Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Synthetic biology is the designing of new biological systems or the modification of the existing ones that do not occur naturally. Synthetic or artificial cells organisms with minimal genomes have uses in molecular medicine, vaccines, environmental chemistry and bio-sensors. Creation of synthetic cells involve in-vitro synthesis of unitary DNA fragments of one-kilo base pairs (1kb). These unitary fragments are ligated to make ten kilo base pair (10kb) fragments, followed by tethering 10 fragments to form one hundred kilo base pair (100kb) fragments. Each step involves transformation and sequencing procedures in E. coli host cells. Ultimately, eleven of these hundred kilo base pair fragments are joined to create a “Synthetic Genome” which is maintained in yeast cells, as maximum limit of DNA transplant acceptance of E. coli is 100kb. By this approach, synthetic chromosomes can be maintained, manipulated and transplanted to an acceptor organism to create a synthetic cell. Applications of the technology include semi-synthetic approach of Artemisinic acid, which can be used to chemically synthesize anti-malarial drug Atremisinin and its therapeutically important derivatives. Second application of synthetic biology is production of meningitis vaccine against poorly immunogenic Neisseria meningitidis serogroup-B, by preparing synthetic vesicles. Third application includes disease mechanism identification of a rare-primary immunodeficiency disease “Agamaglobinemia” using reconstruction of mutant B-cell receptor components in synthetic membranes to validate a point mutation. Fourth application include environmental fixation of carbon di-oxide to produce methane by using minimal genome containing synthetic cells of Metahnococcous sp. Fifth application is production of novel biosensors which can be toggled ON and OFF using “Visible Light” as modulator. These “Gene switches” are also able to operate in mammalian cells. With potential applications and wide research domains, synthetic biology is also under ethical and religious criticism. Future of this new dimension of biological science requires scrutiny from regulatory authorities, and monetary input from funding agencies.
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
Synthetic biology is the designing of new biological systems or the modification of the existing ones that do not occur naturally. Synthetic or artificial cells organisms with minimal genomes have uses in molecular medicine, vaccines, environmental chemistry and bio-sensors. Creation of synthetic cells involve in-vitro synthesis of unitary DNA fragments of one-kilo base pairs (1kb). These unitary fragments are ligated to make ten kilo base pair (10kb) fragments, followed by tethering 10 fragments to form one hundred kilo base pair (100kb) fragments. Each step involves transformation and sequencing procedures in E. coli host cells. Ultimately, eleven of these hundred kilo base pair fragments are joined to create a “Synthetic Genome” which is maintained in yeast cells, as maximum limit of DNA transplant acceptance of E. coli is 100kb. By this approach, synthetic chromosomes can be maintained, manipulated and transplanted to an acceptor organism to create a synthetic cell. Applications of the technology include semi-synthetic approach of Artemisinic acid, which can be used to chemically synthesize anti-malarial drug Atremisinin and its therapeutically important derivatives. Second application of synthetic biology is production of meningitis vaccine against poorly immunogenic Neisseria meningitidis serogroup-B, by preparing synthetic vesicles. Third application includes disease mechanism identification of a rare-primary immunodeficiency disease “Agamaglobinemia” using reconstruction of mutant B-cell receptor components in synthetic membranes to validate a point mutation. Fourth application include environmental fixation of carbon di-oxide to produce methane by using minimal genome containing synthetic cells of Metahnococcous sp. Fifth application is production of novel biosensors which can be toggled ON and OFF using “Visible Light” as modulator. These “Gene switches” are also able to operate in mammalian cells. With potential applications and wide research domains, synthetic biology is also under ethical and religious criticism. Future of this new dimension of biological science requires scrutiny from regulatory authorities, and monetary input from funding agencies.
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This presentation deals with what, why, how, where and who of PDB. In this presentation we have also included briefing about various file formats available in PDB with emphasis on PDB file format
Expression and purification of recombinant proteins in Bacterial and yeast sy...Shreya Feliz
This presentation gives the information about bacterial and yeast system as host for expressing recombinant proteins, suitable vectors, strains of host, Pros and cons of this system, different purification techniques and commercially available proteins produced so far by this system.
Cyclic conformation and nucleic acid sugar puckeringDaniel Morton
Cyclic systems are ubiquitous, in nature and synthetic chemistry. Establishing an understanding of the shape preferences (e.g., strain and energetics) regarding representative cyclic models is a powerful tool in conformational analysis. The expanded review of fundamental cycloalkanes can further assist in preferential conformational analysis of associated derivatives.
Contributed by: Roland Jones, Dane Brankle, and Peter Stevenson, University of Utah, 2015
BioPerl is an active open source software project supported by the Open Bioinformatics Foundation.
BioPerl is a product of community effort to produce Perl code which is useful in biology.
BioPerl is a collection of Perl modules
It has played an integral role in the Human Genome Project
Creating a new language to support open innovationMike Hucka
Presentation given on 19 August 2013 at a BioBriefings meeting of the BioMelbourne Network (http://www.biomelbourne.org/events/view/289) in Melbourne, Australia.
A new language for a new biology: How SBML and other tools are transforming m...Mike Hucka
Presentation given at the Victorian Systems Biology Symposium (http://www.emblaustralia.org/About_us/news/mike-hucka.aspx) at the Walter and Eliza Hall Institute in Melbourne, Australia, on 20 August 2013.
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This presentation deals with what, why, how, where and who of PDB. In this presentation we have also included briefing about various file formats available in PDB with emphasis on PDB file format
Expression and purification of recombinant proteins in Bacterial and yeast sy...Shreya Feliz
This presentation gives the information about bacterial and yeast system as host for expressing recombinant proteins, suitable vectors, strains of host, Pros and cons of this system, different purification techniques and commercially available proteins produced so far by this system.
Cyclic conformation and nucleic acid sugar puckeringDaniel Morton
Cyclic systems are ubiquitous, in nature and synthetic chemistry. Establishing an understanding of the shape preferences (e.g., strain and energetics) regarding representative cyclic models is a powerful tool in conformational analysis. The expanded review of fundamental cycloalkanes can further assist in preferential conformational analysis of associated derivatives.
Contributed by: Roland Jones, Dane Brankle, and Peter Stevenson, University of Utah, 2015
BioPerl is an active open source software project supported by the Open Bioinformatics Foundation.
BioPerl is a product of community effort to produce Perl code which is useful in biology.
BioPerl is a collection of Perl modules
It has played an integral role in the Human Genome Project
Creating a new language to support open innovationMike Hucka
Presentation given on 19 August 2013 at a BioBriefings meeting of the BioMelbourne Network (http://www.biomelbourne.org/events/view/289) in Melbourne, Australia.
A new language for a new biology: How SBML and other tools are transforming m...Mike Hucka
Presentation given at the Victorian Systems Biology Symposium (http://www.emblaustralia.org/About_us/news/mike-hucka.aspx) at the Walter and Eliza Hall Institute in Melbourne, Australia, on 20 August 2013.
A summary of various COMBINE standardization activitiesMike Hucka
Invited presentation given at the Whole-Cell Modeling Summer School, held in Rostock, Germany, March 2015.
https://sites.google.com/site/vwwholecellsummerschool/important-dates/programm
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.
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.
Short summary of recent SBML developments given at the COMBINE (COmputational Modeling in BIology NEtwork) 2014 meeting held at the University of Southern California in August, 2014. The meeting page is available at http://co.mbine.org/events/COMBINE_2014
The importance of model fairness and interpretability in AI systemsFrancesca Lazzeri, PhD
Machine learning model fairness and interpretability are critical for data scientists, researchers and developers to explain their models and understand the value and accuracy of their findings. Interpretability is also important to debug machine learning models and make informed decisions about how to improve them.
In this session, Francesca will go over a few methods and tools that enable you to "unpack” machine learning models, gain insights into how and why they produce specific results, assess your AI systems fairness and mitigate any observed fairness issues.
Using open-source fairness and interpretability packages, attendees will learn how to:
- Explain model prediction by generating feature importance values for the entire model and/or individual data points.
- Achieve model interpretability on real-world datasets at scale, during training and inference.
- Use an interactive visualization dashboard to discover patterns in data and explanations at training time.
- Leverage additional interactive visualizations to assess which groups of users might be negatively impacted by a model and compare multiple models in terms of their fairness and performance.
Machine Learning Interpretability - Mateusz Dymczyk - H2O AI World London 2018Sri Ambati
This talk was recorded in London on Oct 30, 2018 and can be viewed here: https://youtu.be/p4iAnxwC_Eg
The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes!
This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models
Mateusz is a software developer who loves all things distributed, machine learning and hates buzzwords. His favourite hobby data juggling.
He obtained his M.Sc. in Computer Science from AGH UST in Krakow, Poland, during which he did an exchange at L’ECE Paris in France and worked on distributed flight booking systems. After graduation he move to Tokyo to work as a researcher at Fujitsu Laboratories on machine learning and NLP projects, where he is still currently based.
This presentation gives an overview of Caltech DIBS, a system for digital controlled lending (CDL) implemented by the California Institute of Technology Library in early 2021 to support course serves and other academic library needs at Caltech.
Short summary of COMBINE (COmputational Modeling in BIology NEtwork) given at the COMBINE 2014 meeting held at the University of Southern California in August, 2014. The meeting page is available at http://co.mbine.org/events/COMBINE_2014
Afternoon tutorial given at the COMBINE/ERASysApp day of tutorials on "Modelling and Simulation of Biological Models" on Sunday, September 14, ahead of ICSB 2014 in Melbourne, Australia.
Reproducibility of computational research: methods to avoid madness (Session ...Mike Hucka
Introduction on the session "Reproducibility of computational research: methods to avoid madness" held Wednesday, September 17, during ICSB 2014 in Melbourne, Australia, 2014.
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.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Computational Approaches to Systems Biology
1. Computational Approaches
to Systems Biology
Michael Hucka, Ph.D.
Department of Computing + Mathematical Sciences
California Institute of Technology
Pasadena, CA, USA
The Kinghorn Cancer Centre, Australia, August 2013
Email: mhucka@caltech.edu Twitter: @mhucka
2. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
3. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
7. Many models have traditionally been published this way
Problems:
• Errors in printing
• Missing information
• Dependencies on
implementation
• Outright errors
• Can be a huge
effort to recreate
Is it enough to communicate the model in a paper?
8. Is it enough to make your (software X) code available?
It’s vital for good science:
• Someone with access to the same software can try to run it,
understand it, verify the computational results, build on them, etc.
• Opinion: you should always do this in any case
9. Is it enough to make your (software X) code available?
It’s vital for good science—
• Someone with access to the same software can try to run it,
understand it, build on it, etc.
• Opinion: you should always do this in any case
But it’s still not ideal for communication of scientific results:
• Doesn’t necessarily encode biological semantics of the model
• What if they don’t have access to the same software?
• What if they don’t want to use that software?
• What if they want to use a different conceptual framework?
• And how will people be able to relate the model to other work?
11. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
13. Format for representing computational models of biological processes
• Data structures + usage principles + serialization to XML
• (Mostly) Declarative, not procedural—not a scripting language
Neutral with respect to modeling framework
• E.g., ODE, stochastic systems, etc.
Important: software reads/writes SBML, not humans
SBML = Systems Biology Markup Language
15. The process is central
• Literally called a“reaction”in SBML
• Participants are pools of entities (biochemical species)
Models can further include:
• Compartments
• Other constants & variables
• Discontinuous events
• Other, explicit math
Core SBML concepts are fairly simple
• Unit definitions
• Annotations
19. Reactions can cross compartment boundaries
c
n
protein A protein B
gene mRNAn mRNAc
20. Reaction/process rates can be (almost) arbitrary formulas
c
n
protein A protein B
gene mRNAn mRNAc
f1(x)
f2(x)
f3(x)f4(x)
f5(x)
21. “Rules”: equations expressing relationships in addition to reaction sys.
c
n
protein A protein B
gene mRNAn mRNAc
f1(x)
f2(x)
f3(x)
g1(x)
g2(x)
.
.
.
f4(x)
f5(x)
22. “Events”: discontinuous actions triggered by system conditions
c
n
protein A protein B
gene mRNAn mRNAc
f1(x)
f2(x)
f3(x)
g1(x)
g2(x)
.
.
.
Event1: when (...condition...),
do (...assignments...)
Event2: when (...condition...),
do (...assignments...)
...
f4(x)
f5(x)
23. Annotations: machine-readable semantics and links to other resources
Event1: when (...condition...),
do (...assignments...)
Event2: when (...condition...),
do (...assignments...)
...
c
n
protein A protein B
gene mRNAn mRNAc
f1(x)
f2(x)
f3(x)
g1(x)
g2(x)
.
.
.
f4(x)
f5(x)
“This event
represents ...”
“This is identified
by GO id # ...”
“This is an enzymatic
reaction with EC # ...”
“This is a transport
into the nucleus ...” “This compartment
represents the nucleus ...”
28. Question: Which of the following categories best describe your software?
(Check all that apply.)
Results of 2011 survey of SBML-compatible software
Out of 81 responses
Simulation software
Analysis s/w (in addition, or instead of, simulation)
Creation/model development software
Visualization/display/formatting software
Utility software (e.g., format conversion)
Data integration and management software
Repository or database
Framework or library (for use in developing s/w)
S/w for interactive env. (e.g., MATLAB, R, ...)
Annotation software
0 20 40 60 80
11
13
13
14
16
23
31
31
40
42
29. Some particularly full-featured, general simulation tools
COPASI: ODE & stochastic simulation, parameter scanning, plotting
Virtual Cell: web-based environment, spatial models
iBioSim: special features for genetic circuit models for synthetic biology
SBW (Systems Biology Workbench): component-based toolkit
SBMLsimulator: Java-based simulator, web-start or stand-alone
CellDesigner: graphical editing, SBGN support, SABIO-RK integration
30. Free software libraries – libSBML
Reads, writes, validates SBML
Can check & convert units
Written in portable C++
Runs on Linux, Mac, Windows
APIs for C, C++, C#, Java, Octave,
Perl, Python, R, Ruby, MATLAB
Well documented API
Open-source (LGPL)
http://sbml.org/Software/libSBML
31. Evolution of SBML continues
Today: SBML Level 3
• Level 3 Core provides framework for common models
• Level 3 packages add additional constructs to the Core
32. Level 3 package What it enables
Hierarchical model composition Models containing submodels ✔
Flux balance constraints Constraint-based models ✔
Qualitative models Petri net models, Boolean models ✔
Graph layout Diagrams of models ✔
Multicomponent/state species Entities w/ structure; also rule-based models draft
Spatial Nonhomogeneous spatial models draft
Graph rendering Diagrams of models draft
Groups Arbitrary grouping of components draft
Distributions Numerical values as statistical distributions in dev
Arrays & sets Arrays or sets of entities in dev
Dynamic structures Creation & destruction of components in dev
Annotations Richer annotation syntax
Status
33. NationalInstituteofGeneralMedicalSciences(USA)
European Molecular Biology Laboratory (EMBL)
JST ERATO Kitano Symbiotic Systems Project (Japan) (to 2003)
JST ERATO-SORST Program (Japan)
ELIXIR (UK)
Beckman Institute, Caltech (USA)
Keio University (Japan)
International Joint Research Program of NEDO (Japan)
Japanese Ministry of Agriculture
Japanese Ministry of Educ., Culture, Sports, Science and Tech.
BBSRC (UK)
National Science Foundation (USA)
DARPA IPTO Bio-SPICE Bio-Computation Program (USA)
Air Force Office of Scientific Research (USA)
STRI, University of Hertfordshire (UK)
Molecular Sciences Institute (USA)
SBML funding sources over the past 13+ years
34. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
38. Raw models alone are insufficient
Need standard schemes for
machine-readable annotations
• Identify entities
• Mathematical semantics
• Links to other data resources
• Authorship & pub. info
Modelerswanttousetheirownconventions
Low info
content
No standard
identifiers
39. Addresses 2 general areas of annotation needs:
MIRIAM is not specific to SBML
MIRIAM(MinimumInformationRequestedIntheAnnotationofModels)
Requirements for
reference correspondence
Scheme for encoding
annotations
Annotations for
attributing model
creators & sources
Annotations for
referring to external
data resources
40. Addresses 2 general areas of annotation needs:
MIRIAM is not specific to SBML
MIRIAM(MinimumInformationRequestedIntheAnnotationofModels)
Requirements for
reference correspondence
Scheme for encoding
annotations
Annotations for
attributing model
creators & sources
Annotations for
referring to external
data resources
Annotations for
referring to external
data resources
41. Example of a problem that can be solved with annotations
http://www.ebi.ac.uk/chebi
Low info
content
42. Example of a problem that can be solved with annotations
http://www.ebi.ac.uk/chebi
Low info
content
Known by different names –
do you want to write all of
them into your model?
salicylic acid
43. MIRIAM annotations for external references
Goal: link model constituents to corresponding entities in
bioinformatics resources (e.g., databases, controlled vocabularies)
• Supports:
- Precise identification of model constituents
- Discovery of models that concern the same thing
- Comparison of model constituents between different models
MIRIAM approach avoids putting data content directly in the model
• Instead, it points at external resources that contain the data
44. How do we create globally unique identifiers consistently?
Long story short—developed by the Le Novère group at the EBI
• Resource identifiers (URIs) combine 2 parts:
• There’s a registry for namespaces: MIRIAM Registry
- Allows people & software to use same namespace identifiers
• There’s a URI resolution service: MIRIAM Resources & identifiers.org
- Allows people & software to take a given identifier and figure
out what it points to
namespace entity identifier
{
{
Identifies a dataset Identifies a datum
within the dataset
45. Another problem: software can’t read figure legends
?
BIOMD0000000319 in BioModels Database
Decroly & Goldbeter, PNAS, 1982
46. SED-ML = Simulation Experiment Description ML
Application-independent format
•Captures procedures, algorithms, parameter values
Can be used for
•Simulation experiments encoding parametrizations & perturbations
•Simulations using more than one model and/or method
•Data manipulations to produce plot(s)
http://sedml.org
Simulation
Model
Task Data generators
Reports
47. Efforts like SED-ML improve reproducibility of publications
Waltemath et al.,
BMC Sys Bio 5, 2011.
48. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
49. Need interoperable formats, but developing them is not easy
Need people with diverse set of knowledge & skills
• Scientific needs
• Technical implementation skills
• Practical experience
Need manage multiple phases of a standardization effort
• Creation
• Evolution
• Support
50. Need interoperable formats, but developing them is not easy
Need people with diverse set of knowledge & skills
• Scientific needs
• Technical implementation skills
• Practical experience
Need manage multiple phases of a standardization effort
• Creation
• Evolution
• Support
} This is just for the specification of the
standards, to say nothing of the necessary
software and other infrastructure!
51. Realizations about the state of affairs in late-2000’s
• Many standardization efforts overlapped, but lacked coordination
• Efforts were inventing their own processes from scratch
• Many individual meetings meant more travel for many people
• Limited and fragile funding didn’t support solid, coherent base
COMBINE = Computational Modeling in Biology Network
• Coordinate standards development
• Develop common procedures & tools (but not impose them!)
• Coordinate meetings
• Provide a recognized voice
Motivations for the creation of COMBINE
52. Standardization efforts represented in COMBINE today
BioPAX
Qualifiers
GPML
COMBINE Standards
Associated Standardization Efforts
Related Standardization Efforts
54. Examples of community organization
Two main annual meetings, plus ad hoc workshops
• COMBINE meeting: status updates, presentations, outreach
- Next COMBINE: Paris, Sep 16–20, 2013
• HARMONY: Hackathon on Resources for Modeling in Biology
- Software development, interoperability hacking
COMBINE 2012, TorontoCOMBINE 2011, Heidelberg
55. COMBINE is open to all—and COMBINE needs you!
http://co.mbine.org
Current coordinators:
• Nicolas Le Novère, Mike Hucka, Falk Schreiber, Gary Bader
56. Outline
Background and introduction
The Systems Biology Markup Language (SBML)
Complementary efforts: MIRIAM and SED-ML
COMBINE: the Computational Modeling in Biology Network
Conclusion
57. Time it well
• Too early and too late are bad
Start with actual stakeholders
• Address real needs, not perceived ones
Start with small team of dedicated developers
• Can work faster, more focused; also avoids“designed-by-committee”
Engage people constantly, in many ways
• Electronic forums, email, electronic voting, surveys, hackathons
Make the results free and open-source
• Makes people comfortable knowing it will always be available
Be creative about seeking funding
Some things we (maybe?) got right with SBML
58. Not waiting for implementations before freezing specifications
• Sometimes finalized specification before implementations tested it
- Especially bad when we failed to do a good job
‣ E.g.,“forward thinking”features, or“elegant”designs
Not formalizing the development process sufficiently
• Especially early in the history, did not have a very open process
Not resolving intellectual property issues from the beginning
• Industrial users ask“who has the right to give any rights to this?”
Some things we certainly got wrong
59. Nicolas Le Novère, Henning Hermjakob, Camille Laibe, Chen Li, Lukas Endler,
Nico Rodriguez, Marco Donizelli,Viji Chelliah, Mélanie Courtot, Harish Dharuri
Attendees at SBML 10th Anniversary Symposium, Edinburgh, 2010
John C. Doyle, Hiroaki Kitano
Mike Hucka, Sarah Keating, Frank Bergmann, Lucian Smith, Andrew Finney,
Herbert Sauro, Hamid Bolouri, Ben Bornstein, Bruce Shapiro, Akira Funahashi,
Akiya Juraku, Ben Kovitz
OriginalPI’s:
SBMLTeam:
SBMLEditors:
BioModelsDB:
Mike Hucka, Nicolas Le Novère, Sarah Keating, Frank Bergmann, Lucian Smith,
Chris Myers, Stefan Hoops, Sven Sahle, James Schaff, DarrenWilkinson
And a huge thanks to many others in the COMBINE community
This work was made possible thanks to a great community