scholarly journals LOICA: Logical Operators for Integrated Cell Algorithms

2021 ◽  
Author(s):  
Gonzalo Vidal ◽  
Carlos Vidal-Céspedes ◽  
Timothy James Rudge
Keyword(s):  
Design Automation ◽  
Object Oriented ◽  
Analysis Tool ◽  
Genetic Circuits ◽  
Logical Operators ◽  
Object Oriented Design ◽  
Design Build ◽  
High Level ◽  
Python Package ◽  
Mathematical And Computational Modeling

Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.

UML Based Design Approach for Storage System of Nano-Satellite

2014 ◽  
Vol 599-601 ◽  
pp. 530-533
Author(s):  
Hong Hao Wang ◽  
Hui Quan Wang ◽  
Zhong He Jin
Keyword(s):  
Storage System ◽  
Unified Modeling Language ◽  
Object Oriented ◽  
Modeling Language ◽  
Nand Flash ◽  
Unified Modeling ◽  
Object Oriented Design ◽  
High Level Modeling ◽  
High Level

Due to the complex timing sequence of NAND flash, a unified design process is urgently required to guarantee the reliability of storage system of nano-satellite. Unified Modeling Language (UML) is a widely used high level modeling language for object-oriented design. This paper adopts the UML as the design and modelling tool in the low level storage system design to elaborate the UML application in each phase of design in detail. The result shows taking UML as the modelling tool results in a clear and unambiguity design, which promotes the reliability and quality of software. At last, the feasibility of object-oriented implementation in C is presented.

A Survey of Object-Oriented Design Quality Improvement

2009 ◽  
pp. 2646-2664
Author(s):  
Juan José Olmedilla
Keyword(s):  
Quality Improvement ◽  
Evaluation Model ◽  
Object Oriented ◽  
Measurement Model ◽  
Design Quality ◽  
Quality Properties ◽  
Object Oriented Design ◽  
Design Improvement ◽  
High Level

The use of object-oriented (OO) architecture knowledge such as patterns, heuristics, principles, refactorings and bad smells improve the quality of designs, as Garzás and Piattini (2005) state in their study; according to it, the application of those elements impact on the quality of an OO design and can serve as basis to establish some kind of software design improvement (SDI) method. But how can we measure the level of improvement? Is there a set of accepted internal attributes to measure the quality of a design? Furthermore, if such a set exists will it be possible to use a measurement model to guide the SDI in the same way software processimprovement models (Humphrey, 1989; Paulk, Curtis, Chrissis, & Weber, 1993) are guided by process metrics (Fenton & Pfleeger, 1998)? Since (Chidamber & Kemerer, 1991) several OO metrics suites have been proposed to measure OO properties, such as encapsulation, cohesion, coupling and abstraction, both in designs and in code, in this chapter we review the literature to find out to which high level quality properties are mapped and if an OO design evaluation model has been formally proposed or even is possible.

A Survey of Object-Oriented Design Quality Improvement

2011 ◽  
pp. 282-306
Author(s):  
Juan José Olmedilla
Keyword(s):  
Evaluation Model ◽  
Object Oriented ◽  
Software Process Improvement ◽  
Measurement Model ◽  
Design Quality ◽  
Quality Properties ◽  
Object Oriented Design ◽  
Design Improvement ◽  
High Level

The use of object-oriented (OO) architecture knowledge such as patterns, heuristics, principles, refactorings and bad smells improve the quality of designs, as Garzás and Piattini (2005) state in their study; according to it, the application of those elements impact on the quality of an OO design and can serve as basis to establish some kind of software design improvement (SDI) method. But how can we measure the level of improvement? Is there a set of accepted internal attributes to measure the quality of a design? Furthermore, if such a set exists will it be possible to use a measurement model to guide the SDI in the same way software process improvement models (Humphrey, 1989; Paulk, Curtis, Chrissis, & Weber, 1993) are guided by process metrics (Fenton & Pfleeger, 1998)? Since (Chidamber & Kemerer, 1991) several OO metrics suites have been proposed to measure OO properties, such as encapsulation, cohesion, coupling and abstraction, both in designs and in code, in this chapter we review the literature to find out to which high level quality properties are mapped and if an OO design evaluation model has been formally proposed or even is possible.

scholarly journals SOFTARCH: TOOL SUPPORT FOR INTEGRATED SOFTWARE ARCHITECTURE DEVELOPMENT

2003 ◽  
Vol 13 (02) ◽  
pp. 125-151 ◽  
Author(s):  
JOHN GRUNDY ◽  
JOHN HOSKING
Keyword(s):  
Software Architecture ◽  
Object Oriented ◽  
Tool Support ◽  
Architecture Design ◽  
Object Oriented Design ◽  
Architecture Model ◽  
Run Time ◽  
High Level ◽  
Architecture Modelling ◽  
Software Architecture Design

A good software architecture design is crucial in successfully realising an object-oriented analysis (OOA) specification with an object-oriented design (OOD) model that meets the specification's functional and non-functional requirements. Most CASE tools and software architecture design notations do not adequately support software architecture modelling and analysis, integration with OOA and OOD methods and tools, and high-level, dynamic architectural visualisations of running systems. We describe SoftArch, an environment that provides flexible software architecture modelling using a concept of successive refinement and an extensible architecture meta-model. SoftArch provides extensible analysis tools enabling developers to analyse their architecture model properties. Run-time visualisation of systems uses dynamic annotation and animation of high-level architectural modelling views. SoftArch is integrated with a component-based CASE tool and run-time monitoring tool, and has facilities for 3rd party tool integration through a common exchange format. This paper discusses the motivation for SoftArch, its modelling, analysis and dynamic visualisation capabilities, and its integration with various analysis, design and implementation tools.

scholarly journals pyrpipe: a python package for RNA-Seq workflows

2020 ◽  
Author(s):  
Urminder Singh ◽  
Jing Li ◽  
Arun Seetharam ◽  
Eve Syrkin Wurtele
Keyword(s):  
Detailed Analysis ◽  
Source Code ◽  
Workflow Management ◽  
Object Oriented ◽  
Third Party ◽  
Rna Seq ◽  
Link Type ◽  
Computing Environments ◽  
High Level ◽  
Python Package

Implementing RNA-Seq analysis pipelines is challenging as data gets bigger and more complex. With the availability of terabytes of RNA-Seq data and continuous development of analysis tools, there is a pressing requirement for frameworks that allow for fast and efficient development, modification, sharing and reuse of workflows. Scripting is often used, but it has many challenges and drawbacks. We have developed a python package, python RNA-Seq Pipeliner (pyrpipe) that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in python, in an object-oriented manner. pyrpipe provides high level APIs to popular RNA-Seq tools. Pipelines can be customized by integrating new python code, third-party programs, or python libraries. Researchers can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in python and is compatible with python versions 3.6 and higher. All source code is available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source or from PyPi (https://pypi.org/project/pyrpipe). Documentation is available on Read the Docs (http://pyrpipe.rtfd.io).

scholarly journals Flapjack: a data management and analysis tool for genetic circuit characterization

2020 ◽  
Author(s):  
Guillermo Yáñez Feliú ◽  
Benjamín Earle Gómez ◽  
Verner Codoceo Berrocal ◽  
Macarena Muñoz Silva ◽  
Isaac N. Nuñez ◽  
...  
Keyword(s):  
Test Phase ◽  
Genetic Circuit ◽  
Analysis Tool ◽  
Genetic Circuits ◽  
Genetic Components ◽  
Data Registry ◽  
Design Build ◽  
Closing The Loop ◽  
Rest Api ◽  
Analysis Engine

AbstractCharacterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different labs and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parameterize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated to circuit part composition via SBOL. We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

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