The document discusses model meta-information and simulation experiment descriptions. It describes how meta-information helps understand models by providing context through annotations using controlled vocabularies. Standards like MIRIAM ensure meta-information is computer-readable. Simulation Experiment Description Markup Language (SED-ML) allows storing and exchanging simulation experiments to facilitate model reuse. SED-ML specifications are under development and implementations exist in libraries and tools.
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.
Study of Membrane Transport for Protein Filtration Using Artificial Neural Ne...IJERDJOURNAL
ABSTRACT: Artificial Neural Networks (ANNs) are nonlinear mapping structures which functions same as human brain. Modeling can be made stronger especially while the underlying data relationship is not known. ANNs may recognize and learn inter-related patterns between input data sets and related target values. After training, ANNs may be utilized to judge the output of new independent input data. Thus ANNs are used best for the modeling of membrane processes, like ultra filtration and microfiltration. This allows us to judge the permeate flux and membrane rejection as functions of process variables. The aim is modeling of membrane transport for protein filtration is to analyze membrane systems by means of ANNs. To analyze this different ANNs are developed with the help of Mat lab. [1a][9]
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
This document discusses improving reproducibility of simulation studies in computational biology through better management of simulation models and data. The SEMS project aims to develop standards and tools to link related data such as publications, models, simulations, results and more. This will be achieved by using graph databases and COMBINE standards to integrate data from various repositories. Tools will be created to search, compare, cluster and visualize models and their evolution over time to enable more reproducible and reusable simulation studies.
The document is a collection of passages from Domenique Whitmore's senior class speech and biography. It discusses her family and upbringing, experiences with dancing and basketball, friendships, relationship, and gratitude for her parents as she prepares to attend college on a basketball scholarship.
This document discusses three approaches to integrating model-related data in computational biology:
1) The COMBINE archive which bundles all model data into a single zip file for easy distribution.
2) Using a graph database (MORRE) to manage existing model data by representing it as a network of interrelated nodes that can be queried using information retrieval techniques.
3) Integrating model data into the semantic web and linked open data through BIO2RDF to enable automated reasoning and linking to other biological knowledge bases.
This document discusses challenges in modeling reproducibility, dissemination, and management. It notes that researchers struggle with data management. Standards are needed for reproducible modeling results, including models, annotations, and protocols. Models should be disseminated through public repositories for higher visibility, long-term availability, and quality checks. Management of models and related data can be improved through integration into graph databases linked to ontologies, as well as version control systems. The SEMS projects aim to address these issues to foster dissemination, ensure reproducibility, and improve management of computational models.
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.
Study of Membrane Transport for Protein Filtration Using Artificial Neural Ne...IJERDJOURNAL
ABSTRACT: Artificial Neural Networks (ANNs) are nonlinear mapping structures which functions same as human brain. Modeling can be made stronger especially while the underlying data relationship is not known. ANNs may recognize and learn inter-related patterns between input data sets and related target values. After training, ANNs may be utilized to judge the output of new independent input data. Thus ANNs are used best for the modeling of membrane processes, like ultra filtration and microfiltration. This allows us to judge the permeate flux and membrane rejection as functions of process variables. The aim is modeling of membrane transport for protein filtration is to analyze membrane systems by means of ANNs. To analyze this different ANNs are developed with the help of Mat lab. [1a][9]
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
This document discusses improving reproducibility of simulation studies in computational biology through better management of simulation models and data. The SEMS project aims to develop standards and tools to link related data such as publications, models, simulations, results and more. This will be achieved by using graph databases and COMBINE standards to integrate data from various repositories. Tools will be created to search, compare, cluster and visualize models and their evolution over time to enable more reproducible and reusable simulation studies.
The document is a collection of passages from Domenique Whitmore's senior class speech and biography. It discusses her family and upbringing, experiences with dancing and basketball, friendships, relationship, and gratitude for her parents as she prepares to attend college on a basketball scholarship.
This document discusses three approaches to integrating model-related data in computational biology:
1) The COMBINE archive which bundles all model data into a single zip file for easy distribution.
2) Using a graph database (MORRE) to manage existing model data by representing it as a network of interrelated nodes that can be queried using information retrieval techniques.
3) Integrating model data into the semantic web and linked open data through BIO2RDF to enable automated reasoning and linking to other biological knowledge bases.
This document discusses challenges in modeling reproducibility, dissemination, and management. It notes that researchers struggle with data management. Standards are needed for reproducible modeling results, including models, annotations, and protocols. Models should be disseminated through public repositories for higher visibility, long-term availability, and quality checks. Management of models and related data can be improved through integration into graph databases linked to ontologies, as well as version control systems. The SEMS projects aim to address these issues to foster dissemination, ensure reproducibility, and improve management of computational models.
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.
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.
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.
Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM
Written and presented by Dagmar Waltemath (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
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.
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.
MIRIAM (Minimum Information Requested in the Annotation of biochemical Models) is a standard for encoding and annotating quantitative models to unambiguously identify each model constituent. MIRIAM Resources provides tools to generate and resolve MIRIAM Uniform Resource Names (URNs) for cross-referencing different data types and model annotations. These include resources like BioModels Database and data types like UniProt, Gene Ontology, and EC codes. Software tools and databases are working to support MIRIAM standards to improve sharing and reuse of quantitative models.
USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEYcseij
This document summarizes research on using ontologies to overcome drawbacks of databases and vice versa. It discusses how ontologies can be used to store and manage large numbers of database instances to improve performance. It also explains how databases can help address issues with ontologies, such as a lack of semantics, by providing structured storage. The document reviews drawbacks of both databases and ontologies and how each can help address limitations of the other through integration. This mutual benefit is an active area of research at the intersection of databases and ontologies.
Model Management in Systems Biology: Challenges – Approaches – SolutionsMartin Scharm
I gave this talk as a webinar in the FAIRDOM webinar series 2016. The recordings of the webinar are available from http://fair-dom.org/knowledgehub/webinars-2/martin-scharm/
Metadata provides machine-readable descriptions of learning objects to create searchable indexes and catalogs. Common metadata standards like Dublin Core and IEEE Learning Object Metadata aim to promote interoperability across systems. While metadata standards define general information models and relationships, they need to be customized into metadata application profiles tailored to specific communities through controlled vocabularies and technical bindings like XML.
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.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
The document summarizes activities of POPIX and DDMoRe related to population modelling and the clinical trial simulator. Key points include:
1) POPIX develops new population modelling methods in fields like pharmacology while partnering with DDMoRe on PK/PD modelling.
2) DDMoRe aims to establish modelling standards and shares disease models through its modelling library and framework.
3) POPIX works on flexible statistical models, Bayesian estimation, errors in design/covariates, hidden Markov models, and stochastic differential equation models.
4) The clinical trial simulator can simulate trials using various PKPD models and recruitment/compliance models, and integrate workflows for simulation and analysis.
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.
Over time, Machine Learning inference workloads became more and more demanding in terms of latency and throughput, with multiple models being deployed in the system. This scenario provides large rooms for optimizations of runtime and memory, which current systems fall short in exploring because they employ a black-box model of ML models and tasks.
On the opposite side, Pretzel adopts a white-box description of ML models, which allows the framework to perform optimizations over deployed models and running tasks, saving memory and increasing the overall system performance. In this talk we will show the motivations behind Pretzel, its current design and possible future developments.
This document discusses using molecular visualization software like RasMol, Chime, and Jmol to display and interact with 3D molecular structures in online courses. It provides a history of these tools and how they have evolved from requiring expensive computers to run to being accessible through web browsers and platforms like Moodle. The document also outlines ways Jmol has been integrated into Moodle through filters and resources, and ideas to improve these integrations by adding more features and sharing molecular structures and visualizations between courses.
This document discusses using molecular visualization software like RasMol, Chime, and Jmol to display and interact with 3D molecular structures in online courses. It provides a history of these tools and how they have evolved from requiring expensive computers to run to being accessible through web browsers using Java applets. The document also describes integrating Jmol into Moodle courses through filters and custom resources, and outlines ideas for sharing structured molecular data and learning objects between courses.
Model-based Regression Testing of Autonomous RobotsZoltan Micskei
Slides for the paper in 18th Int. Conf. on System Design Languages (SDL 2017). We present a method and a case study on how model-based regression testing can be achieved in the context of autonomous robots. The method uses information from several domain-specific languages for modeling the robot’s context and configuration. Our approach is implemented in a prototype tool, and its scalability is evaluated on models from the case study.
This document provides an overview of the Computational Modeling in Biology Network (COMBINE) which coordinates the standardization of data and models in computational biology. It describes COMBINE's role in developing standards for encoding models (SBML), visualizing models (SBGN), and simulating models (SED-ML). The document also discusses COMBINE's guidance on publishing models according to FAIR principles, developing software tools and libraries to support the standards, and establishing best practices through documentation and training resources.
Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
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.
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.
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.
Reproducibility of model-based results: standards, infrastructure, and recogn...FAIRDOM
Written and presented by Dagmar Waltemath (University of Rostock) as part of the Reproducible and Citable Data and Models Workshop in Warnemünde, Germany. September 14th - 16th 2015.
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.
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.
MIRIAM (Minimum Information Requested in the Annotation of biochemical Models) is a standard for encoding and annotating quantitative models to unambiguously identify each model constituent. MIRIAM Resources provides tools to generate and resolve MIRIAM Uniform Resource Names (URNs) for cross-referencing different data types and model annotations. These include resources like BioModels Database and data types like UniProt, Gene Ontology, and EC codes. Software tools and databases are working to support MIRIAM standards to improve sharing and reuse of quantitative models.
USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEYcseij
This document summarizes research on using ontologies to overcome drawbacks of databases and vice versa. It discusses how ontologies can be used to store and manage large numbers of database instances to improve performance. It also explains how databases can help address issues with ontologies, such as a lack of semantics, by providing structured storage. The document reviews drawbacks of both databases and ontologies and how each can help address limitations of the other through integration. This mutual benefit is an active area of research at the intersection of databases and ontologies.
Model Management in Systems Biology: Challenges – Approaches – SolutionsMartin Scharm
I gave this talk as a webinar in the FAIRDOM webinar series 2016. The recordings of the webinar are available from http://fair-dom.org/knowledgehub/webinars-2/martin-scharm/
Metadata provides machine-readable descriptions of learning objects to create searchable indexes and catalogs. Common metadata standards like Dublin Core and IEEE Learning Object Metadata aim to promote interoperability across systems. While metadata standards define general information models and relationships, they need to be customized into metadata application profiles tailored to specific communities through controlled vocabularies and technical bindings like XML.
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.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
The document summarizes activities of POPIX and DDMoRe related to population modelling and the clinical trial simulator. Key points include:
1) POPIX develops new population modelling methods in fields like pharmacology while partnering with DDMoRe on PK/PD modelling.
2) DDMoRe aims to establish modelling standards and shares disease models through its modelling library and framework.
3) POPIX works on flexible statistical models, Bayesian estimation, errors in design/covariates, hidden Markov models, and stochastic differential equation models.
4) The clinical trial simulator can simulate trials using various PKPD models and recruitment/compliance models, and integrate workflows for simulation and analysis.
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.
Over time, Machine Learning inference workloads became more and more demanding in terms of latency and throughput, with multiple models being deployed in the system. This scenario provides large rooms for optimizations of runtime and memory, which current systems fall short in exploring because they employ a black-box model of ML models and tasks.
On the opposite side, Pretzel adopts a white-box description of ML models, which allows the framework to perform optimizations over deployed models and running tasks, saving memory and increasing the overall system performance. In this talk we will show the motivations behind Pretzel, its current design and possible future developments.
This document discusses using molecular visualization software like RasMol, Chime, and Jmol to display and interact with 3D molecular structures in online courses. It provides a history of these tools and how they have evolved from requiring expensive computers to run to being accessible through web browsers and platforms like Moodle. The document also outlines ways Jmol has been integrated into Moodle through filters and resources, and ideas to improve these integrations by adding more features and sharing molecular structures and visualizations between courses.
This document discusses using molecular visualization software like RasMol, Chime, and Jmol to display and interact with 3D molecular structures in online courses. It provides a history of these tools and how they have evolved from requiring expensive computers to run to being accessible through web browsers using Java applets. The document also describes integrating Jmol into Moodle courses through filters and custom resources, and outlines ideas for sharing structured molecular data and learning objects between courses.
Model-based Regression Testing of Autonomous RobotsZoltan Micskei
Slides for the paper in 18th Int. Conf. on System Design Languages (SDL 2017). We present a method and a case study on how model-based regression testing can be achieved in the context of autonomous robots. The method uses information from several domain-specific languages for modeling the robot’s context and configuration. Our approach is implemented in a prototype tool, and its scalability is evaluated on models from the case study.
This document provides an overview of the Computational Modeling in Biology Network (COMBINE) which coordinates the standardization of data and models in computational biology. It describes COMBINE's role in developing standards for encoding models (SBML), visualizing models (SBGN), and simulating models (SED-ML). The document also discusses COMBINE's guidance on publishing models according to FAIR principles, developing software tools and libraries to support the standards, and establishing best practices through documentation and training resources.
Introduction to FAIR principles in the context of computational biology models. Presented at a Workshop at the Basel Conference of Computational Biology. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This talk was part of the 2020 Disease Map Modeling Community meeting, covering the steps towards publishing reproducible simulation studies (based on a reused model). Links to different COMBINE guidelines, tutorials and efforts. Grants: European Commission: EOSCsecretariat.eu - EOSCsecretariat.eu (831644)
This document provides an overview of standards and best practices for making computational models reusable through the use of model repositories and standard formats. It discusses the COMBINE initiative for standardizing the encoding of models and simulations. The document encourages authors to make their models and data FAIR (Findable, Accessible, Interoperable, Reusable) by using community standards for publishing, exchanging, and archiving models. Examples of open model repositories and standards-compliant tools and libraries are provided to demonstrate how authors can improve sharing and reuse of their models.
Presentation on how to enable model reuse in systems biology. Presented as part of the series "Führende Köpfe in der IT - Wissenschaftlerinnen im Dialog" (ZB Med, Bonn, Germany)
This document summarizes work using Neo4j graph databases for computational systems biology models. It discusses:
1) Projects using Neo4j to integrate storage of models and simulation studies, enable ranked retrieval, and identify frequent patterns in models.
2) Tools developed including MASYMOS for linking models, simulations, annotations via graph structures, and STON for converting SBGN maps to Neo4j.
3) Applications including model repositories, analysis tools, and identifying common reaction motifs in models.
This document summarizes Dagmar Waltemath's presentation on model management for systems biology projects. It discusses the need for effective data management strategies due to the large, complex, and heterogeneous nature of systems biology data. It recommends using a data management plan, dedicated model management systems like FAIRDOMHub, standards for sharing data, publishing models in repositories, ensuring model quality, and tracking provenance. The goal is to make studies reproducible, valuable, and sustainable.
This document discusses challenges to reproducibility in systems biology and potential solutions. It notes a lack of data standards, quality, availability, and transparency make it difficult for researchers to reproduce results. Tools and initiatives discussed that aim to improve reproducibility include the COMBINE archive to bundle necessary files, graph databases to integrate model-related data, and version control systems to track model evolution over time. The overall goal is to better support scientists in sharing reproducible model-based studies.
This document discusses SED-ML (Simulation Experiment Description Markup Language), a standard for describing computational simulations. SED-ML files contain information like the models, data, simulation settings and algorithms used in an experiment. Using SED-ML allows experiments to be reproduced and shared. The document encourages adopting SED-ML to make research more reproducible and help curation of models in repositories. It also provides an overview of tools that support SED-ML and ways to get involved in its development.
This document discusses data and model management in systems biology. It covers topics such as data ownership, metadata, ontologies, standards for encoding models and analyses, and tools for working with systems biology models and data. Standards like SBML, SBGN, SED-ML and COMBINE Archive allow for structured representation, visualization, simulation, and sharing of models and data. Resources like SEEK enable curation, simulation and publication of models in a findable, accessible, interoperable and reusable (FAIR) manner.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
Introduction to the hands on session on "Standards and tools for model management" at the ICSB 2015.
Focus on COMBINE standards, tools for search, version control and archiving. Used management platform is SEEK.
These are the slides from COMBINE 2015. In this talk, I presented the different approaches we take to determine the similarity between simulation models encoded in SBML or CeLLML -- namely: Information Retrieval based ranked model retrieval; annotation-based feature extraction for sets of models; and structure-based similarity search and clustering of model sets.
The document summarizes the work of the SBGN-ED+ project, which aims to further develop and integrate the Systems Biology Graphical Notation (SBGN) for modeling biological networks. Some key goals of the project include contributing to the SBGN specification and library, implementing SBGN support for model version control and merging in software tools like SBGN-ED, and using SBGN maps to display differences between model versions. The project also seeks to incorporate SBGN maps into model search, comparison and integration of model-related data. This would help address the need for standardized visual representations of biological networks to reduce ambiguity and enable sharing of computational models.
Some slides put together on analogies between biosamples and model samples. Prepared for the Biosamples workshop at The University of Manchester, 17th June 2015.
Talk in the research seminar of the Systems Biology group at the University of Rostock. The goal was to introduce the two new projects running in SEMS from summer 2015: The de.NBI-SYSBIO German Network for Bioinformatics infrastructure (focus: systems biology data management) and SBGN-ED (support and further development of SBGN-ED and libSBGN).
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Fix the Import Error in the Odoo 17Celine George
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Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
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How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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Meta-Information for Bio-Models
1. Model Meta-Information
Dagmar Waltemath
Workshop on
Ontology in Modeling and Simulation
of Neuronal Systems
Rostock, 26th of May, 2010
Dagmar Waltemath Rostock, 2010
3. Model structure vs meta-information
B
0.9
0.2
2A C
0.4
• Model structure, e.g. SBML, CellML
– Encodes the network, e.g. of biochemical
reactions
– Necessary mathematical information for
simulation/execution of a model
Dagmar Waltemath Rostock, 2010
4. Model structure vs meta-information
B
0.9
0.2
2A C
0.4
• Models not only are one-time encodings of the
mathematics of a biological system
– Model reuse (expansion, teaching, collaborations …)
– Model search & browsing
– Model visualisation
– Model merging ...
Dagmar Waltemath Rostock, 2010
5. Model structure vs meta-information
B
[SBO] 0.9 [Uniprot]
Protein complex 0.2 Tetracycline
[UniProt] repressor
[GO] 2A C Lactose protein
translation Operon
process 0.4 Repressor
• Model meta-information helps “understanding” the
model
– MIRIAM (Minimum Information Requested in the
Annotation of Models)
– Use of controlled annotation, particularly ontologies,
including Gene Ontology, Systems Biology Ontology,
UniProt, CheBi ...
Dagmar Waltemath Rostock, 2010
6. Model meta-information encoding
MIRIAM standard on MIRIAM resources
•
Makes meta-information computer-processable
•
Ensures permanent links to information and knowledge
•
http://www.ebi.ac.uk/miriam/main/ and http://www.biomodels.net/qualifiers/
Dagmar Waltemath Rostock, 2010
7. Possible types of meta-information
Reaction:
Organism:
degradation of TetR transcripts
(GO:0006402) E-Coli (UniProt:562)
Compartment:
Cell (GO:0005623)
Publication:
pubmed:10659856
Format: SBML
(SED-ML:type=”SBML”)
Species:
transcript Lactose operon
repressor (UniProtKB:P03023),
is versionOf
mRNA (CHEBI:33699),
located in the cell (GO:0005623) Behavior: Oscillation (TEDDY_0000006)
SimulationAlgorithm: Gillespie (KiSAO:000029)
Dagmar Waltemath Rostock, 2010
8. Biomodels.net initiative
Minimum Information Requested In the
Annotation of Models (MIRIAM)
• Systems Biology Ontology (SBO)
Minimum Information About a Simulation
• Experiment (MIASE)
• Simulation Experiment Description Markup
Language (SED-ML)
• Kinetic Simulation Algorithm
Ontology (KiSAO)
• Terminology for the Description of
Dynamics (TEDDY)
•
http://www.biomodels.net
9. Summary
• Use cases and software for model annotation
→ follow-up presentation Ron Henkel
• Further information on model meta-information
– Metadata For Systems Biology, Juty (2009)
http://videolectures.net/mlsb09_juty_mfsb/
– Minimum information requested in the annotation of biochemical
models (MIRIAM), Le Novère, Finney, Hucka et al. , Nature (2006)
http://www.nature.com/nbt/journal/v23/n12/abs/nbt1156.html
Dagmar Waltemath Rostock, 2010
10. Part 2: Simulation experiment descriptions
Dagmar Waltemath Rostock, 2010
11. Part 2: Simulation experiment descriptions
SED-ML: A format proposal for the
storage and exchange of
simulation experiments
(as one particular type of meta-information)
Dagmar Waltemath Rostock, 2010
12. Motivation
Biological
publication find according Model
repository publication database
load model
read
Simulation
tool
apply model simulation
changes result
Dagmar Waltemath Rostock, 2010
13. SED-ML
• Simulation Experiment Description Markup
Language
– Community project since 2007
– XML Format / XML Schema / UML Object model
– Main parts:
• Pre-processing
• Model references
• Simulation settings
• Post-processing
Dagmar Waltemath Rostock, 2010
14. SED-ML
• Model
• Simulation
• Task
• DataGenerator
• Output
(Figure by Frank Bergmann, biomodels.net 2010)
Dagmar Waltemath Rostock, 2010
15. SED-ML
Biological
publication find according Model
repository publication database
load model
read
Simulation
tool
apply model simulation
changes result
Dagmar Waltemath Rostock, 2010
16. SED-ML
Biological
publication find according Model
repository publication database SED-ML
load model
export & store
read SED-ML
Simulation
tool
apply model simulation
changes result
Dagmar Waltemath Rostock, 2010
17. SED-ML
load model(s)
call SED-ML Model
file
SED-ML database
apply
model changes
run
simulation(s)
Simulation
tool
simulation result
Dagmar Waltemath Rostock, 2010
18. SED-ML Specification & Implementation
• SED-ML L1 V1 Specification
– under development
– preliminary version available
from Sourceforge
• SED-ML Implementation
– Libsedml & examples
– Jlibsedml
– SED-ML validator
Dagmar Waltemath Rostock, 2010
19. Use Cases
• Storage simulation experiment
– independently from a simulation tool
– in a reusable and exchangeable manner
• Import simulation experiment
– collaborative work
– teaching
– curation
• Simulation using several models
– in different formats → coupling?
• Simulation experiment using different settings
Dagmar Waltemath Rostock, 2010
20. Example
“I normally use Copasi but most of the time it shows errors and/or warnings when I tried to
import SBML models in it. For an example in Biomodel database the model
BIOMD0000000139 and BIOMD0000000140 are two different models and they are
supposed to show different results. Unfortunately simulating them in Copasi gives same
result for both the models. Moreover different versions and curated model also cause
problem. “ (arvin mer on sbml-discuss)
(Figures produced by Frank Bergmann in SBW Workbench)
Dagmar Waltemath Rostock, 2010
21. Summary
• Community
– Nicolas Le Novère (EBI)
– Frank Bergmann (SBW Workbench, libsedml)
– Richard Adams (SED-ML validator, jlibsedml)
– Ion Moraru (Virtual Cell)
– …
• Further Information
– http://sourceforge.net/projects/sed-ml/
– http://biomodels.net/sed-ml
• Getting involved
– sed-ml-discuss@lists.sourceforge.net
Dagmar Waltemath Rostock, 2010
23. Information in the SBML model
• 1 compartment
• 1 standard species
• No reactions
• 8 global quantities (parameters)
• 2 rate rules
• 2 events
Dagmar Waltemath Rostock, 2010
24. Information in the model annotation
Model reference urn:miriam:biomodels.db:BIOMD0000000127
•
Publication reference urn:miriam:pubmed:18244602
•
Model is on organism mammals urn:miriam:taxonomy:40674
•
Compartment is version of a cellular compartment urn:miriam:obo.go:GO
•
%3A0005623
Has a standard species not annotated in the model
•
Encodes 2 rate rules: the regulation of membrane potential (variable v)
•
urn:miriam:obo.go:GO%3A0042391, the positive regulation of potassium ion
transport (variable U) urn:miriam:obo.go:GO%3A0043268
No reactions
•
8 global quantities (parameters) not annotated in the SBML model
•
Has 2 events: a version of the stabilization of membrane potential (event
•
event_0000001) urn:miriam:obo.go:GO%3A0030322, and the detection of
electrical stimulus (event Stimulus) urn:miriam:obo.go:GO%3A0050981
Dagmar Waltemath Rostock, 2010
25. Information in the SED-ML file
• First tries (COPASI, time course on v,
initial parametrisation)
1 ms 100ms 1000ms
Dagmar Waltemath Rostock, 2010
26. Information in the SED-ML file
• Adjusting simulation step size and duration
publication COPASI, duration: 140ms, step size: 0.14
Dagmar Waltemath Rostock, 2010
27. Information in the SED-ML file
• Updating initial model parameters
Publication COPASI, adjusted parameter values
(a=0.02, b=0.2 c=-55, d=4)
Dagmar Waltemath Rostock, 2010
28. Thank you for your attention!
dagmar.waltemath@uni-rostock.de
sed-ml-discuss@lists.sourceforge.net
Dagmar Waltemath Rostock, 2010