The document discusses various approaches for visualizing metabolic models and networks. It describes how metabolic models are complex mathematical representations of biochemical reactions in cells. It outlines challenges in visualizing large genome-scale models and introduces the concept of zooming user interfaces that allow semantic decomposition of models into modular views at different levels of detail. The document highlights Mimoza, a web-based tool that provides automated multi-level semantic zooming of metabolic models.
The document summarizes TAIR, a resource for Arabidopsis thaliana genome annotation, and its transition to institutional subscription funding after losing grant funding. It provides statistics on TAIR's curation of experimental gene function data from publications. Over the past 10 months, TAIR curated annotations for 940 new genes from 3,835 new publications. It promotes TAIR's value in its long history of high-quality, reliable data integration and community involvement. TAIR is working with other resources like AIP to provide more comprehensive plant genomics data.
Wikidata workshop for ISB Biocuration 2016Benjamin Good
This document discusses using Wikidata as a platform for biocuration. Wikidata is presented as a new paradigm that could reduce pain points in current biocuration practices by providing a single platform with persistent data access. It describes Wikidata's structure as a knowledge base of unique items and statements linked together to form a knowledge graph. Examples show how biomedical data like genes and proteins are represented. The document outlines Wikidata's community processes and increasing impact on applications like Wikipedia and genome browsers. It envisions the potential for researchers to contribute new biomedical knowledge through Wikidata.
ER&L 2016 Using the scrum project management methodology to create a comprehe...Galadriel Chilton
Commonly used in software development, Scrum is a simple and practical project management methodology. This presentation will demonstrate how using Scrum to develop a framework for a comprehensive review of e-resource collections kept the project on task, strengthened the project's deliverables, increased team morale, and resulted in equitable task distribution.
Project report: Investigating the effect of cellular objectives on genome-sca...Jarle Pahr
Report from a half-semester master-level project carried out at the department of biotechnology, Norwegian University of Science and Technology. Describes a MATLAB-based framework for comparing experimental metabolic flux data with model predictions and evaluating objective functions.
Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks
Cornell Medical School, Physiology, Biophysics and Systems Biology (PBSB) graduate program, 2009.01.26, 16:00-17:00; [I:CORNELL-PBSB] (Long networks talk, incl. the following topics: why networks w. amsci*, funnygene*, net. prediction intro, memint*, tse*, essen*, sandy*, metagenomics*, netpossel*, tyna*+ topnet*, & pubnet* . Fits easily into 60’ w. 10’ questions. PPT works on mac & PC and has many photos w. EXIF tag kwcornellpbsb .)
Date Given: 01/26/2009
Randomizing genome-scale metabolic networksAreejit Samal
The document proposes a new Markov Chain Monte Carlo (MCMC) based method to generate randomized metabolic networks that impose biochemical and functional constraints. The method successively constrains the networks by (1) fixing the number of reactions, (2) fixing the number of metabolites, (3) excluding blocked reactions, and (4) requiring growth on a specified environment. Imposing these constraints causes the randomized networks to more closely match properties of real metabolic networks like E. coli. The approach generates an ensemble of diverse yet meaningful randomized networks to help identify design principles in metabolic networks.
ChEBI and genome scale metabolic reconstructionsNeil Swainston
1. Genome-scale metabolic reconstructions are network maps of metabolism that are computer-readable and can be used for metabolic engineering, biological discovery, and predicting phenotypic behavior.
2. Standards like SBML and MIRIAM allow sharing, comparing and merging models by providing a common language and requiring annotation of model constituents to external databases.
3. ChEBI is commonly used for annotating metabolites in models due to its comprehensive coverage, fast search capabilities, and web services that enable automation. This allows relating experimental data to models and developing tools for tasks like reaction balancing.
Predictive modelling of cancer through metabolic networksBhavitha Pulaparthi
1) Genome-scale metabolic network reconstruction allows for predictive modeling of cancer metabolism through metabolic networks.
2) By constructing cell-specific metabolic networks for cancerous cells using systems biology approaches like genome-scale modeling, the metabolic phenotypes of cancers can be determined.
3) Case studies have used constraint-based modeling techniques like flux balance analysis on reconstructed human metabolic networks to predict cancer-specific drug targets and metabolic liabilities by integrating omics data from cancer datasets.
The document summarizes TAIR, a resource for Arabidopsis thaliana genome annotation, and its transition to institutional subscription funding after losing grant funding. It provides statistics on TAIR's curation of experimental gene function data from publications. Over the past 10 months, TAIR curated annotations for 940 new genes from 3,835 new publications. It promotes TAIR's value in its long history of high-quality, reliable data integration and community involvement. TAIR is working with other resources like AIP to provide more comprehensive plant genomics data.
Wikidata workshop for ISB Biocuration 2016Benjamin Good
This document discusses using Wikidata as a platform for biocuration. Wikidata is presented as a new paradigm that could reduce pain points in current biocuration practices by providing a single platform with persistent data access. It describes Wikidata's structure as a knowledge base of unique items and statements linked together to form a knowledge graph. Examples show how biomedical data like genes and proteins are represented. The document outlines Wikidata's community processes and increasing impact on applications like Wikipedia and genome browsers. It envisions the potential for researchers to contribute new biomedical knowledge through Wikidata.
ER&L 2016 Using the scrum project management methodology to create a comprehe...Galadriel Chilton
Commonly used in software development, Scrum is a simple and practical project management methodology. This presentation will demonstrate how using Scrum to develop a framework for a comprehensive review of e-resource collections kept the project on task, strengthened the project's deliverables, increased team morale, and resulted in equitable task distribution.
Project report: Investigating the effect of cellular objectives on genome-sca...Jarle Pahr
Report from a half-semester master-level project carried out at the department of biotechnology, Norwegian University of Science and Technology. Describes a MATLAB-based framework for comparing experimental metabolic flux data with model predictions and evaluating objective functions.
Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks
Cornell Medical School, Physiology, Biophysics and Systems Biology (PBSB) graduate program, 2009.01.26, 16:00-17:00; [I:CORNELL-PBSB] (Long networks talk, incl. the following topics: why networks w. amsci*, funnygene*, net. prediction intro, memint*, tse*, essen*, sandy*, metagenomics*, netpossel*, tyna*+ topnet*, & pubnet* . Fits easily into 60’ w. 10’ questions. PPT works on mac & PC and has many photos w. EXIF tag kwcornellpbsb .)
Date Given: 01/26/2009
Randomizing genome-scale metabolic networksAreejit Samal
The document proposes a new Markov Chain Monte Carlo (MCMC) based method to generate randomized metabolic networks that impose biochemical and functional constraints. The method successively constrains the networks by (1) fixing the number of reactions, (2) fixing the number of metabolites, (3) excluding blocked reactions, and (4) requiring growth on a specified environment. Imposing these constraints causes the randomized networks to more closely match properties of real metabolic networks like E. coli. The approach generates an ensemble of diverse yet meaningful randomized networks to help identify design principles in metabolic networks.
ChEBI and genome scale metabolic reconstructionsNeil Swainston
1. Genome-scale metabolic reconstructions are network maps of metabolism that are computer-readable and can be used for metabolic engineering, biological discovery, and predicting phenotypic behavior.
2. Standards like SBML and MIRIAM allow sharing, comparing and merging models by providing a common language and requiring annotation of model constituents to external databases.
3. ChEBI is commonly used for annotating metabolites in models due to its comprehensive coverage, fast search capabilities, and web services that enable automation. This allows relating experimental data to models and developing tools for tasks like reaction balancing.
Predictive modelling of cancer through metabolic networksBhavitha Pulaparthi
1) Genome-scale metabolic network reconstruction allows for predictive modeling of cancer metabolism through metabolic networks.
2) By constructing cell-specific metabolic networks for cancerous cells using systems biology approaches like genome-scale modeling, the metabolic phenotypes of cancers can be determined.
3) Case studies have used constraint-based modeling techniques like flux balance analysis on reconstructed human metabolic networks to predict cancer-specific drug targets and metabolic liabilities by integrating omics data from cancer datasets.
Practices for drawing biological networks using the SBGN standardVasundra Touré
Presented at the University of Rostock research seminar the 25th July 2016.
Abstract:
In Systems Biology, the visualization of data as networks support researchers in analyzing and understanding the biological system under study. Since 2009, creating an easy-to-understand and exchangeable network is possible using a standard called SBGN, the Systems Biology Graphical Notation. In this talk, I will show some good practices for generating biological networks using SBGN. After a short presentation of the Systems Biology Graphical Notation, I will give a demo on drawing SBGN maps using the SBGN-ED software.
Large-scale generation of mathematical models from biological pathways
This document discusses the large-scale generation of mathematical models from biological pathway data. Pathway data from databases are converted into models using standardized formats and modeling languages. Over 100,000 models of metabolic networks and 27,000 models of signaling pathways have been generated in SBML format. These models provide a starting point for systems-level modeling and simulation of biochemical pathways across many species. The workflow involves translating pathways into activity flows, logical models, and flux balance models using common modeling approaches and rate laws.
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
Keynote presentation at the iConference 2015, Newport Beach, Los Angeles, 26 March 2015.
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
http://ischools.org/the-iconference/
BEWARE: presentation includes hidden slides AND in situ build animations - best viewed by downloading.
Open Notebook Science refers to making all aspects of scientific research fully transparent and accessible online. This includes publishing lab notebooks, experimental procedures and results in open wikis and databases. The benefits are increased collaboration, accessibility of information for other researchers, and opportunities for broader impact. However, it also means loss of some control over the research process and timeline of publishing results.
Phylogeny-driven approaches to microbial & microbiome studies: talk by Jonath...Jonathan Eisen
This document summarizes an automated phylogenetic tree-based small subunit rRNA taxonomy and alignment pipeline (STAP) developed by the authors. STAP generates high-quality multiple sequence alignments and phylogenetic trees from rRNA gene sequences in a fully automated manner, allowing for phylogenetic analysis of large datasets. It combines existing tools like BLAST, CLUSTALW and PHYML with new programs for automated alignment, masking, and tree parsing. STAP yields results comparable to manual analysis but with increased speed and capacity needed to analyze the large volumes of rRNA data now being generated.
The document is a presentation on writing scientific papers. It discusses the structure and components of an introduction section. An effective introduction (1) presents the research field and importance, (2) identifies gaps, questions or limitations in current understanding, and (3) discusses the state of the art in recent research findings to provide context for the study. The purpose is to establish why the present study is important and timely.
The document is a presentation on writing scientific papers. It discusses the structure and components of an introduction section. An effective introduction (1) presents the research field and importance, (2) identifies gaps, questions or limitations in current understanding, and (3) discusses the state of the art in recent research findings to provide context for the study. The purpose is to establish why the present study is important and timely.
Shahid Manzoor has over 15 years of experience in bioinformatics. He holds a PhD in Bioinformatics from the Swedish University of Agricultural Sciences and has taught bioinformatics courses there. His research focuses on analyzing next generation sequencing data to study metabolic pathways and discover new correlations in large biological datasets. He has authored or co-authored over 15 publications and is currently a guest researcher at the Swedish University of Agricultural Sciences.
This paper reviews recent developments in the design of microfluidic concentration gradient generators for biological applications. It discusses how gradient generator designs leverage mass transport principles like diffusion and convection to control gradients. The review provides guidance on design considerations for different biological assays and summarizes factors to account for when using gradient generators. It also outlines perspectives on future improvements to gradient generator technology.
Artificial Neural Networks And Their Application In Biological And Agricultur...Deja Lewis
This document discusses artificial neural networks (ANNs) and their increasing application in biological and agricultural research. It provides background on ANNs, noting they are inspired by the human brain but can currently only partially mimic its functioning. The document outlines two major learning paradigms for ANNs - supervised and unsupervised learning. It also gives examples of ANN applications in plant biology, including for chlorophyll fluorescence data analysis where ANNs achieved 95% accuracy in plant species recognition.
This document provides a preface for a textbook on systems and control. It describes the scope and objectives of the textbook, which is to familiarize readers with dynamical system theory and provide tools for control system design. The textbook can be used for senior/graduate level courses in systems and control, introductory courses in nonlinear systems, or introductory courses in modern automatic control. It includes modeling of dynamical systems from various engineering and scientific disciplines. Analysis methods like Lyapunov stability theory and numerical techniques are discussed. Classical and modern control approaches like fuzzy and neural networks are presented in a unified manner.
The cellular cytoskeleton is essential in proper cell function as well as in organism development. These polymers provide the elaborate roads along which most intracellular protein transport occurs. I will discuss several examples where mathematical modeling, analysis, and simulation tools help us study and understand the interactions between these filaments roads and motor proteins in cells. In neurons, neurofilaments navigate axons and their constrictions to maintain a healthy speed of neuronal communication. We develop stochastic models that may provide insights into transport mechanisms through axonal constrictions. In the reproductive system of the worm C. elegans, we use agent - based modeling to study how myosin motors interact with actin filaments to maintain contractile rings that allow passage and nutrient transport for developing egg cells. In addition, we have recently become interested in using topological data analysis tools to assess maintenance and establishment of these ring structures.
36041 Topic SCI 207 Our Dependence upon the EnvironmentNumber.docxrhetttrevannion
36041 Topic: SCI 207 Our Dependence upon the Environment
Number of Pages: 2 (Double Spaced)
Number of sources: 2
Writing Style: APA
Type of document: Essay
Academic Level:Undergraduate
Category: Environmental Issues
Language Style: English (U.S.)
Order Instructions: Attached
Week 2 - Assignment 1
PLEASE NEED TO TAKE A PHOTO AND DO THE LAB, SEE BELOW THE TEMPLATE:
Properties of Soil, Agriculture and Water Availability Impacts Laboratory
[WLO: 2] [CLOs: 1, 3, 5]
This lab enables you to analyze the natural porosity and particle size of soil samples along with the chemical composition and profile of different soil types.
The Process:
Take the required photos and complete all parts of the assignment (calculations, data tables, etc.). On the “Lab Worksheet,” answer all of the questions in the “Lab Questions” section. Finally, transfer all of your answers and visual elements from the “Lab Worksheet” into the “Lab Report.” You will submit both the “Lab Report” and the “Lab Worksheet” to Waypoint.
The Assignment:
Make sure to complete all of the following items before submission:
Before you begin the assignment, read the Properties of Soil: Agricultural and Water Availability Impacts Investigation ManualPreview the document and review The Scientific Method (Links to an external site.)Links to an external site. presentation video.
Complete Activities 1 through 4 using materials in your kit, augmented by additional materials that you will supply. Photograph each activity following these instructions:
When taking lab photos, you need to include in each image a strip of paper with your name and the date clearly written on it.
Complete all parts of the Week 2 Lab WorksheetPreview the document, and answer all of the questions in the “Lab Questions” section.
Transfer your responses to the lab questions and the data tables and your photos from the “Lab Worksheet” into the “Lab Report” by downloading the Lab Report TemplatePreview the document.
Submit your completed “Lab Report” and “Lab Worksheet” through Waypoint.
Carefully review the Grading Rubric (Links to an external site.)Links to an external site. for the criteria that will be used to evaluate your assignment.
Name of Lab
Your Name
SCI 207: Our Dependence Upon the Environment
Instructor’s Name
Date
*This template will enable you to turn your lab question responses into a polished Lab Report. Simply copy paste your answers to the lab questions, as well as all data tables, graphs, and photographs, in the locations indicated. Before you submit your Lab Report, it is recommended that you run it through Turnitin, using the student folder, to ensure protection from accidental plagiarism. Please delete this purple text before submitting your report.
Name of Lab
Introduction
Copy and paste your response to Question One here.
Copy and paste your response to Question Two here.
Copy and paste your response to Question Three here.
Materials and Methods
Copy and paste y.
Learning to recommend with user generated contentYueshen Xu
This document discusses recommendation systems that incorporate user generated content (UGC) such as tags, reviews, questions/answers, blogs and tweets. It proposes two new matrix factorization-based recommendation models: 1) UTR-MF which regularizes user latent factors based on their interested topics learned from UGC, and 2) ITR-MF which regularizes item latent factors based on their topic distributions learned from associated UGC. The models are evaluated on three real-world datasets and are shown to outperform baselines by utilizing UGC to better learn user preferences and item features. Future work could explore incorporating other UGC types like tweets and blogs.
International Journal of Biometrics and Bioinformatics(IJBB) Volume (3) Issue...CSCJournals
This document summarizes a research paper that proposes a new crossover operator called Sequential Constructive Crossover (SCX) for solving the Traveling Salesman Problem (TSP) using a genetic algorithm. SCX constructs offspring from parent chromosomes by selecting better edges present in the parents while maintaining the node sequence. The performance of SCX is compared to other crossover operators like Edge Recombination Crossover and Generalized N-point Crossover on benchmark TSP instances, and experimental results show that SCX finds higher quality solutions than the other operators. The TSP is an NP-complete problem where the goal is to find the shortest route to visit all cities on a tour and return to the starting city. Genetic algorithms are
This document summarizes research on non-model ascidian species Molgula occulta and Molgula oculata. An international collaboration generated transcriptome data, sequenced the genomes of three Molgula species, and examined gene expression patterns related to tail development. Analysis revealed heterochronic shifts in developmental timing between tailed and tailless species. The data resources enabled further study of evolutionary shifts in gene regulatory networks underlying conserved developmental processes. The document emphasizes the importance of methods development for large-scale data analysis to enable new biological insights.
ICBO 2018 Poster - Current Development in the Evidence and Conclusion Ontolog...dolleyj
The Evidence & Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best
practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology (GO) term for a molecular function to a protein), the type of evidence
supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they
support. These include the Ontology for Microbial Phenotypes and GO. In addition, we continue to support user-level class requests through our GitHub issue tracker. To
facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow.
ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is
also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical
definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open
Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as
the ECO web page (http://evidenceontology.org/).
Genome-Scale Metabolic Models and Systems Medicine of Metabolic SyndromeNatal van Riel
workshop on 'The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes', December 12 and 13, 2013, Eindhoven University of Technology
The document provides an introduction to phylogenetics and phylogenetic trees. It defines a phylogenetic tree as representing the evolutionary relationships among various biological taxa based on similarities and differences in their genetic or physical traits, with taxa more closely related shown closer together on the tree. It discusses how phylogenetic trees are reconstructed from genetic sequence alignments using either character-based or distance-based methods, and how the trees can be used to study organismal evolution and disease transmission patterns.
Introduction to phylogenetics from a mathematical point of view. Distance met...Anna Zhukova
This document introduces phylogenetic trees and bipartitions. It defines the key parts of phylogenetic trees, including roots, branches, inner nodes, and tips. It explains how to determine if a tree is rooted or unrooted, resolved or unresolved. Bipartitions represent splits between taxa on a tree. The bipartition set of a tree includes all possible splits. The bipartition distance between two trees is the number of differing bipartitions in their bipartition sets. This metric is equivalent to the Robinson-Foulds topological distance, which measures the minimum number of operations needed to transform one tree into another.
Practices for drawing biological networks using the SBGN standardVasundra Touré
Presented at the University of Rostock research seminar the 25th July 2016.
Abstract:
In Systems Biology, the visualization of data as networks support researchers in analyzing and understanding the biological system under study. Since 2009, creating an easy-to-understand and exchangeable network is possible using a standard called SBGN, the Systems Biology Graphical Notation. In this talk, I will show some good practices for generating biological networks using SBGN. After a short presentation of the Systems Biology Graphical Notation, I will give a demo on drawing SBGN maps using the SBGN-ED software.
Large-scale generation of mathematical models from biological pathways
This document discusses the large-scale generation of mathematical models from biological pathway data. Pathway data from databases are converted into models using standardized formats and modeling languages. Over 100,000 models of metabolic networks and 27,000 models of signaling pathways have been generated in SBML format. These models provide a starting point for systems-level modeling and simulation of biochemical pathways across many species. The workflow involves translating pathways into activity flows, logical models, and flux balance models using common modeling approaches and rate laws.
Results Vary: The Pragmatics of Reproducibility and Research Object FrameworksCarole Goble
Keynote presentation at the iConference 2015, Newport Beach, Los Angeles, 26 March 2015.
Results Vary: The Pragmatics of Reproducibility and Research Object Frameworks
http://ischools.org/the-iconference/
BEWARE: presentation includes hidden slides AND in situ build animations - best viewed by downloading.
Open Notebook Science refers to making all aspects of scientific research fully transparent and accessible online. This includes publishing lab notebooks, experimental procedures and results in open wikis and databases. The benefits are increased collaboration, accessibility of information for other researchers, and opportunities for broader impact. However, it also means loss of some control over the research process and timeline of publishing results.
Phylogeny-driven approaches to microbial & microbiome studies: talk by Jonath...Jonathan Eisen
This document summarizes an automated phylogenetic tree-based small subunit rRNA taxonomy and alignment pipeline (STAP) developed by the authors. STAP generates high-quality multiple sequence alignments and phylogenetic trees from rRNA gene sequences in a fully automated manner, allowing for phylogenetic analysis of large datasets. It combines existing tools like BLAST, CLUSTALW and PHYML with new programs for automated alignment, masking, and tree parsing. STAP yields results comparable to manual analysis but with increased speed and capacity needed to analyze the large volumes of rRNA data now being generated.
The document is a presentation on writing scientific papers. It discusses the structure and components of an introduction section. An effective introduction (1) presents the research field and importance, (2) identifies gaps, questions or limitations in current understanding, and (3) discusses the state of the art in recent research findings to provide context for the study. The purpose is to establish why the present study is important and timely.
The document is a presentation on writing scientific papers. It discusses the structure and components of an introduction section. An effective introduction (1) presents the research field and importance, (2) identifies gaps, questions or limitations in current understanding, and (3) discusses the state of the art in recent research findings to provide context for the study. The purpose is to establish why the present study is important and timely.
Shahid Manzoor has over 15 years of experience in bioinformatics. He holds a PhD in Bioinformatics from the Swedish University of Agricultural Sciences and has taught bioinformatics courses there. His research focuses on analyzing next generation sequencing data to study metabolic pathways and discover new correlations in large biological datasets. He has authored or co-authored over 15 publications and is currently a guest researcher at the Swedish University of Agricultural Sciences.
This paper reviews recent developments in the design of microfluidic concentration gradient generators for biological applications. It discusses how gradient generator designs leverage mass transport principles like diffusion and convection to control gradients. The review provides guidance on design considerations for different biological assays and summarizes factors to account for when using gradient generators. It also outlines perspectives on future improvements to gradient generator technology.
Artificial Neural Networks And Their Application In Biological And Agricultur...Deja Lewis
This document discusses artificial neural networks (ANNs) and their increasing application in biological and agricultural research. It provides background on ANNs, noting they are inspired by the human brain but can currently only partially mimic its functioning. The document outlines two major learning paradigms for ANNs - supervised and unsupervised learning. It also gives examples of ANN applications in plant biology, including for chlorophyll fluorescence data analysis where ANNs achieved 95% accuracy in plant species recognition.
This document provides a preface for a textbook on systems and control. It describes the scope and objectives of the textbook, which is to familiarize readers with dynamical system theory and provide tools for control system design. The textbook can be used for senior/graduate level courses in systems and control, introductory courses in nonlinear systems, or introductory courses in modern automatic control. It includes modeling of dynamical systems from various engineering and scientific disciplines. Analysis methods like Lyapunov stability theory and numerical techniques are discussed. Classical and modern control approaches like fuzzy and neural networks are presented in a unified manner.
The cellular cytoskeleton is essential in proper cell function as well as in organism development. These polymers provide the elaborate roads along which most intracellular protein transport occurs. I will discuss several examples where mathematical modeling, analysis, and simulation tools help us study and understand the interactions between these filaments roads and motor proteins in cells. In neurons, neurofilaments navigate axons and their constrictions to maintain a healthy speed of neuronal communication. We develop stochastic models that may provide insights into transport mechanisms through axonal constrictions. In the reproductive system of the worm C. elegans, we use agent - based modeling to study how myosin motors interact with actin filaments to maintain contractile rings that allow passage and nutrient transport for developing egg cells. In addition, we have recently become interested in using topological data analysis tools to assess maintenance and establishment of these ring structures.
36041 Topic SCI 207 Our Dependence upon the EnvironmentNumber.docxrhetttrevannion
36041 Topic: SCI 207 Our Dependence upon the Environment
Number of Pages: 2 (Double Spaced)
Number of sources: 2
Writing Style: APA
Type of document: Essay
Academic Level:Undergraduate
Category: Environmental Issues
Language Style: English (U.S.)
Order Instructions: Attached
Week 2 - Assignment 1
PLEASE NEED TO TAKE A PHOTO AND DO THE LAB, SEE BELOW THE TEMPLATE:
Properties of Soil, Agriculture and Water Availability Impacts Laboratory
[WLO: 2] [CLOs: 1, 3, 5]
This lab enables you to analyze the natural porosity and particle size of soil samples along with the chemical composition and profile of different soil types.
The Process:
Take the required photos and complete all parts of the assignment (calculations, data tables, etc.). On the “Lab Worksheet,” answer all of the questions in the “Lab Questions” section. Finally, transfer all of your answers and visual elements from the “Lab Worksheet” into the “Lab Report.” You will submit both the “Lab Report” and the “Lab Worksheet” to Waypoint.
The Assignment:
Make sure to complete all of the following items before submission:
Before you begin the assignment, read the Properties of Soil: Agricultural and Water Availability Impacts Investigation ManualPreview the document and review The Scientific Method (Links to an external site.)Links to an external site. presentation video.
Complete Activities 1 through 4 using materials in your kit, augmented by additional materials that you will supply. Photograph each activity following these instructions:
When taking lab photos, you need to include in each image a strip of paper with your name and the date clearly written on it.
Complete all parts of the Week 2 Lab WorksheetPreview the document, and answer all of the questions in the “Lab Questions” section.
Transfer your responses to the lab questions and the data tables and your photos from the “Lab Worksheet” into the “Lab Report” by downloading the Lab Report TemplatePreview the document.
Submit your completed “Lab Report” and “Lab Worksheet” through Waypoint.
Carefully review the Grading Rubric (Links to an external site.)Links to an external site. for the criteria that will be used to evaluate your assignment.
Name of Lab
Your Name
SCI 207: Our Dependence Upon the Environment
Instructor’s Name
Date
*This template will enable you to turn your lab question responses into a polished Lab Report. Simply copy paste your answers to the lab questions, as well as all data tables, graphs, and photographs, in the locations indicated. Before you submit your Lab Report, it is recommended that you run it through Turnitin, using the student folder, to ensure protection from accidental plagiarism. Please delete this purple text before submitting your report.
Name of Lab
Introduction
Copy and paste your response to Question One here.
Copy and paste your response to Question Two here.
Copy and paste your response to Question Three here.
Materials and Methods
Copy and paste y.
Learning to recommend with user generated contentYueshen Xu
This document discusses recommendation systems that incorporate user generated content (UGC) such as tags, reviews, questions/answers, blogs and tweets. It proposes two new matrix factorization-based recommendation models: 1) UTR-MF which regularizes user latent factors based on their interested topics learned from UGC, and 2) ITR-MF which regularizes item latent factors based on their topic distributions learned from associated UGC. The models are evaluated on three real-world datasets and are shown to outperform baselines by utilizing UGC to better learn user preferences and item features. Future work could explore incorporating other UGC types like tweets and blogs.
International Journal of Biometrics and Bioinformatics(IJBB) Volume (3) Issue...CSCJournals
This document summarizes a research paper that proposes a new crossover operator called Sequential Constructive Crossover (SCX) for solving the Traveling Salesman Problem (TSP) using a genetic algorithm. SCX constructs offspring from parent chromosomes by selecting better edges present in the parents while maintaining the node sequence. The performance of SCX is compared to other crossover operators like Edge Recombination Crossover and Generalized N-point Crossover on benchmark TSP instances, and experimental results show that SCX finds higher quality solutions than the other operators. The TSP is an NP-complete problem where the goal is to find the shortest route to visit all cities on a tour and return to the starting city. Genetic algorithms are
This document summarizes research on non-model ascidian species Molgula occulta and Molgula oculata. An international collaboration generated transcriptome data, sequenced the genomes of three Molgula species, and examined gene expression patterns related to tail development. Analysis revealed heterochronic shifts in developmental timing between tailed and tailless species. The data resources enabled further study of evolutionary shifts in gene regulatory networks underlying conserved developmental processes. The document emphasizes the importance of methods development for large-scale data analysis to enable new biological insights.
ICBO 2018 Poster - Current Development in the Evidence and Conclusion Ontolog...dolleyj
The Evidence & Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best
practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology (GO) term for a molecular function to a protein), the type of evidence
supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they
support. These include the Ontology for Microbial Phenotypes and GO. In addition, we continue to support user-level class requests through our GitHub issue tracker. To
facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow.
ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is
also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical
definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open
Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as
the ECO web page (http://evidenceontology.org/).
Genome-Scale Metabolic Models and Systems Medicine of Metabolic SyndromeNatal van Riel
workshop on 'The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes', December 12 and 13, 2013, Eindhoven University of Technology
The document provides an introduction to phylogenetics and phylogenetic trees. It defines a phylogenetic tree as representing the evolutionary relationships among various biological taxa based on similarities and differences in their genetic or physical traits, with taxa more closely related shown closer together on the tree. It discusses how phylogenetic trees are reconstructed from genetic sequence alignments using either character-based or distance-based methods, and how the trees can be used to study organismal evolution and disease transmission patterns.
Introduction to phylogenetics from a mathematical point of view. Distance met...Anna Zhukova
This document introduces phylogenetic trees and bipartitions. It defines the key parts of phylogenetic trees, including roots, branches, inner nodes, and tips. It explains how to determine if a tree is rooted or unrooted, resolved or unresolved. Bipartitions represent splits between taxa on a tree. The bipartition set of a tree includes all possible splits. The bipartition distance between two trees is the number of differing bipartitions in their bipartition sets. This metric is equivalent to the Robinson-Foulds topological distance, which measures the minimum number of operations needed to transform one tree into another.
A modeling workflow in systems biology: An overviewAnna Zhukova
A talk at the 1st RSGLux Congress, November 2016, Belval-University, Luxembourg.
In this talk we go though different steps of a typical modeling workflow, such as model inference, curation, simulation and analysis. We discuss tools and knowledgebases (model databases, ontologies) that can be used at each step, as well as the standards (e.g. SBML, SBGN) that permit us to encode models in a machine-readable format that can be exchanged between various tools, and facilitates model reuse.
Multi-level representation of metabolic networks.Anna Zhukova
Large-scale metabolic models describe thousands of biochemical reactions needed for accurate computer simulation of organism's metabolism. However, the abundance of details in these networks can mask errors and important organism-specific adaptations. This hampers many important tasks during model inference, such as model comparison, analysis, curation and refinement by human experts. It is therefore important to find an abstraction of the network that is comfortable for human experts: It should highlight the essential model structure and the divergences from it, such as alternative paths or missing reactions, while hiding inessential details.
To address this issue, we defined a knowledge-based generalization that allows for production of higher-level abstract views of metabolic network models. We developed a theoretical method that compresses the network by grouping similar metabolites and reactions. The grouping is based on the network structure and on the knowledge extracted from the ontology of molecular entities ChEBI. We implemented our method as a python library, that is available for download from metamogen.gforge.inria.fr.
To validate our method we applied it to 1 286 metabolic models from the Path2Model project, and showed that it helps to detect organism-, and domain-specific adaptations, as well as to compare models.
To facilitate the navigation in metabolic networks, we combined our model generalization method with the zooming user interface (ZUI) paradigm and developed Mimoza, a web-based user-centric tool for knowledge-based exploration of metabolic networks. Mimoza produces a 3-level representation of networks: the full-model level, the generalized level, and the compartment level. Mimoza is available both as an on-line tool and for download at mimoza.bordeaux.inria.fr.
Knowledge-based generalization for metabolic modelsAnna Zhukova
Genome-scale metabolic models describe the relationships between thousands of reactions and biochemical molecules, and are used to improve our understanding of organism’s metabolism. They found applications in pharmaceutical, chemical and bioremediation industries.
The complexity of metabolic models hampers many tasks that are important during the process of model inference, such as model comparison, analysis, curation and refinement by human experts. The abundance of details in large-scale networks can mask errors and important organism-specific adaptations. It is therefore important to find the right levels of abstraction that are comfortable for human experts. These abstract levels should highlight the essential model structure and the divergences from it, such as alternative paths or missing reactions, while hiding inessential details.
To address this issue, we defined a knowledge-based generalization that allows for production of higher-level abstract views of metabolic network models. We developed a theoretical method that groups similar metabolites and reactions based on the network structure and the knowledge extracted from metabolite ontologies, and then compresses the network based on this grouping. We implemented our method as a python
library, that is available for download from metamogen.gforge.inria.fr.
To validate our method we applied it to 1 286 metabolic models from the Path2Model project, and showed that it helps to detect organism-, and domain-specific adaptations, as well as to compare models.
Based on discussions with users about their ways of navigation in metabolic networks, we defined a 3-level representation of metabolic networks: the full-model level, the generalized level, the compartment level. We combined our model generalization method with the zooming user interface (ZUI) paradigm and developed Mimoza, a user-centric tool for zoomable navigation and knowledge-based exploration of metabolic networks that produces this 3-level representation. Mimoza is available both as an on-line tool and for download at mimoza.bordeaux.inria.fr.
The document summarizes Anna Zhukova's presentation on metabolic model generalization. It discusses an algorithm to generalize metabolic models by finding an equivalence relation that minimizes the number of generalized reactions while maximizing the number of generalized species, while preserving stoichiometry. The algorithm works by first defining generalized ubiquitous and specific species, then using a greedy algorithm to solve the exact set cover problem of grouping species into generalized reactions in a way that preserves stoichiometry, and finally maximizing the number of generalized species. The presentation shows an example network being generalized from 53 to 15 reactions.
How to represent a generalized metabolic model using SBML and SBGN?Anna Zhukova
The document discusses representing generalized metabolic models using Systems Biology Markup Language (SBML) and Systems Biology Graphical Notation (SBGN). It describes a method for generalizing metabolic models by grouping similar species and reactions. The method involves creating quotient nodes in the graph representation and using submaps in SBML. The goal is to develop a standardized way of representing generalized models that can integrate information from multiple species-specific models.
Kinetic Simulation Algorithm Ontology and libKiSAOAnna Zhukova
The document describes an update to the Kinetic Simulation Algorithm Ontology (KiSAO). KiSAO 2.0 provides more extensive definitions of simulation algorithms and includes a new library called libKiSAO that allows programs to access KiSAO terms. The talk presented at the COMBINE 2011 conference in Heidelberg discusses the changes between KiSAO 1.0 and 2.0 and provides two use cases of how libKiSAO can interface with simulation tools.
The document discusses the Kinetic Simulation Algorithm Ontology (KiSAO) 2.0, which is an ontology for describing kinetic simulation algorithms. It compares KiSAO 2.0 to the previous version 1.0, noting new classes, properties, and other changes in the updated version. The authors of KiSAO 2.0 are Anna Zhukova, Nick Juty, Camille Laibe, and Nicolas Le Novère.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Visualisation of metabolic data
1. Visualisation of metabolic data
Anna Zhukova 1
1Institut Pasteur
Bioinformatics and Biostatistics Hub
C3BI, USR 3756 IP CNRS
Paris, France
March 3, 2016
Anna Zhukova Metabolic visualisation March 3, 2016 1 / 22
2. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
3. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Model exchange formats
[Hucka et al., 2003]
XML-like
[Lloyd et al., 2004]
XML-like
[Demir et al., 2010]
OWL ontology
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
4. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Model exchange formats
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
5. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Model exchange formats
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
6. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Model exchange formats
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
7. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Model exchange formats
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
8. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Model visualization formats
[Le Nov`ere et al., 2009]
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
9. Metabolic modelling
Metabolic models are mathematical de-
scriptions of biochemical reactions between
molecules in a cell.
Repositories
[Li et al., 2010]
379 Models annotated with metabolic
process
[Kanehisa et al., 2012]
Pathways
[Alc´antara et al., 2012]
Reactions
Anna Zhukova Metabolic visualisation March 3, 2016 2 / 22
10. Size of metabolic models
Pathway-scale – up to hundreds of reactions
Genome-scale (GSM) – thousands of reactions
98 Bacteria, 39 Eukaryota, 6 Archaea
[systemsbiology.ucsd.edu/InSilicoOrganisms/OtherOrganisms]
E. coli [Smallbone, 2013] – 2 168 reactions
S. cerevisiae [Aung et al., 2013] – 2 352 reactions
H. sapiens [Thiele et al., 2013] – 7 440 reactions
Anna Zhukova Metabolic visualisation March 3, 2016 3 / 22
11. Genome-scale models are complicated
Anna Zhukova Metabolic visualisation March 3, 2016 4 / 22
12. Genome-scale models are complicated
Anna Zhukova Metabolic visualisation March 3, 2016 4 / 22
13. Organisation and visualisation of metabolic models
Scale-free networks: fraction of nodes with k connections P(k) ∼ k−γ
, where
typically 2 < γ < 3 [Jeong et al., 2000].
Few hub metabolites, common for most networks:
H+
, H2O, O2, phosphate, ADP, ATP, NADP, NAD+
, etc. (∼ 5%)
Anna Zhukova Metabolic visualisation March 3, 2016 5 / 22
14. Organisation and visualisation of metabolic models
Scale-free networks: fraction of nodes with k connections P(k) ∼ k−γ
, where
typically 2 < γ < 3 [Jeong et al., 2000].
Few hub metabolites, common for most networks:
H+
, H2O, O2, phosphate, ADP, ATP, NADP, NAD+
, etc. (∼ 5%)
– need to be multiplicated
Anna Zhukova Metabolic visualisation March 3, 2016 5 / 22
15. Organisation and visualisation of metabolic models
Scale-free networks: fraction of nodes with k connections P(k) ∼ k−γ
, where
typically 2 < γ < 3 [Jeong et al., 2000].
Few hub metabolites, common for most networks:
H+
, H2O, O2, phosphate, ADP, ATP, NADP, NAD+
, etc. (∼ 5%)
– need to be multiplicated
Nested compartments
mitochondrion ⊂ mitochondrion membrane ⊂ cytoplasm
Anna Zhukova Metabolic visualisation March 3, 2016 5 / 22
16. Organisation and visualisation of metabolic models
Scale-free networks: fraction of nodes with k connections P(k) ∼ k−γ
, where
typically 2 < γ < 3 [Jeong et al., 2000].
Few hub metabolites, common for most networks:
H+
, H2O, O2, phosphate, ADP, ATP, NADP, NAD+
, etc. (∼ 5%)
– need to be multiplicated
Nested compartments
mitochondrion ⊂ mitochondrion membrane ⊂ cytoplasm
– ’cellular component’ in the Gene Ontology [Ashburner et al., 2000]
Anna Zhukova Metabolic visualisation March 3, 2016 5 / 22
17. Organisation and visualisation of metabolic models
Scale-free networks: fraction of nodes with k connections P(k) ∼ k−γ
, where
typically 2 < γ < 3 [Jeong et al., 2000].
Few hub metabolites, common for most networks:
H+
, H2O, O2, phosphate, ADP, ATP, NADP, NAD+
, etc. (∼ 5%)
– need to be multiplicated
Nested compartments
mitochondrion ⊂ mitochondrion membrane ⊂ cytoplasm
– ’cellular component’ in the Gene Ontology [Ashburner et al., 2000]
Cycles/cascades..
TCA cycle
Glycolysis
Anna Zhukova Metabolic visualisation March 3, 2016 5 / 22
18. Organisation and visualisation of metabolic models
Scale-free networks: fraction of nodes with k connections P(k) ∼ k−γ
, where
typically 2 < γ < 3 [Jeong et al., 2000].
Few hub metabolites, common for most networks:
H+
, H2O, O2, phosphate, ADP, ATP, NADP, NAD+
, etc. (∼ 5%)
– need to be multiplicated
Nested compartments
mitochondrion ⊂ mitochondrion membrane ⊂ cytoplasm
– ’cellular component’ in the Gene Ontology [Ashburner et al., 2000]
Cycles/cascades..
TCA cycle – circular layout
Glycolysis – hierarchical layout
Anna Zhukova Metabolic visualisation March 3, 2016 5 / 22
20. I wanna see a pathway!
KEGG
[Kanehisa et al., 2012]
WikiPathways
[Kutmon et al., 2015]
TCA cycle
Anna Zhukova Metabolic visualisation March 3, 2016 7 / 22
21. I wanna see a pathway!
KEGG
[Kanehisa et al., 2012]
WikiPathways
[Kutmon et al., 2015]
TCA cycle
Anna Zhukova Metabolic visualisation March 3, 2016 7 / 22
23. I wanna see my small model!
Desktop tools
Cytoscape
[Cline et al., 2007]
CellDesigner
[Funahashi et al., 2008]
VANTED
[Rohn et al., 2012]
Web-based tools
JWS online
[Olivier and Snoep, 2004]
MetDraw
[Jensen et al., 2014]
Mimoza
[Zhukova and Sher-
man, 2015]
TCA cycle [Nazaret et al., 2008]
Anna Zhukova Metabolic visualisation March 3, 2016 9 / 22
24. I wanna see my small model!
Desktop tools
Cytoscape
[Cline et al., 2007]
CellDesigner
[Funahashi et al., 2008]
VANTED
[Rohn et al., 2012]
Web-based tools
JWS online
[Olivier and Snoep, 2004]
MetDraw
[Jensen et al., 2014]
Mimoza
[Zhukova and Sher-
man, 2015]
TCA cycle [Nazaret et al., 2008]
Anna Zhukova Metabolic visualisation March 3, 2016 9 / 22
25. I wanna see my small model!
Desktop tools
Cytoscape
[Cline et al., 2007]
CellDesigner
[Funahashi et al., 2008]
VANTED
[Rohn et al., 2012]
Web-based tools
JWS online
[Olivier and Snoep, 2004]
MetDraw
[Jensen et al., 2014]
Mimoza
[Zhukova and Sher-
man, 2015]
TCA cycle [Nazaret et al., 2008]
Anna Zhukova Metabolic visualisation March 3, 2016 9 / 22
26. I wanna see my small model!
Desktop tools
Cytoscape
[Cline et al., 2007]
CellDesigner
[Funahashi et al., 2008]
VANTED
[Rohn et al., 2012]
Web-based tools
JWS online
[Olivier and Snoep, 2004]
MetDraw
[Jensen et al., 2014]
Mimoza
[Zhukova and Sher-
man, 2015]
TCA cycle [Nazaret et al., 2008]
Anna Zhukova Metabolic visualisation March 3, 2016 9 / 22
27. I wanna see my small model!
Desktop tools
Cytoscape
[Cline et al., 2007]
CellDesigner
[Funahashi et al., 2008]
VANTED
[Rohn et al., 2012]
Web-based tools
JWS online
[Olivier and Snoep, 2004]
MetDraw
[Jensen et al., 2014]
Mimoza
[Zhukova and Sher-
man, 2015]
TCA cycle [Nazaret et al., 2008]
Anna Zhukova Metabolic visualisation March 3, 2016 9 / 22
28. I wanna see my small model!
Desktop tools
Cytoscape
[Cline et al., 2007]
CellDesigner
[Funahashi et al., 2008]
VANTED
[Rohn et al., 2012]
Web-based tools
JWS online
[Olivier and Snoep, 2004]
MetDraw
[Jensen et al., 2014]
Mimoza
[Zhukova and Sher-
man, 2015]
TCA cycle [Nazaret et al., 2008]
Anna Zhukova Metabolic visualisation March 3, 2016 9 / 22
30. I wanna see my huge model! Classical approach?
Desktop tools?
CellDesigner
[Funahashi et al., 2008]
Web-based tools?
MetDraw
[Jensen et al., 2014]
S. cerevisiae [Aung et al., 2013]
Anna Zhukova Metabolic visualisation March 3, 2016 11 / 22
31. I wanna see my huge model! Classical approach?
Desktop tools?
CellDesigner
[Funahashi et al., 2008]
Web-based tools?
MetDraw
[Jensen et al., 2014]
S. cerevisiae [Aung et al., 2013]
Anna Zhukova Metabolic visualisation March 3, 2016 11 / 22
32. Genome-scale models are complicated
Anna Zhukova Metabolic visualisation March 3, 2016 12 / 22
33. Zooming User Interface (ZUI)
Zoom
geometric zoom
semantic zoom
Anna Zhukova Metabolic visualisation March 3, 2016 13 / 22
34. Zooming User Interface (ZUI)
Zoom
geometric zoom
semantic zoom
Anna Zhukova Metabolic visualisation March 3, 2016 13 / 22
35. Zooming User Interface (ZUI)
Zoom
geometric zoom
semantic zoom
Anna Zhukova Metabolic visualisation March 3, 2016 13 / 22
36. Zooming User Interface (ZUI)
Zoom
geometric zoom
semantic zoom
Decomposition into modules
compartments
pathways
similar elements
Anna Zhukova Metabolic visualisation March 3, 2016 13 / 22
37. Mimoza [mimoza.bordeaux.inria.fr]
3-levels of semantic zoom:
1 full model
2 generalized view
3 compartment view
user’s model as input
fully automatic
Anna Zhukova Metabolic visualisation March 3, 2016 14 / 22
38. Mimoza [mimoza.bordeaux.inria.fr]
3-levels of semantic zoom:
1 full model
2 generalized view
3 compartment view
user’s model as input
fully automatic
Anna Zhukova Metabolic visualisation March 3, 2016 14 / 22
39. Mimoza [mimoza.bordeaux.inria.fr]
3-levels of semantic zoom:
1 full model
2 generalized view
3 compartment view
user’s model as input
fully automatic
Anna Zhukova Metabolic visualisation March 3, 2016 14 / 22
40. Mimoza [mimoza.bordeaux.inria.fr]
3-levels of semantic zoom:
1 full model
2 generalized view
3 compartment view
user’s model as input
fully automatic
Anna Zhukova Metabolic visualisation March 3, 2016 15 / 22
41. NaviCell [Kuperstein et al., 2013]
• large maps of molecular interactions
:) user’s model as input
:( layout is not automatic, user has to:
1 create a map in CellDesigner
2 export as an image
3 produce intermediate views in a graphical designer
4 (optionally) split the map into submaps
Anna Zhukova Metabolic visualisation March 3, 2016 16 / 22
43. Genome Projector [Arakawa et al., 2009]
• zoomable genome map with multiple views
• 320 bacterial genomes
• based on Roche Biochemical Pathway wall chart
• overlay layers to highlight organism-specific reactions
:( only geometric zooming
Anna Zhukova Metabolic visualisation March 3, 2016 18 / 22
44. Cellular Overview [Latendresse et al., 2011]
• maps of organisms in BioCyc [Caspi et al., 2012]
• pathway-oriented
:( no compartments
:( only geometric zooming
Anna Zhukova Metabolic visualisation March 3, 2016 19 / 22
45. Reactome pathway browser [Croft et al., 2013]
• manually curated pathways for 19 organisms
• pathway-oriented
:) two semantic zoom levels:
1 general representation of organism’s pathways
2 detailed pathway submaps
:) compartments
Anna Zhukova Metabolic visualisation March 3, 2016 20 / 22
46. Escher [Zachary et al., 2015]
• manually curated pathways for human, S.cerevisiae, and E.coli
:) user can edit layout and add data
:( only geometric zoom
:( limited selection of pathways
Anna Zhukova Metabolic visualisation March 3, 2016 21 / 22
47. And the winner is...
Manual layout
Anna Zhukova Metabolic visualisation March 3, 2016 22 / 22
48. And the winner is...
Manual layout ZUI
Anna Zhukova Metabolic visualisation March 3, 2016 22 / 22
49. And the winner is...
Manual layout ZUI
Thank you for your attention!
Anna Zhukova Metabolic visualisation March 3, 2016 22 / 22