Presentation given at the Victorian Systems Biology Symposium (http://www.emblaustralia.org/About_us/news/mike-hucka.aspx) at the Walter and Eliza Hall Institute in Melbourne, Australia, on 20 August 2013.
Creating a new language to support open innovationMike Hucka
Presentation given on 19 August 2013 at a BioBriefings meeting of the BioMelbourne Network (http://www.biomelbourne.org/events/view/289) in Melbourne, Australia.
A summary of various COMBINE standardization activitiesMike Hucka
Invited presentation given at the Whole-Cell Modeling Summer School, held in Rostock, Germany, March 2015.
https://sites.google.com/site/vwwholecellsummerschool/important-dates/programm
Recent developments in the world of SBML (the Systems Biology Markup Language) Mike Hucka
The document discusses recent developments in SBML (Systems Biology Markup Language), including new features added to software tools like the SBML Test Suite, the addition of packages to SBML Level 3 to support new model types, and ongoing work to evolve SBML standards to better enable model sharing and reuse. SBML has become widely adopted for representing computational models in systems biology, with many software tools and thousands of publicly available models using the standard.
SBML (the Systems Biology Markup Language)Mike Hucka
Morning tutorial given at the COMBINE/ERASysApp day of tutorials on "Modelling and Simulation of Biological Models" on Sunday, September 14, ahead of ICSB 2014 in Melbourne, Australia.
Short summary of recent SBML developments given at the COMBINE (COmputational Modeling in BIology NEtwork) 2014 meeting held at the University of Southern California in August, 2014. The meeting page is available at http://co.mbine.org/events/COMBINE_2014
A Profile of Today's SBML-Compatible SoftwareMike Hucka
Slides from presentation given at the Workshop on Interoperability in Scientific Computing during the 7th IEEE International Conference on e-Science Stockholm, Sweden, December 5, 2011.
A status update on COMBINE standardization activities, with a focus on SBMLMike Hucka
The document discusses the Systems Biology Markup Language (SBML), which is a format for representing computational models of biological processes that has been under development since 2000; it describes the core concepts of SBML including reactions, species, compartments, and parameters as well as SBML levels and packages that extend its capabilities; and it provides information on where to find SBML specifications, software, and libraries.
Creating a new language to support open innovationMike Hucka
Presentation given on 19 August 2013 at a BioBriefings meeting of the BioMelbourne Network (http://www.biomelbourne.org/events/view/289) in Melbourne, Australia.
A summary of various COMBINE standardization activitiesMike Hucka
Invited presentation given at the Whole-Cell Modeling Summer School, held in Rostock, Germany, March 2015.
https://sites.google.com/site/vwwholecellsummerschool/important-dates/programm
Recent developments in the world of SBML (the Systems Biology Markup Language) Mike Hucka
The document discusses recent developments in SBML (Systems Biology Markup Language), including new features added to software tools like the SBML Test Suite, the addition of packages to SBML Level 3 to support new model types, and ongoing work to evolve SBML standards to better enable model sharing and reuse. SBML has become widely adopted for representing computational models in systems biology, with many software tools and thousands of publicly available models using the standard.
SBML (the Systems Biology Markup Language)Mike Hucka
Morning tutorial given at the COMBINE/ERASysApp day of tutorials on "Modelling and Simulation of Biological Models" on Sunday, September 14, ahead of ICSB 2014 in Melbourne, Australia.
Short summary of recent SBML developments given at the COMBINE (COmputational Modeling in BIology NEtwork) 2014 meeting held at the University of Southern California in August, 2014. The meeting page is available at http://co.mbine.org/events/COMBINE_2014
A Profile of Today's SBML-Compatible SoftwareMike Hucka
Slides from presentation given at the Workshop on Interoperability in Scientific Computing during the 7th IEEE International Conference on e-Science Stockholm, Sweden, December 5, 2011.
A status update on COMBINE standardization activities, with a focus on SBMLMike Hucka
The document discusses the Systems Biology Markup Language (SBML), which is a format for representing computational models of biological processes that has been under development since 2000; it describes the core concepts of SBML including reactions, species, compartments, and parameters as well as SBML levels and packages that extend its capabilities; and it provides information on where to find SBML specifications, software, and libraries.
Standards and software: practical aids for reproducibility of computational r...Mike Hucka
My presentation during the session titled "Reproducibility of computational research: methods to avoid madness" on Wednesday, 17 September 2014, during ICSB 2014, held in Melbourne, Australia.
Modeling and meta-modeling presentation at LTH, Sweden Saïd Assar
This document provides an overview of modeling and meta-modeling in information systems and software engineering. It begins with introductions to modeling and defines a model as any representation that can help answer questions about a system. It provides examples of different types of models used in computer science. It then defines meta-models as formal representations of models, and provides examples of meta-models. It discusses related concepts like language syntax definitions. It also covers issues around instantiating meta-models. Finally, it discusses some computerized tools for meta-modeling like MetaEdit.
Requirements variability specification for data intensive softwareijseajournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements specification methodology based on concepts more close to the nature and which are inspired from
genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology seems to be promising in data intensive software requirements variability specification.
Development, distribution and use of open source software comprise a market of data (source code, bug reports, documentation, number of downloads, etc.) from projects, developers and users. This large amount of data makes it difficult for people involved to make sense of implicit links between software projects, e.g., dependencies, patterns, licenses. This context raises the question of what techniques and mechanisms can be used to help users and developers to link related pieces of information across software projects. In this paper, we propose a framework for a marketplace enhanced using linked open data (LOD) technology for linking software artifacts within projects as well as across software projects. The marketplace provides the infrastructure for collecting and aggregating software engineering data as well as developing services for mining, statistics, analytics and visualization of software data. Based on cross-linking software artifacts and projects, the marketplace enables developers and users to understand the individual value of components, their relationship to bigger software systems. Improved understanding creates new business opportunities for software companies: users will be better able to analyze and compare projects, developers can increase the visibility of their products, hosts may offer plug-ins and services over the data to paying customers.
CS4443 - Modern Programming Language IDilawar Khan
This document outlines the course details for CS4443 – Modern Programming Language – I. The 3 credit hour course uses C# and .NET to teach programming skills and has prerequisites of Object Oriented Programming. Grading is based 50% on internal evaluations like assignments, tests, and attendance and 50% on a final exam. The course covers topics like the C# language, OOP, classes, inheritance, exceptions handling, generics, delegates and events over 17 weeks.
The document presents an approach called Convolutional Analysis of code Metrics Evolution (CAME) that uses a convolutional neural network to detect anti-patterns by analyzing the historical evolution of source code metrics at the class level. An evaluation on 7 open-source systems shows that considering longer histories of metrics improves detection performance and that CAME outperforms other machine learning and anti-pattern detection techniques in terms of precision, recall, and F-measure.
This document outlines the course objectives, policies, and schedule for EE5440 - Computer Architecture, a 3-credit course with prerequisites in Computer Organization. The course objectives cover processor design, instruction sets, addressing, control structures, memory hierarchies, pipelining, parallelism, and multiprocessor systems. Course policies require timely attendance of at least 75% of classes and adherence to assignment deadlines. Grading will be based 50% on a midterm and final exam, and 50% on assignments, presentations, quizzes, and attendance. The course will cover topics like basic computer structure, instruction set architecture, I/O systems, processor design, arithmetic, memory systems, caches, and virtual memory over 16 weekly classes
Research Developments and Directions in Speech Recognition and ...butest
The document discusses research developments and directions in speech recognition and understanding. It outlines significant developments including improvements to infrastructure like hardware, corpora, and evaluation benchmarks. It also discusses advances in knowledge representation like acoustic features and graph representations, as well as models and algorithms like hidden Markov models and discriminative training. The document proposes six potential "grand challenges" for future research, including creating systems robust to everyday audio variability, rapidly developing speech technologies for emerging languages with limited resources, and enabling spoken language comprehension.
This document summarizes experience in software development including Java programming, databases, software engineering, web and mobile application development, software testing and verification, and an individual software development project. Key topics covered include object oriented programming in Java, the Java collection framework, relational databases and SQL, software engineering processes, mobile and web development, software testing principles and techniques, and a project to build a web application to track student attendance.
This document discusses various architectural styles for information systems. It begins by defining data and information, then describes factors that affect the usefulness of information like quality, timeliness, completeness and relevance. It then presents a taxonomy of architectural styles including data flow, data-centered, virtual machine, independent component, and call-and-return styles. For each style it provides the goals, characteristics, advantages, and disadvantages. It also discusses heterogeneous styles and different types of management information systems.
Propelling Standards-based Sharing and Reuse in Instructional Modeling Commun...Michael Derntl
This document summarizes the Open Graphical Learning Modeler (OpenGLM) tool, which allows for standards-based sharing of instructional models within communities. OpenGLM builds upon existing modeling tools by integrating a sharing space called the Open ICOPER Content Space, which allows users to search, retrieve, annotate, enrich, and share instructional models. The document describes how OpenGLM streamlines the reuse and adaptation of instructional models for new courses. An evaluation with university users found benefits like reuse of materials and shared resources, though limitations around IT literacy, motivation, and intellectual property were also noted.
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Common ground between modelers and simulation software: the Systems Biology M...Mike Hucka
The document discusses several standards for modeling biological processes including SBML, SBO, MIRIAM, and SED-ML. SBML is a format for representing biological models that over 230 software systems support. SBO provides controlled vocabularies to annotate SBML models to make their meaning more precise. MIRIAM defines guidelines for including minimum provenance information in models. SED-ML is a format for recording simulation experiments to reproduce modeling results across different software. These standards aim to facilitate sharing and reuse of biological models.
SBML, SBML Packages, SED-ML, COMBINE Archive, and moreMike Hucka
SBML, SBML Packages, SED-ML, COMBINE Archive, and more is a presentation about standards for representing computational models in systems biology. It introduces SBML (Systems Biology Markup Language) as a format for exchanging models of biological processes between software tools. It describes extensions to SBML called packages that add new modeling constructs. It also briefly mentions related standards like SED-ML and COMBINE Archive.
System modeling techniques are used during requirements engineering and design to represent different perspectives of a system. Context models show the system and its environment, while process models illustrate system processes. Behavioral models include data flow diagrams for data processing and state machine diagrams for event-driven behavior. Semantic data models describe logical data structures. Object models represent system entities and relationships. CASE tools support creating and analyzing various system models during development. Prototyping, through evolutionary or throw-away approaches, helps validate requirements by allowing users to interact with early versions of the system. Rapid prototyping techniques include visual programming and reusing components.
Towards a Comprehensive Machine Learning BenchmarkTuri, Inc.
This document presents a framework for developing a comprehensive machine learning benchmark. It discusses identifying the core building blocks of machine learning algorithms, such as linear algebra, data characteristics, and memory access. It proposes evaluating these building blocks using representative algorithms, datasets, and configurations. Thousands of executions are clustered into a smaller set capturing different software and hardware behaviors. The resulting benchmark suite of 50 workloads incorporates the main building blocks and bottlenecks to help evaluate machine learning performance.
This document discusses SBML (Systems Biology Markup Language), SBGN (Systems Biology Graphical Notation), and BioModels.net. SBML is a standard format for representing computational models of biochemical networks that allows models to be exchanged between software tools and researchers. Over 100 software tools now support SBML. SBGN is a project to develop a standard notation for diagrams of cellular networks. BioModels.net is a database of models encoded in SBML that has been made possible by the adoption of SBML as a common exchange format.
SADP PPTs of all modules - Shanthi D.L.pdfB.T.L.I.T
The document discusses design patterns and software architecture. It includes an introduction to design patterns, describing what they are and how they solve common design problems. It also provides details on various design patterns organized in a catalog, including creational, structural and behavioral patterns. The document gives an example of using the Model-View-Controller (MVC) pattern in Smalltalk and provides a template for describing design patterns.
Standards and software: practical aids for reproducibility of computational r...Mike Hucka
My presentation during the session titled "Reproducibility of computational research: methods to avoid madness" on Wednesday, 17 September 2014, during ICSB 2014, held in Melbourne, Australia.
Modeling and meta-modeling presentation at LTH, Sweden Saïd Assar
This document provides an overview of modeling and meta-modeling in information systems and software engineering. It begins with introductions to modeling and defines a model as any representation that can help answer questions about a system. It provides examples of different types of models used in computer science. It then defines meta-models as formal representations of models, and provides examples of meta-models. It discusses related concepts like language syntax definitions. It also covers issues around instantiating meta-models. Finally, it discusses some computerized tools for meta-modeling like MetaEdit.
Requirements variability specification for data intensive softwareijseajournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements specification methodology based on concepts more close to the nature and which are inspired from
genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology seems to be promising in data intensive software requirements variability specification.
Development, distribution and use of open source software comprise a market of data (source code, bug reports, documentation, number of downloads, etc.) from projects, developers and users. This large amount of data makes it difficult for people involved to make sense of implicit links between software projects, e.g., dependencies, patterns, licenses. This context raises the question of what techniques and mechanisms can be used to help users and developers to link related pieces of information across software projects. In this paper, we propose a framework for a marketplace enhanced using linked open data (LOD) technology for linking software artifacts within projects as well as across software projects. The marketplace provides the infrastructure for collecting and aggregating software engineering data as well as developing services for mining, statistics, analytics and visualization of software data. Based on cross-linking software artifacts and projects, the marketplace enables developers and users to understand the individual value of components, their relationship to bigger software systems. Improved understanding creates new business opportunities for software companies: users will be better able to analyze and compare projects, developers can increase the visibility of their products, hosts may offer plug-ins and services over the data to paying customers.
CS4443 - Modern Programming Language IDilawar Khan
This document outlines the course details for CS4443 – Modern Programming Language – I. The 3 credit hour course uses C# and .NET to teach programming skills and has prerequisites of Object Oriented Programming. Grading is based 50% on internal evaluations like assignments, tests, and attendance and 50% on a final exam. The course covers topics like the C# language, OOP, classes, inheritance, exceptions handling, generics, delegates and events over 17 weeks.
The document presents an approach called Convolutional Analysis of code Metrics Evolution (CAME) that uses a convolutional neural network to detect anti-patterns by analyzing the historical evolution of source code metrics at the class level. An evaluation on 7 open-source systems shows that considering longer histories of metrics improves detection performance and that CAME outperforms other machine learning and anti-pattern detection techniques in terms of precision, recall, and F-measure.
This document outlines the course objectives, policies, and schedule for EE5440 - Computer Architecture, a 3-credit course with prerequisites in Computer Organization. The course objectives cover processor design, instruction sets, addressing, control structures, memory hierarchies, pipelining, parallelism, and multiprocessor systems. Course policies require timely attendance of at least 75% of classes and adherence to assignment deadlines. Grading will be based 50% on a midterm and final exam, and 50% on assignments, presentations, quizzes, and attendance. The course will cover topics like basic computer structure, instruction set architecture, I/O systems, processor design, arithmetic, memory systems, caches, and virtual memory over 16 weekly classes
Research Developments and Directions in Speech Recognition and ...butest
The document discusses research developments and directions in speech recognition and understanding. It outlines significant developments including improvements to infrastructure like hardware, corpora, and evaluation benchmarks. It also discusses advances in knowledge representation like acoustic features and graph representations, as well as models and algorithms like hidden Markov models and discriminative training. The document proposes six potential "grand challenges" for future research, including creating systems robust to everyday audio variability, rapidly developing speech technologies for emerging languages with limited resources, and enabling spoken language comprehension.
This document summarizes experience in software development including Java programming, databases, software engineering, web and mobile application development, software testing and verification, and an individual software development project. Key topics covered include object oriented programming in Java, the Java collection framework, relational databases and SQL, software engineering processes, mobile and web development, software testing principles and techniques, and a project to build a web application to track student attendance.
This document discusses various architectural styles for information systems. It begins by defining data and information, then describes factors that affect the usefulness of information like quality, timeliness, completeness and relevance. It then presents a taxonomy of architectural styles including data flow, data-centered, virtual machine, independent component, and call-and-return styles. For each style it provides the goals, characteristics, advantages, and disadvantages. It also discusses heterogeneous styles and different types of management information systems.
Propelling Standards-based Sharing and Reuse in Instructional Modeling Commun...Michael Derntl
This document summarizes the Open Graphical Learning Modeler (OpenGLM) tool, which allows for standards-based sharing of instructional models within communities. OpenGLM builds upon existing modeling tools by integrating a sharing space called the Open ICOPER Content Space, which allows users to search, retrieve, annotate, enrich, and share instructional models. The document describes how OpenGLM streamlines the reuse and adaptation of instructional models for new courses. An evaluation with university users found benefits like reuse of materials and shared resources, though limitations around IT literacy, motivation, and intellectual property were also noted.
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Common ground between modelers and simulation software: the Systems Biology M...Mike Hucka
The document discusses several standards for modeling biological processes including SBML, SBO, MIRIAM, and SED-ML. SBML is a format for representing biological models that over 230 software systems support. SBO provides controlled vocabularies to annotate SBML models to make their meaning more precise. MIRIAM defines guidelines for including minimum provenance information in models. SED-ML is a format for recording simulation experiments to reproduce modeling results across different software. These standards aim to facilitate sharing and reuse of biological models.
SBML, SBML Packages, SED-ML, COMBINE Archive, and moreMike Hucka
SBML, SBML Packages, SED-ML, COMBINE Archive, and more is a presentation about standards for representing computational models in systems biology. It introduces SBML (Systems Biology Markup Language) as a format for exchanging models of biological processes between software tools. It describes extensions to SBML called packages that add new modeling constructs. It also briefly mentions related standards like SED-ML and COMBINE Archive.
System modeling techniques are used during requirements engineering and design to represent different perspectives of a system. Context models show the system and its environment, while process models illustrate system processes. Behavioral models include data flow diagrams for data processing and state machine diagrams for event-driven behavior. Semantic data models describe logical data structures. Object models represent system entities and relationships. CASE tools support creating and analyzing various system models during development. Prototyping, through evolutionary or throw-away approaches, helps validate requirements by allowing users to interact with early versions of the system. Rapid prototyping techniques include visual programming and reusing components.
Towards a Comprehensive Machine Learning BenchmarkTuri, Inc.
This document presents a framework for developing a comprehensive machine learning benchmark. It discusses identifying the core building blocks of machine learning algorithms, such as linear algebra, data characteristics, and memory access. It proposes evaluating these building blocks using representative algorithms, datasets, and configurations. Thousands of executions are clustered into a smaller set capturing different software and hardware behaviors. The resulting benchmark suite of 50 workloads incorporates the main building blocks and bottlenecks to help evaluate machine learning performance.
This document discusses SBML (Systems Biology Markup Language), SBGN (Systems Biology Graphical Notation), and BioModels.net. SBML is a standard format for representing computational models of biochemical networks that allows models to be exchanged between software tools and researchers. Over 100 software tools now support SBML. SBGN is a project to develop a standard notation for diagrams of cellular networks. BioModels.net is a database of models encoded in SBML that has been made possible by the adoption of SBML as a common exchange format.
SADP PPTs of all modules - Shanthi D.L.pdfB.T.L.I.T
The document discusses design patterns and software architecture. It includes an introduction to design patterns, describing what they are and how they solve common design problems. It also provides details on various design patterns organized in a catalog, including creational, structural and behavioral patterns. The document gives an example of using the Model-View-Controller (MVC) pattern in Smalltalk and provides a template for describing design patterns.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
This document provides an introduction and overview of Model-Driven Architecture (MDA). It discusses the motivation for MDA, including generating code from configurations to reduce boring code. It describes the key concepts of MDA, including using models as the core artifacts written in formal languages defined by metamodels. It also explains how models can be transformed from one form to another, such as from platform-independent to platform-specific models. Finally, it presents a case study of applying MDA to a triathlon tracking system as an example.
This document provides an overview of AutoML. It discusses how AutoML aims to automate the machine learning process to make ML accessible even for non-experts. The document outlines the main stages in an AutoML system, including data preparation, feature engineering, model generation, and model evaluation. It describes techniques used in each stage like data cleaning, feature selection, neural architecture search, and early stopping. The document also notes some open problems in AutoML like improving performance on NLP tasks and increasing reproducibility.
Model-Based Systems Engineering (MBSE) is an ambiguous concept that means many things to many different people. The purpose of this presentation is to “de-mystify” MBSE, with the intent of moving the sub-discipline forward. Model-Based Systems Engineering was envisioned to manage the increasing complexity within systems and System of Systems (SoS). This presentation defines MBSE as the formalized application of modeling (static and dynamic) to support system design and analysis, throughout all phases of the system lifecycle, and through the collection of modeling languages, structures, model-based processes, and presentation frameworks used to support the discipline of systems engineering in a model-based or model-driven context. Using this definition, the components of MBSE (modeling languages, processes, structures, and presentation frameworks) are defined. The current state of MBSE is then evaluated against a set of effective measures. Finally, this presents a vision for the future direction of MBSE.
The document provides an overview of database management systems (DBMS). It discusses the need for DBMS, different database architectures including centralized, client-server and distributed. It also covers data models, ER diagrams, relational models, and SQL. Key advantages of DBMS over file systems include reducing data redundancy, improving data integrity and security, and enabling concurrent access.
Recent software and services to support the SBML community Mike Hucka
MOCCASIN analyzed the MATLAB model and generated an equivalent SBML model with:
- 2 species named 'x1' and 'x2'
- 4 parameters named 'a', 'b', 'c', and 'd'
- An initial assignment block setting the initial concentrations of 'x1' and 'x2'
- A single reaction that uses the parameters and species to define the rate of change of each species according to the equations in the MATLAB code
Afternoon tutorial given at the COMBINE/ERASysApp day of tutorials on "Modelling and Simulation of Biological Models" on Sunday, September 14, ahead of ICSB 2014 in Melbourne, Australia.
This document summarizes research into software engineering patterns for designing machine learning systems. A survey found that ML developers have little knowledge of applicable architecture and design patterns. A literature review identified 19 scholarly papers and 19 gray documents discussing practices. The research aims to classify ML patterns according to the typical ML pipeline process and software development lifecycle. It identifies 12 architecture patterns, 13 design patterns, and 8 anti-patterns for ML systems. Future work includes documenting the patterns fully and analyzing their impact on ML system quality attributes.
Slides presented at MODEVAR 2024 co-located with VaMoS 2024: https://modevar.github.io/program/ Thanks to Jessie Galasso and Chico Sundermann for the invitation Abstract: "Variability models (e.g., feature models) are widely considered in software engineering research and practice, in order to develop software product lines, configurable systems, generators, or adaptive systems. Numerous success stories of variability models have been reported in different domains (e.g., automotive, aerospace, avionics) and the applicability broadens. However, the use of variability models is not yet universally adopted… Why? Some examples: variability models are not continuously extracted from projects and artefacts; each time a variability model is used, its expressiveness and language should be specialized; learning-based models are decoupled from variability models; modeling software variability should cover different layers and their interactions, etc. The list is arguably opinionated, incomplete, and based on my own practical experience, but also on observations of existing works. I will give 24 reasons to occupy your day as a researcher or practitioner in the field of variability modeling."
Slides: https://github.com/acherm/24RWVMANYU-VaMoS-MODEVAR/blob/main/vamos2024.md (Markdown content) PDF: https://github.com/acherm/24RWVMANYU-VaMoS-MODEVAR/blob/main/VaMoS2024-MODEVAR.pdf (slides in PDF format)
Studying Software Engineering Patterns for Designing Machine Learning SystemsHironori Washizaki
Hironori Washizaki, Hiromu Uchida, Foutse Khomh and Yann-Gaël Guéhéneuc, “Studying Software Engineering Patterns for Designing Machine Learning Systems,” The 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019), Tokyo, Japan, on December 13-14, 2019.
Prof. Luciana Tricai Cavalini, MD, PhD. presents the Multi-Level Healthcare Information Modelling specifications for Third International Symposium on Foundations of Health Information Engineering and Systems (FHIES) 2013 conference. There is also a video on YouTube http://goo.gl/9QPW5x
It is based on the paper: "Use of XML Schema Definition for the Development of Semantically Interoperable Healthcare Applications" to be published in an upcoming issue of Springer LNCS.
Getting Unstuck: Working with Legacy Code and DataCory Foy
From this presentation for the IASA in 2007, Cory covers common challenges in dealing with Legacy Code and Data, and some tools and techniques for handling them.
Software Modeling and Artificial Intelligence: friends or foes?Jordi Cabot
(1) Modeling and AI can be both friends and foes, depending on how they are used together.
(2) Model-driven engineering (MDE) approaches can help make AI systems like chatbots and machine learning pipelines more rigorous, robust, and interoperable by applying modeling principles.
(3) AI techniques like machine learning and deep learning also have the potential to enhance MDE, for example by enabling automated model transformations and smarter modeling tools with features like autocomplete.
Similar to A new language for a new biology: How SBML and other tools are transforming models of life (20)
This presentation gives an overview of Caltech DIBS, a system for digital controlled lending (CDL) implemented by the California Institute of Technology Library in early 2021 to support course serves and other academic library needs at Caltech.
This document reports on a survey of 69 software developers and non-developers about how they search for and evaluate ready-to-run software. The survey found that the most important characteristics when searching for software included the size of the software, similarity to other software used, software architecture, quality of support, and other people's opinions. Respondents also rated characteristics like programming languages used, reputation of developers, ease of use, licensing, and performance as usually or above-average in importance. The document concludes by recommending that software developers make key information like features, standards, pricing, requirements, and licensing prominent in documentation to improve discoverability and reuse of their software.
The document discusses COMBINE, which stands for Computational Modeling in Biology Network. COMBINE coordinates standards development, meetings, and infrastructure to support modeling in biology. It brings together people from different fields to develop standards through multiple phases of creation, evolution, and support. COMBINE also coordinates annual meetings and hackathons to facilitate software development and interoperability. It provides resources like common URIs to support adopted specifications.
Reproducibility of computational research: methods to avoid madness (Session ...Mike Hucka
Introduction on the session "Reproducibility of computational research: methods to avoid madness" held Wednesday, September 17, during ICSB 2014 in Melbourne, Australia, 2014.
Update on SBML for Tuesday Sep. 17 (COMBINE 2013)Mike Hucka
Michael Hucka provided an update on the status of SBML (Systems Biology Markup Language) development at the COMBINE 2013 conference in Paris. Key points included:
- The SBML editors are working to finalize changes for Version 2 of SBML Level 3 and Version 5 of Level 2, focusing on backward compatibility.
- Detailed status pages track the progress of package specifications.
- A status tracking spreadsheet monitors the progress of SBML Level 3 packages including hierarchical models, constraints, qualitative models, and more.
- Discussions are ongoing to develop specifications for multistate, multicomponent and multicompartment species to support structured entities and pattern rules.
- A draft specification has been
Systems biology aims to understand biological systems as a whole rather than individual parts. Early criticisms saw molecular biology as too reductionist. Systems modeling using mathematical approaches also emerged. Standards like SBML and community-building efforts were important to allow sharing and integration of computational models between different research groups and software tools. This helped a systems biology community flourish by providing interoperability between various modeling approaches and data types.
SBML and related resources and standardization effortsMike Hucka
Slides from presentation given on November 21, 2011, at the 4th Global COE International Symposium on Physiome and Systems Biology for Integrated Life Sciences and Predictive Medicine, in Osaka, Japan.
Finding common ground between modelers and simulation software in systems bio...Mike Hucka
The document discusses Systems Biology Markup Language (SBML), a format for representing computational models of biological processes. SBML allows models to be exchanged between different software applications and defines concepts like species, compartments, reactions, and parameters. It aims to serve as a common language for software in systems biology. The document outlines some basic SBML concepts and notes that the scope of SBML is not limited to metabolic models, but can also represent signaling pathways, neural models, pharmacokinetic models, and other types of models. It discusses how SBML continues to evolve through new Levels and Packages to support additional model constructs and capabilities.
General updates about SBML and SBML Team activitiesMike Hucka
The summary provides updates about SBML and the SBML Team's activities:
1. The SBML.org website was updated with a new version of the SBML software survey and statistics about 81 SBML software tools reported between May-July.
2. The SBML development process is progressing with work on specifications for the Hierarchical Model Composition package and new LaTeX templates for SBML package specifications.
3. A vote for a new SBML editor will take place, with nominations open to the sbml-discuss mailing list. The SBML Team software like libSBML continues to see strong download rates, with over 3,400 downloads of libSBML 5.0.0 since April
SBML (Systems Biology Markup Language) is a format for representing computational models of biological processes. It defines data structures and serialization to XML for representing models in a neutral, machine-readable way. Development of SBML started in 2000 with the goal of facilitating exchange of models between software tools and databases. SBML provides syntax but limited semantics, so standard annotation schemes have been developed to link models to external data resources and provide additional meaning. The scope of SBML encompasses many types of biological models and is expanding through new packages to support additional model types.
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A new language for a new biology: How SBML and other tools are transforming models of life
1. A new language for a new biology: How SBML
and other tools are transforming models of life
Michael Hucka, Ph.D.
Department of Computing + Mathematical Sciences
California Institute of Technology
Pasadena, CA, USA
Victorian Systems Biology Symposium, Australia, August 2013
Email: mhucka@caltech.edu Twitter: @mhucka
2. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
3. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
7. Many models have traditionally been published this way
Problems:
• Errors in printing
• Missing information
• Dependencies on
implementation
• Outright errors
• Can be a huge
effort to recreate
Is it enough to communicate the model in a paper?
8. Is it enough to make your (software X) code available?
It’s vital for good science:
• Someone with access to the same software can try to run it,
understand it, verify the computational results, build on them, etc.
• Opinion: you should always do this in any case
9. Is it enough to make your (software X) code available?
It’s vital for good science—
• Someone with access to the same software can try to run it,
understand it, build on it, etc.
• Opinion: you should always do this in any case
But it’s still not ideal for communication of scientific results:
• Doesn’t necessarily encode biological semantics of the model
• What if they don’t have access to the same software?
• What if they don’t want to use that software?
• What if they want to use a different conceptual framework?
• And how will people be able to relate the model to other work?
11. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
13. Format for representing computational models of biological processes
• Data structures + usage principles + serialization to XML
• (Mostly) Declarative, not procedural—not a scripting language
Neutral with respect to modeling framework
• E.g., ODE, stochastic systems, etc.
Important: software reads/writes SBML, not humans
SBML = Systems Biology Markup Language
15. The process is central
• Literally called a“reaction”in SBML
• Participants are pools of entities (biochemical species)
Models can further include:
• Compartments
• Other constants & variables
• Discontinuous events
• Other, explicit math
Core SBML concepts are fairly simple
• Unit definitions
• Annotations
16. SBML is now widely used
Dozens of journals accept models in SBML format
100’s of software tools available today
1000’s of models available in SBML format today
0
100
200
300
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
254+ today
17. Contents of BioModels Database
Contents today:
• 142,000+ pathway models (converted from KEGG)
• 460+ hand-curated quantitative models
• 460+ non-curated quantitative models
8%
2%
3%
6%
6%
7%
8%
9%
24%
27%
signal transduction
metabolic process
multicelullar organismal process
rhythmic process
cell cycle
homeostatic process
response to stimulus
cell death
localization
others (e.g., developmental process)
Database data from 2013
18. Free software libraries – libSBML
Reads, writes, validates SBML
Can check & convert units
Written in portable C++
Runs on Linux, Mac, Windows
APIs for C, C++, C#, Java, Octave,
Perl, Python, R, Ruby, MATLAB
Well documented API
Open-source (LGPL)
http://sbml.org/Software/libSBML
19. Evolution of SBML continues
Today: SBML Level 3
• Level 3 Core provides framework for common models
• Level 3 packages add additional constructs to the Core
20. Level 3 package What it enables
Hierarchical model composition Models containing submodels ✔
Flux balance constraints Constraint-based models ✔
Qualitative models Petri net models, Boolean models ✔
Graph layout Diagrams of models ✔
Multicomponent/state species Entities w/ structure; also rule-based models draft
Spatial Nonhomogeneous spatial models draft
Graph rendering Diagrams of models draft
Groups Arbitrary grouping of components draft
Distributions Numerical values as statistical distributions in dev
Arrays & sets Arrays or sets of entities in dev
Dynamic structures Creation & destruction of components in dev
Annotations Richer annotation syntax
Status
21. NationalInstituteofGeneralMedicalSciences(USA)
European Molecular Biology Laboratory (EMBL)
JST ERATO Kitano Symbiotic Systems Project (Japan) (to 2003)
JST ERATO-SORST Program (Japan)
ELIXIR (UK)
Beckman Institute, Caltech (USA)
Keio University (Japan)
International Joint Research Program of NEDO (Japan)
Japanese Ministry of Agriculture
Japanese Ministry of Educ., Culture, Sports, Science and Tech.
BBSRC (UK)
National Science Foundation (USA)
DARPA IPTO Bio-SPICE Bio-Computation Program (USA)
Air Force Office of Scientific Research (USA)
STRI, University of Hertfordshire (UK)
Molecular Sciences Institute (USA)
SBML funding sources over the past 13+ years
22. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
26. Raw models alone are insufficient
Need standard schemes for
machine-readable annotations
• Identify entities
• Mathematical semantics
• Links to other data resources
• Authorship & pub. info
Modelerswanttousetheirownconventions
Low info
content
No standard
identifiers
27. Addresses 2 general areas of annotation needs:
MIRIAM is not specific to SBML
MIRIAM(MinimumInformationRequestedIntheAnnotationofModels)
Requirements for
reference correspondence
Scheme for encoding
annotations
Annotations for
attributing model
creators & sources
Annotations for
referring to external
data resources
28. Addresses 2 general areas of annotation needs:
MIRIAM is not specific to SBML
MIRIAM(MinimumInformationRequestedIntheAnnotationofModels)
Requirements for
reference correspondence
Scheme for encoding
annotations
Annotations for
attributing model
creators & sources
Annotations for
referring to external
data resources
Annotations for
referring to external
data resources
29. Example of a problem that can be solved with annotations
http://www.ebi.ac.uk/chebi
Low info
content
30. Example of a problem that can be solved with annotations
http://www.ebi.ac.uk/chebi
Low info
content
Known by different names –
do you want to write all of
them into your model?
salicylic acid
31. MIRIAM annotations for external references
Goal: link model constituents to corresponding entities in
bioinformatics resources (e.g., databases, controlled vocabularies)
• Supports:
- Precise identification of model constituents
- Discovery of models that concern the same thing
- Comparison of model constituents between different models
MIRIAM approach avoids putting data content directly in the model
• Instead, it points at external resources that contain the data
32. How do we create globally unique identifiers consistently?
Long story short—developed by the Le Novère group at the EBI
• Resource identifiers (URIs) combine 2 parts:
• There’s a registry for namespaces: MIRIAM Registry
- Allows people & software to use same namespace identifiers
• There’s a URI resolution service: MIRIAM Resources & identifiers.org
- Allows people & software to take a given identifier and figure
out what it points to
namespace entity identifier
{
{
Identifies a dataset Identifies a datum
within the dataset
33. Another problem: software can’t read figure legends
?
BIOMD0000000319 in BioModels Database
Decroly & Goldbeter, PNAS, 1982
34. SED-ML = Simulation Experiment Description ML
Application-independent format
•Captures procedures, algorithms, parameter values
Can be used for
•Simulation experiments encoding parametrizations & perturbations
•Simulations using more than one model and/or method
•Data manipulations to produce plot(s)
http://sedml.org
Simulation
Model
Task Data generators
Reports
35. Efforts like SED-ML improve reproducibility of publications
Waltemath et al.,
BMC Sys Bio 5, 2011.
36. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
37. Need interoperable formats, but developing them is not easy
Need people with diverse set of knowledge & skills
• Scientific needs
• Technical implementation skills
• Practical experience
Need manage multiple phases of a standardization effort
• Creation
• Evolution
• Support
38. Need interoperable formats, but developing them is not easy
Need people with diverse set of knowledge & skills
• Scientific needs
• Technical implementation skills
• Practical experience
Need manage multiple phases of a standardization effort
• Creation
• Evolution
• Support
} This is just for the specification of the
standards, to say nothing of the necessary
software and other infrastructure!
39. Realizations about the state of affairs in late-2000’s
• Many standardization efforts overlapped, but lacked coordination
• Efforts were inventing their own processes from scratch
• Many individual meetings meant more travel for many people
• Limited and fragile funding didn’t support solid, coherent base
COMBINE = Computational Modeling in Biology Network
• Coordinate standards development
• Develop common procedures & tools (but not impose them!)
• Coordinate meetings
• Provide a recognized voice
Motivations for the creation of COMBINE
40. Standardization efforts represented in COMBINE today
BioPAX
Qualifiers
GPML
COMBINE Standards
Associated Standardization Efforts
Related Standardization Efforts
42. Examples of community organization
Two main annual meetings, plus ad hoc workshops
• COMBINE meeting: status updates, presentations, outreach
- Next COMBINE: Paris, Sep 16–20, 2013
• HARMONY: Hackathon on Resources for Modeling in Biology
- Software development, interoperability hacking
COMBINE 2012, TorontoCOMBINE 2011, Heidelberg
43. COMBINE is open to all—and COMBINE needs you!
http://co.mbine.org
Current coordinators:
• Nicolas Le Novère, Mike Hucka, Falk Schreiber, Gary Bader
44. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
45. Time it well
• Too early and too late are bad
Start with actual stakeholders
• Address real needs, not perceived ones
Start with small team of dedicated developers
• Can work faster, more focused; also avoids“designed-by-committee”
Engage people constantly, in many ways
• Electronic forums, email, electronic voting, surveys, hackathons
Make the results free and open-source
• Makes people comfortable knowing it will always be available
Be creative about seeking funding
Some things we (maybe?) got right with SBML
46. Not waiting for implementations before freezing specifications
• Sometimes finalized specification before implementations tested it
- Especially bad when we failed to do a good job
‣ E.g.,“forward thinking”features, or“elegant”designs
Not formalizing the development process sufficiently
• Especially early in the history, did not have a very open process
Not resolving intellectual property issues from the beginning
• Industrial users ask“who has the right to give any rights to this?”
Some things we certainly got wrong
47. Nicolas Le Novère, Henning Hermjakob, Camille Laibe, Chen Li, Lukas Endler,
Nico Rodriguez, Marco Donizelli,Viji Chelliah, Mélanie Courtot, Harish Dharuri
Attendees at SBML 10th Anniversary Symposium, Edinburgh, 2010
John C. Doyle, Hiroaki Kitano
Mike Hucka, Sarah Keating, Frank Bergmann, Lucian Smith, Andrew Finney,
Herbert Sauro, Hamid Bolouri, Ben Bornstein, Bruce Shapiro, Akira Funahashi,
Akiya Juraku, Ben Kovitz
OriginalPI’s:
SBMLTeam:
SBMLEditors:
BioModelsDB:
Mike Hucka, Nicolas Le Novère, Sarah Keating, Frank Bergmann, Lucian Smith,
Chris Myers, Stefan Hoops, Sven Sahle, James Schaff, DarrenWilkinson
And a huge thanks to many others in the COMBINE community
This work was made possible thanks to a great community