SBML) Level 2: Structures and Facilities for Model Definitions
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Kinetic Modeling Using Biopax Ontology
Kinetic Modeling using BioPAX ontology Oliver Ruebenacker, Ion. I. Moraru, James C. Schaff, and Michael L. Blinov Center for Cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, 06030 {oruebenacker,moraru,schaff,blinov}@exchange.uchc.edu Abstract – e.g. Cytoscape (http://cytoscape.org, [5]), cPath database http://cbio.mskcc.org/software/cpath, [6]), Thousands of biochemical interactions are PathCase (http://nashua.case.edu/PathwaysWeb), available for download from curated databases such VisANT (http://visant.bu.edu, [7]). However, the as Reactome, Pathway Interaction Database and other current standard for kinetic modeling is Systems sources in the Biological Pathways Exchange Biology Markup Language, SBML ([8], (BioPAX) format. However, the BioPAX ontology http://sbml.org). Both BioPAX and SBML are used to does not encode the necessary information for kinetic encode key information about the participants in modeling and simulation. The current standard for biochemical pathways, their modifications, locations kinetic modeling is the System Biology Markup and interactions, but only SBML can be used directly Language (SBML), but only a small number of models for kinetic modeling, because elements are included in are available in SBML format in public repositories. SBML specifically for the context of a quantitative Additionally, reusing and merging SBML models theory. In contrast, concepts in BioPAX are more presents a significant challenge, because often each abstract. SBML-encoded models typically contain all element has a value only in the context of the given data necessary for simulations, such as molecular model, and information encoding biological meaning species and their concentrations, reactions among is absent. We describe a software system that enables these species, and kinetic laws for these reactions. -
So Many Date Formats: Which Should You Use? Kamlesh Patel, Jigar Patel, Dilip Patel, Vaishali Patel Rang Technologies Inc, Piscataway, NJ
Paper – 2463-2017 So Many Date Formats: Which Should You Use? Kamlesh Patel, Jigar Patel, Dilip Patel, Vaishali Patel Rang Technologies Inc, Piscataway, NJ ABSTRACT Nearly all data is associated with some Date information. In many industries, a SAS® programmer comes across various Date formats in data that can be of numeric or character type. An industry like pharmaceuticals requires that dates be shown in ISO 8601 formats due to industry standards. Many SAS programmers commonly use a limited number of Date formats (like YYMMDD10. or DATE9.) with workarounds using scans or substring SAS functions to process date-related data. Another challenge for a programmer can be the source data, which can include either Date-Time or only Date information. How should a programmer convert these dates to the target format? There are many existing Date formats (like E8601 series, B8601 series, and so on) and informants available to convert the source Date to the required Date efficiently. In addition, there are some useful functions that we can explore for deriving timing-related variables. For these methods to be effective, one can use simple tricks to remember those formats. This poster is targeted to those who have a basic understanding of SAS dates. KEYWORDS SAS, date, time, Date-Time, format, informat, ISO 8601, tips, Basic, Extended, Notation APPLICATIONS: SAS v9.2 INTRODUCTION Date and time concept in SAS is very interesting and can be handled very efficiently if SAS programmers know beyond basics of different SAS Date formats and informats along with many different Date functions. There are various formats available to use as per need in SAS. -
ASN.1. Communication Between Heterogeneous Systems – Errata
ASN.1. Communication between heterogeneous systems – Errata These errata concern the June 5, 2000 electronic version of the book "ASN.1. Communication between heterogeneous systems", by Olivier Dubuisson (© 2000). (freely available at http://www.oss.com/asn1/resources/books‐whitepapers‐pubs/asn1‐ books.html#dubuisson.) N.B.: The first page number refers to the PDF electronic copy and is the number printed on each page, not the number at the bottom of the Acrobat Reader window. The page number in parentheses refers to the paper copy published by Morgan Kaufmann. Page 9 (page 8 for the paper copy): ‐ last line, replace "(the number 0 low‐weighted byte" with "(the number 0 low‐weighted bit". Page 19 (page 19 for the paper copy): ‐ on the paper copy, second bullet, replace "the Physical Layer marks up..." with "the Data Link Layer marks up...". ‐ on the electronic copy, second bullet, replace "it is down to the Physical Layer to mark up..." with "it is down to the Data Link Layer to mark up...". Page 25 (page 25 for the paper copy): second paragraph, replace "that all the data exchange" with "that all the data exchanged". Page 32 (page 32 for the paper copy): last paragraph, replace "The order number has at least 12 digits" with "The order number has always 12 digits". Page 34 (page 34 for the paper copy): first paragraph, replace "it will be computed again since..." with "it will be computed again because...". Page 37 for the electronic copy only: in the definition of Quantity, replace "unites" with "units". Page 104 (page 104 for the paper copy): ‐ in rule <34>, delete ", digits"; ‐ at the end of section 8.3.2, add the following paragraph: Symbols such as "{", "[", "[[", etc, are also lexical tokens of the ASN.1 notation. -
Understanding JSON Schema Release 2020-12
Understanding JSON Schema Release 2020-12 Michael Droettboom, et al Space Telescope Science Institute Sep 14, 2021 Contents 1 Conventions used in this book3 1.1 Language-specific notes.........................................3 1.2 Draft-specific notes............................................4 1.3 Examples.................................................4 2 What is a schema? 7 3 The basics 11 3.1 Hello, World!............................................... 11 3.2 The type keyword............................................ 12 3.3 Declaring a JSON Schema........................................ 13 3.4 Declaring a unique identifier....................................... 13 4 JSON Schema Reference 15 4.1 Type-specific keywords......................................... 15 4.2 string................................................... 17 4.2.1 Length.............................................. 19 4.2.2 Regular Expressions...................................... 19 4.2.3 Format.............................................. 20 4.3 Regular Expressions........................................... 22 4.3.1 Example............................................. 23 4.4 Numeric types.............................................. 23 4.4.1 integer.............................................. 24 4.4.2 number............................................. 25 4.4.3 Multiples............................................ 26 4.4.4 Range.............................................. 26 4.5 object................................................... 29 4.5.1 Properties........................................... -
Array Databases: Concepts, Standards, Implementations
Baumann et al. J Big Data (2021) 8:28 https://doi.org/10.1186/s40537-020-00399-2 SURVEY PAPER Open Access Array databases: concepts, standards, implementations Peter Baumann , Dimitar Misev, Vlad Merticariu and Bang Pham Huu* *Correspondence: b. Abstract phamhuu@jacobs-university. Multi-dimensional arrays (also known as raster data or gridded data) play a key role in de Large-Scale Scientifc many, if not all science and engineering domains where they typically represent spatio- Information Systems temporal sensor, image, simulation output, or statistics “datacubes”. As classic database Research Group, Jacobs technology does not support arrays adequately, such data today are maintained University, Bremen, Germany mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for fexible ad-hoc analytics on large n-D arrays, similar to what SQL ofers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison diferentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the feld; for example, subsetting tests were designed in a way that systems cannot be tuned to specifcally these queries. -
Academic Year 2007 Computing Project Group Report SBML-ABC, A
Academic year 2007 Computing project Group report SBML-ABC, a package for data simulation, parameter inference and model selection Imperial College- Division of Molecular Bioscience MSc. Bioinformatics Nathan Harmston, David Knowles, Sarah Langley, Hang Phan Supervisor: Professor Michael Stumpf June 13, 2008 Contents 1 Introduction 1 1.1 Background ...................................... 1 2 Features and dependencies 2 2.1 Project outline ..................................... 2 2.2 Key features ...................................... 2 2.2.1 SBML ..................................... 2 2.2.2 Stochastic simulation ............................. 4 2.2.3 Deterministic simulation ........................... 4 2.2.4 ABC inference ................................ 5 2.3 Interfaces ....................................... 5 2.3.1 CAPI ..................................... 5 2.3.2 Command line interface ........................... 5 2.3.3 Python .................................... 5 2.3.4 R ....................................... 6 2.4 Dependencies ..................................... 6 3 Methods 8 3.1 SBML Adaptor .................................... 8 3.2 Stochastic simulation ................................. 8 3.2.1 Random number generator .......................... 9 3.2.2 Multicompartmental Gillespie algorithm ................... 9 3.2.3 Tau leaping .................................. 10 3.2.4 Chemical Langevin Equation ......................... 11 3.3 Deterministic algorithms ............................... 11 3.3.1 ODE solvers ................................ -
TUTORIAL.Pdf
Systems Biology Toolbox for MATLAB A computational platform for research in Systems Biology Tutorial www.sbtoolbox.org Henning Schmidt, [email protected] Vision ° The Systems Biology Toolbox for MATLAB offers systems biologists an open and user extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological systems. Henning Schmidt, [email protected] www.sbtoolbox.org Tutorial Outline ° General introduction to the toolbox ° Using the toolbox documentation ° Building models and simulation ° Import/Export of models ° Using the toolbox functions - examples ° Mass conservation and simple model reduction ° Steady-state analysis and stability ° Bifurcation analysis ° Parameter sensitivity analysis (metabolic control analysis) ° In silico experiments and the representation of measurement data ° Parameter estimation ° Localization of mechanisms leading to oscillations and bistability ° Network identification ° Writing your own functions for the toolbox ° Modifying existing MATLAB models for use with the toolbox Henning Schmidt, [email protected] www.sbtoolbox.org ° General introduction to the toolbox Henning Schmidt, [email protected] www.sbtoolbox.org Model Development Cycle Graphical Modelling (CellDesigner, PathwayLab, etc.) Model export to graphical Model import modelling tool to SB Toolbox Modelling, Simulation, Analysis, Identification, etc. (SB Toolbox) M e M a o s d u e re l m m e o n Henning Schmidt, [email protected] -
An SBML Overview
An SBML Overview Michael Hucka, Ph.D. Control and Dynamical Systems Division of Engineering and Applied Science California Institute of Technology Pasadena, CA, USA 1 So much is known, and yet, not nearly enough... 2 Must weave solutions using different methods & tools 3 Common side-effect: compatibility problems 4 Models represent knowledge to be exchanged 5 SBML 6 SBML = Systems Biology Markup Language Format for representing quantitative models • Defines object model + rules for its use - Serialized to XML Neutral with respect to modeling framework • ODE vs. stochastic vs. ... A lingua franca for software • Not procedural 7 Some basics of SBML model encoding ๏ Well-stirred compartments c n 8 Some basics of SBML model encoding c protein A protein B n gene mRNAn mRNAc 9 Some basics of SBML model encoding ๏ Reactions can involve any species anywhere c protein A protein B n gene mRNAn mRNAc 10 Some basics of SBML model encoding ๏ Reactions can cross compartment boundaries c protein A protein B n gene mRNAn mRNAc 11 Some basics of SBML model encoding ๏ Reaction/process rates can be (almost) arbitrary formulas c protein A f1(x) protein B n f5(x) f2(x) gene f4(x) mRNAn f3(x) mRNAc 12 Some basics of SBML model encoding ๏ “Rules”: equations expressing relationships in addition to reaction sys. g1(x) c g (x) 2 protein A f1(x) protein B . n f5(x) f2(x) gene f4(x) mRNAn f3(x) mRNAc 13 Some basics of SBML model encoding ๏ “Events”: discontinuous actions triggered by system conditions g1(x) c g (x) 2 protein A f1(x) protein B . -
Representation of Date and Time Data Standard
REPRESENTATION OF DATE AND TIME DATA STANDARD Standard No.: EX000013.1 January 6, 2006 This standard has been produced through the Environmental Data Standards Council (EDSC). Representation of Date and Time Data Standard Std No.: EX000013.1 Foreword The Environmental Data Standards Council (EDSC) identifies, prioritizes and pursues the creation of data standards for those areas where information exchange standards will provide the most value in achieving environmental results. The Council involves Tribes and Tribal Nations, state and federal agencies in the development of the standards and then provides the draft materials for general review. Business groups, non-governmental organizations, and other interested parties may then provide input and comment for Council consideration and standard finalization. Standards are available at http://www.epa.gov/datastandards 1.0 INTRODUCTION This is a format standard which indicates how one displays a particular day within a Gregorian calendar month and specifies an instance of time in the day. Time is expressed in Coordinated Universal Time (UTC). UTC is the official time scale, maintained by the Bureau International des Poids et Mesures (BIPM), and the International Earth Rotation Service (IERS). Examples of the formats follow: a. Date only format When the need is for an expression only of a calendar date, then the complete representation shall be a single numeric data element comprising eight digits, where [YYYY] represents a calendar year, [MM] the ordinal number of a calendar month within the calendar year, and [DD] the ordinal number of a day within the calendar month. Extended format: YYYY-MM-DD EXAMPLE 1985-04-12 b. -
Modeling Using Biopax Standard
Modeling using BioPax standard Oliver Ruebenacker1 and Michael L. Blinov1 biological identification for each element of the model The key issue that should be resolved when making makes BioPax standard a recipe for reusable modeling reusable modeling modules is species recognition: are modules. species S1 of model 1 and species S2 of model 2 identical Currently, several online resources provide and should they be mapped onto the same species in a information about pathways in BioPax format: NetPath – merged model? When merging models, species Signal Transduction Pathways [5], Reactome - a curated recognition should be automated. We address this issue knowledgebase of biological pathways [6], and some others, by providing a modeling process compatible with with more resources aiming at BioPax representation. These Biological Pathways Exchange (BioPax) standard [1]. resources store complete pathways, pathways participants, and individual interactions. Due to principal differences Keywords — Model, Pathway data, BioPax, SBML, the between SBML and BioPax, there are no one-to-one Virtual Cell. translators from BioPax to SBML. We have implemented import of pathway data in BioPax standard into the Virtual he main format currently used for encoding of pathway Cell modeling framework [7], using Jena (Java application Tmodels is Systems Biology Markup Language (SBML) programming interface that provides support for Resource [2], which is designed mainly to enable the exchange of Description Framework). The imported data forms a model biochemical networks between different software packages skeleton where each species being automatically related to with little or no human intervention. SBML model contains some database entry. This model skeleton may lack data necessary for simulation, such as species, interactions simulation-related information (such as concentrations, among species, and kinetic laws for these interactions. -
Flask-JSON Documentation Release 0.3.4
Flask-JSON Documentation Release 0.3.4 Sergey Kozlov Aug 23, 2019 Contents 1 Installation 3 2 Initialization 5 3 Basic usage 7 4 Examples 9 5 Creating JSON responses 11 5.1 Jsonify HTTP errors........................................... 13 6 Encoding values 15 6.1 Iterables................................................. 15 6.2 Time values................................................ 15 6.3 Translation strings............................................ 16 6.4 Custom types............................................... 16 6.5 Encoding order.............................................. 17 7 Errors handing 19 8 JSONP support 21 9 Testing 23 10 Configuration 25 11 API 29 11.1 Low-Level API.............................................. 34 12 Flask-JSON Changelog 37 12.1 0.3.4................................................... 37 12.2 0.3.3................................................... 37 12.3 0.3.2................................................... 37 12.4 0.3.1................................................... 37 12.5 0.3.0................................................... 37 12.6 0.2.0................................................... 38 12.7 0.1.................................................... 38 12.8 0.0.1................................................... 38 i Index 39 ii Flask-JSON Documentation, Release 0.3.4 Flask-JSON is a simple extension that adds better JSON support to Flask application. It helps to handle JSON-based requests and provides the following features: • json_response() and @as_json to generate JSON responses. -
Integrating Biopax Knowledge with SBML Models
Integrating BioPAX knowledge with SBML models O. Ruebenacker, I.I. Moraru, J.S. Schaff, M. L. Blinov Center for cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, USA E-mail: [email protected] Abstract Online databases store thousands of molecular interactions and pathways, and numerous modeling software tools provide users with an interface to create and simulate mathematical models of such interactions. However, the two most widespread used standards for storing pathway data (Biological Pathway Exchange; BioPAX) and for exchanging mathematical models of pathways (Systems Biology Markup Langiuage; SBML) are structurally and semantically different. Conversion between formats (making data present in one format available in another format) based on simple one-to-one mappings may lead to loss or distortion of data, is difficult to automate, and often impractical and/or erroneous. This seriously limits the integration of knowledge data and models. In this paper we introduce an approach for such integration based on a bridging format that we named Systems Biology Pathway Exchange (SBPAX) alluding to SBML and BioPAX. It facilitates conversion between data in different formats by a combination of one-to- one mappings to and from SBPAX and operations within the SBPAX data. The concept of SBPAX is to provide a flexible description expanding around essential pathway data – basically the common subset of all formats describing processes, the substances participating in these processes and their locations. SBPAX can act as a repository for molecular interaction data from a variety of sources in different formats, and the information about their relative relationships, thus providing a platform for converting between formats and documenting assumptions used during conversion, gluing (identifying related elements across different formats) and merging (creating a coherent set of data from multiple sources) data.