Merge pull request #364 from combine-org/harmony-fixes

More link fixes.

Author: luciansmith

content/authors/COMBINE_2023/_index.md

@@ -120,7 +120,7 @@ The hotel serves hot breakfast. There is a [Butchers and Bakers restaurant](http
 | <b>Eran Agmon</b> <br/> UConn Health | Oct 5 Oct 6 Oct 7 Oct 8 | SED-ML, OMEX, COMBINE;Multicellular modeling <hr> Vivarium: https://vivarium-collective.github.io | 
 | <b>Fengkai Zhang</b> <br/> NIH | Oct 5 Oct 6 Oct 7 Oct 8 | SBML;SED-ML, OMEX, COMBINE;SBGN <hr> Simmune (https://simmune.org/simmune/i_sim.html ), rule-based modeling and libSBML-multi | 
 | <b>Frank T. Bergmann</b> <br/> BioQUANT, Heidelberg University | Oct 5 Oct 6 Oct 7 Oct 8 | SBML;SED-ML, OMEX, COMBINE;SBGN <hr> COPASI (https://copasi.org ), basico (https://basico.readthedocs.io/ ), libSBML / libSEDML / libCombine | 
-| <b>Gaoxiang Zhou</b> <br/> University of Pittsburgh | Oct 5 Oct 6 Oct 7 Oct 8 | SBML;BioPAX;SBOL, SBOL visual;CellML;Multicellular modeling <hr> (www.nmzlab.pitt.edu/people/gaoxiang-zhou), https://github.com/pitt-miskov-zivanov-lab, https://melody-biorecipe.readthedocs.io | 
+| <b>Gaoxiang Zhou</b> <br/> University of Pittsburgh | Oct 5 Oct 6 Oct 7 Oct 8 | SBML;BioPAX;SBOL, SBOL visual;CellML;Multicellular modeling <hr> https://github.com/pitt-miskov-zivanov-lab, https://melody-biorecipe.readthedocs.io | 
 | <b>Gerhard Mayer</b> <br/> HITS (Heidelberg Institute for Theoretical Studies) gGmbH, Heidelberg | remotely | SED-ML, OMEX, COMBINE;Multicellular modeling <hr> EDITH (Ecosystem Digital Twins in Healthcare); https://www.edith-csa.eu | 
 | <b>Herbert M Sauro</b> <br/> University of Washington | remotely | SBML;SED-ML, OMEX, COMBINE;Multicellular modeling <hr> SBML, roadrunner etc | 
 | <b>Ion Moraru</b> <br/> UConn Health | Oct 5 Oct 6 Oct 7 Oct 8 | SBML;SED-ML, OMEX, COMBINE;model credibility <hr> VCell, BioSimulations, BioSimulators | 
@@ -283,16 +283,16 @@ In this demo, we will showcase our framework, DySE (Dynamic System Explanation),
 
 To tackle this challenge, DySE integrates machine reading, automated model assembly, and computational analysis to enhance understanding and explanation of complex systems. The toolset is conveniently accessible via a user-friendly graphical interface (GUI).
 
-FLUTE utilizes existing databases to evaluate confidence and trustworthiness of given biochemical interactions (https://tinyurl.com/flutedoc ). VIOLIN classifies large sets of interactions with respect to a given model. The interactions are classified into four main categories, corroborations, contradictions, extensions, and flagged, and several subcategories within main ones (https://tinyurl.com/violindoc ). CLARINET (see https://tinyurl.com/clarinetdoc and link to binder notebook on main page) and ACCORDION (see https://tinyurl.com/accordiondoc and link to binder notebook on main page) automatically expand and recommend models based on selected desired model properties.
+FLUTE utilizes existing databases to evaluate confidence and trustworthiness of given biochemical interactions (https://melody-flute.readthedocs.io/en/latest/ ). VIOLIN classifies large sets of interactions with respect to a given model. The interactions are classified into four main categories, corroborations, contradictions, extensions, and flagged, and several subcategories within main ones (https://github.com/pitt-miskov-zivanov-lab/violin). CLARINET (see https://github.com/pitt-miskov-zivanov-lab/clarinet and link to binder notebook on main page) and ACCORDION (see https://github.com/pitt-miskov-zivanov-lab/accordion and link to binder notebook on main page) automatically expand and recommend models based on selected desired model properties.
 
-In conjunction with these front-end tools, we've also developed back-end tools within the DySE framework. DiSH is a stochastic simulator offering versatile simulation schemes and timing options (https://tinyurl.com/dishjupyter). PIANO provides comprehensive sensitivity analysis for the entire model, and allows for identifying most influential pathways and suggesting interventions (https://tinyurl.com/pianojupyter ). Additionally, we've introduced a unified format compatible with the tools mentioned above: BioRECIPE (https://tinyurl.com/biorecipe ), seamlessly translating to and from widely used synthetic biology modeling languages.
+In conjunction with these front-end tools, we've also developed back-end tools within the DySE framework. DiSH is a stochastic simulator offering versatile simulation schemes and timing options (https://tinyurl.com/dishjupyter). PIANO provides comprehensive sensitivity analysis for the entire model, and allows for identifying most influential pathways and suggesting interventions (https://github.com/pitt-miskov-zivanov-lab/dyse_wm/blob/main/examples/sa_test_hybrid.ipynb ). Additionally, we've introduced a unified format compatible with the tools mentioned above: BioRECIPE (https://melody-biorecipe.readthedocs.io/en/latest/ ), seamlessly translating to and from widely used synthetic biology modeling languages.
 
 All of these tools can be used either independently or in combination. For instance, synthetic biologists can rely on it to ensure the reliability of interactions in designing synthetic biological systems. Bioinformaticians can efficiently filter and prioritize data, while computational modelers benefit from model extensions to always get model up-to-date. Professionals in biotech industries utilize sensitivity analysis for optimizing therapies. Additionally, even educators would find it valuable when using simulation software.
 <hr>
 
 <b> Novel advances in the automation of knowledge selection and model assembly</b> 	Natasa Miskov-Zivanov, 	Yasmine Ahmed,  Gaoxiang Zhou	(University of Pittsburgh)<br>
 Creating computational models of complicated systems, including intracellular and intercellular bionetworks, is a time and labor-intensive task which is often limited by the knowledge and experience of Pathway database modelers. This has naturally led to the emergence of the idea of automating the process of building new/extending existing models, which could have a significant potential in enabling rapid, consistent, comprehensive and robust analysis of complicated systems. Inspired by this idea, we propose in this work different novel approaches namely ACCORDION (ACCelerating and Optimizing model RecommenDatIONs) and CLARINET (CLARifying NETworks) for expanding models using the information extracted from literature by machine reading engines. Our proposed approaches combine machine reading with clustering, and graph theoretical analysis to create an automated framework for efficient model assembly. Furthermore, by automatically extending models with the information published in literature, our proposed methods allow for collecting the existing information in a consistent and comprehensive way. This, in turn, facilitates information reuse, data reproducibility, and replacing hundreds/thousands of manual experiments, thereby reducing the time needed for the advancement of knowledge. To evaluate ACCORDION1 and CLARINET, we compare their outcomes with three previously published manually created models namely naive T cell differentiation model, T cell large granular lymphocyte leukemia model and pancreatic cancer cell model. Besides demonstrating automated reconstruction of a model that was previously built manually, our tools can assemble multiple models that satisfy desired system properties. As such, they replace large number of tedious or even impractical manual experiments and guide alternative hypotheses and interventions in biological systems.
-A GitHub page, ReadtheDocs and Jupyter notebook are available for ACCORDION http://www.nmzlab.pitt.edu/accordion and CLARINET http://www.nmzlab.pitt.edu/clarinet.
+A GitHub page, ReadtheDocs and Jupyter notebook are available for ACCORDION https://github.com/pitt-miskov-zivanov-lab/accordion and CLARINET https://github.com/pitt-miskov-zivanov-lab/clarinet.
 <hr>
 
 <b>Using Compucell3D as a Platform for Model Building to Explore Cell Behaviors, Cell-Cell Interactions, Cell Migration and Chemotaxis </b> Pedro Dal-Castel (Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University)<br>

content/authors/COMBINE_2024/_index.md

@@ -95,7 +95,7 @@ COMBINE 2024 takes place at the [University of Stuttgart](https://www.uni-stuttg
 
 <h3>Arrival and Transportation</h3>
 
-The closest airport is the [Stuttgart Airport (STR)](https://www.stuttgart-airport.com/?cl=en). However, overseas the International Airport of Frankfurt [Frankfurt Airport (FRA)](https://www.frankfurt-airport.com/en.html) is probably the better choice. There are ICEs and ICs going from the Frankfurt Airport to Stuttgart every one to two hours. You can book the train tickets via the [Deutsche Bahn](https://int.bahn.de/en) website. The Campus in Stuttgart, where we will host the Combine2024 is a 10 minute drive from the city center of Stuttgart. Unfortunately, the main line of the Sbahn is still closed during the COMBINE period. There is a rail replacement service (SEV) that serves all stops every 5 minutes Replacement Service - only German website available (was www.vvs.de/stammstreckensperrung24). You can take any of these buses to the Universität stop when arriving at Stuttgart Central Station. If you arrive at Stuttgart airport, take the S-Bahn S2 to „Schorndorf“ or S3 to „Backnang“. In this case you can change to the bus replacement service (SEV) at the train station "Stuttgart Vaihingen". Alternatively, if you come by car, set your destination at "Universitätsstrasse 34. 70569 Stuttgart (Campus Vaihingen)".
+The closest airport is the [Stuttgart Airport (STR)](https://www.stuttgart-airport.com/?cl=en). However, overseas the International Airport of Frankfurt [Frankfurt Airport (FRA)](https://www.frankfurt-airport.com/en.html) is probably the better choice. There are ICEs and ICs going from the Frankfurt Airport to Stuttgart every one to two hours. You can book the train tickets via the [Deutsche Bahn](https://int.bahn.de/en) website. The Campus in Stuttgart, where we will host the Combine2024 is a 10 minute drive from the city center of Stuttgart. Unfortunately, the main line of the Sbahn is still closed during the COMBINE period. There is a rail replacement service (SEV) that serves all stops every 5 minutes Replacement Service. You can take any of these buses to the Universität stop when arriving at Stuttgart Central Station. If you arrive at Stuttgart airport, take the S-Bahn S2 to „Schorndorf“ or S3 to „Backnang“. In this case you can change to the bus replacement service (SEV) at the train station "Stuttgart Vaihingen". Alternatively, if you come by car, set your destination at "Universitätsstrasse 34. 70569 Stuttgart (Campus Vaihingen)".
 
 <h3>Accomodation</h3>
 
@@ -109,7 +109,7 @@ There are no hotels at the workshop venue. However, you can find accommodations
 
 
 <b>Training Models using PEtab</b> Fröhlich, Fabian	<br>
-[PEtab](https://doi.org/10.1371/journal.pcbi.1008646) is a standardized file format for specifying parameter estimation problems. The interoperable format is currently supported by [11 different tools](https://github.com/PEtab-dev/petab#petab-support-in-systems-biology-tools), enabling users to benefit from standardized parameter estimation across frameworks based in Python, Julia, R, MATLAB, C++, or GUIs. Although PEtab was initially developed for parameter estimation, recent efforts have extended the format to improve standardization of various adjacent tasks, including: model selection, multi-scale modeling, PKPD and NLME modeling, optimal control, and visualization. In this breakout session, based on audience interests, we will present introductions to PEtab and its extensions, then discuss current efforts to improve PEtab. People unfamiliar with PEtab are welcome to attend, and might first like to check out the [tutorial](https://petab.readthedocs.io/en/latest/tutorial.html).
+[PEtab](https://doi.org/10.1371/journal.pcbi.1008646) is a standardized file format for specifying parameter estimation problems. The interoperable format is currently supported by [11 different tools](https://github.com/PEtab-dev/petab#petab-support-in-systems-biology-tools), enabling users to benefit from standardized parameter estimation across frameworks based in Python, Julia, R, MATLAB, C++, or GUIs. Although PEtab was initially developed for parameter estimation, recent efforts have extended the format to improve standardization of various adjacent tasks, including: model selection, multi-scale modeling, PKPD and NLME modeling, optimal control, and visualization. In this breakout session, based on audience interests, we will present introductions to PEtab and its extensions, then discuss current efforts to improve PEtab. People unfamiliar with PEtab are welcome to attend, and might first like to check out the [docs](https://petab.readthedocs.io/en/latest/).
 
 <b>Introduction into Nix for scientific software</b> Hauser, Simon	<br>
 Working on software in a team brings all kinds of challenges, especially because everyone has a slightly different development environment.

content/authors/Dagmar--Waltemath/_index.md

@@ -80,7 +80,7 @@ social:
   link: https://orcid.org/0000-0002-5886-5563
 - icon: university-logo
   icon_pack: Custom_University
-  link: https://www.medizin.uni-greifswald.de/de/home/
+  link: https://www.unimedizin-greifswald.de/
 # - icon: university
 #   icon_pack: fas
 #   link: https://www.colorado.edu/ecee