado en Ingeniería de Sistemas de Telecomunicación, Sonido e Imagen de la Universitat Politècnica de València, en las prácticas de una asignatura anual del primer año, Matemáticas 2. La utilización de la Clase Inversa en dichas prácticas ha permitido dedicar las horas presenciales a la realización de trabajos colaborativos durante la sesión presencial o grupal. Se ha utilizado la plataforma PoliformaT a través de la cual profesores y estudiantes pueden compartir información sobre sus asignaturas así como utilizar herramientas para su gestión como son tareas, exámenes o la herramienta Lessons que permite la creación de contenidos e itinerarios formativos, facilitándonos la implementación de la clase inversa en esta asignatura. Durante las prácticas nuestros estudiantes han utilizado el programa Matlab consiguiendo entre todos varios productos finales, como una colección de gráficas de curvas en coordenadas paramétricas y polares, una colección de gráficas de superficies y una representación tridimensional de la Sierra de Bernia. Durante el curso se han realizado varios posters de estos resultados conjuntos que han sido expuestos en nuestro aulario, despertando el interés de profesores y estudiantes de otras asignaturas

Computing the shortest elementary flux modes in genome-scale metabolic networks

This article is available open access through the publisher’s website through the link below. Copyright @ The Author 2009.Motivation: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. Results: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online (http://bioinformatics.oxfordjournals.org/cgi/content/full/btp564/DC1).Fundação Calouste Gulbenkian, Fundação para a Ciência e a Tecnologia (FCT) and Siemens SA Portugal

In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer

Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on diferent leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identifcation in cance

Cancertool: A visualization and representation interface to exploit cancer datasets

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.We are grateful to Iñaki Lazaro for the design of the tumor type logos, Evarist Planet and Antoni Berenguer for insightful discussions, and the Carracedo lab for valuable input. V. Torrano is funded by Fundación Vasca de Innovación e Investigación Sanitarias, BIOEF (BIO15/CA/052), the AECC J.P. Bizkaia and the Basque Department of Health (2016111109). The work of A. Carracedo is supported by the Basque Department of Industry, Tourism and Trade (Etortek) and the Department of Education (IKERTALDE IT1106-16, also participated by A. Gomez-Muñoz), the BBVA Foundation, the MINECO [SAF2016-79381-R (FEDER/EU)]; Severo Ochoa Excellence Accreditation SEV-2016-0644; Excellence Networks (SAF2016-81975-REDT), European Training Networks Project (H2020-MSCA-ITN-308 2016 721532), the AECC IDEAS16 (IDEAS175CARR), and the European Research Council (Starting Grant 336343, PoC 754627). CIBERONC was cofunded with FEDER funds. The work of A. Aransay is supported by the Basque Department of Industry, Tourism and Trade (Etortek and Elkartek Programs), the Innovation Technology Department of Bizkaia County, CIBERehd Network, and Spanish MINECO the Severo Ochoa Excellence Accreditation (SEV-2016-0644). I. Apaolaza is funded by a Basque Government predoctoral grant (PRE_2017_2_0028). X.R. Bustelo is supported by grants from the Castilla-León Government (BIO/SA01/15, CSI049U16), Spanish Ministry of Economy and Competitiveness (MINECO; SAF2015-64556-R), Worldwide Cancer Research (14-1248), Ramón Areces Foundation, and the Spanish Society against Cancer (GC16173472GARC). Funding from MINECO to X.R. Bustelo is partially contributed by the European Regional Development Fund. The work of F.J. Planes is supported by the MINECO (BIO2016-77998-R) and ELKARTEK Programme of the Basque Government (KK-2016/00026)