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Example of a LinearGradient along the horizontal line, starting from orange at 0%, going to yellow at 100%
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Many software tools provide facilities for depicting reaction network diagrams in a visual form. Two aspects of such a visual diagram can be distinguished: the layout (i.e.: the positioning and connections) of the elements in the diagram, and the graphical form of the elements (for example, the glyphs used for symbols, the properties of the lines c...
Citations
... Layout Gauges et al. (2006) permits users to specify the visual layout of a biochemical network model described in the SBML. Render Bergmann et al. (2018) is an extension to the layout package that permits users to specify the look and feel of a laid-out network, in terms of colors, line thickness etc. ...
Antimony is a high-level, human-readable text-based language designed for defining and sharing models in the systems biology community. It enables scientists to describe biochemical networks and systems using a simple and intuitive syntax. It allows users to easily create, modify, and distribute reproducible computational models. By allowing the concise representation of complex biological processes, Antimony enhances collaborative efforts, improves reproducibility, and accelerates the iterative development of models in systems biology. This paper provides an update to the Antimony language since it was introduced in 2009. In particular, we highlight new annotation features, support for flux balance analysis, a new rateOf method, support for probability distributions and uncertainty, named stochiometries, and algebraic rules. Antimony is also now distributed as a C/C++ library, together with python and Julia bindings, as well as a JavaScript version for use within a web browser. Availability: https://github.com/sys-bio/antimony.
... However, a particular extension, the Flux Balance Constraints (FBC) package (Olivier and Bergmann, 2018), is required to store constraint-based genome-scale models in it. Other packages open up additional features of the SBML format to users, such as linking metabolic maps directly to the computational model (Gauges et al., 2015;Bergmann et al., 2018), offering a wide range of visualization capabilities (Buchweitz et al., 2020;Holzapfel et al., 2022). ...
Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated.
Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum .
Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth.
Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems .
... The diagrams are available in SBML format (Keating et al, 2020), allowing computational modelling of biological processes. SBML stores visual information about encoded elements and reactions using render (Bergmann et al, 2018) and layout (Gauges et al, 2015) packages. An early version of SBML adapted by CellDesigner allows storing layout and rendering information. ...
... This is addressed by another hybrid approach that goes a step further toward the analogy with circuit diagrams and integrates layout and rendering information directly into the model structure. It is mainly prevalent in languages with an industrial background, such as Modelica and MATLAB, but is also implemented in the SBML level 3 layout and rendering packages 62,63 . In this approach, model components are assigned graphical annotations, which define how the component should look and where it should be placed in the diagram representation 1)), which can be placed in a diagram coordinate system (//(2)) and connected with lines (// (3)). ...
Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.
... The diagrams are available in SBML format (Keating et al, 2020), allowing computational modelling of biological processes. SBML stores visual information about encoded elements and reactions using render (Bergmann et al, 2018) and layout (Gauges et al, 2015) packages. An early version of SBML adapted by CellDesigner allows storing layout and rendering information. ...
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
... The diagrams are available in SBML format (Keating et al, 2020), allowing computational modelling of biological processes. SBML stores visual information about encoded elements and reactions using render (Bergmann et al, 2018) and layout (Gauges et al, 2015) packages. An early version of SBML adapted by CellDesigner allows storing layout and rendering information. ...
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
... Using graphics to describe a biological network system is direct, efficient, and easily understood. However, before the advent of SBGN, graphical descriptions of biological network systems did not [26] Fbc Genome-scale Whole-cell Groups [28] Groups Model annotation Sub-systems Layout [29] Layout Data integration Multistate, multicomponent and multicompartment species [30] Multi Multistate molecules Multicomponent complexes Qualitative models [31] Qual Regulatory control Signaling Rendering [32] Render Data integration Spatial processes Spatial Reaction-diffusion Spatial organization 1 3 ...
The complex mechanisms of the internal operation of cellular functions have not been fully resolved and these functions are regulated by multiple effects, such as transcription regulation, signal transduction, and enzyme catalysis, forming complex interactive mechanisms. This makes the construction of a whole-cell computational model, containing various complex cellular functions, very challenging. However, biological models have played a significant role in the field of systems biology, such as guiding gene-target mining and studying cell metabolic characteristics. Therefore, there is increasing research interest in the construction of whole-cell computational models. Combining two classical languages of systems biology, this review expounds on the development and challenges of whole-cell computational modeling from the two classical methods of steady-state and dynamic modeling. Finally, we propose a new approach for constructing whole-cell computational models.
... This is addressed by another hybrid approach that goes a step further towards the analogy with circuit diagrams and integrates layout and rendering information directly into the model structure. It is mainly prevalent in languages with an industrial background such as Modelica and MAT-LAB, but is also implemented in the SBML level 3 layout and rendering packages [63,64]. In this approach, model components are assigned graphical annotations, which define how the component should look and where it should be placed in the diagram representation of the model. ...
Reproducible, understandable models that can be reused and combined to true multi-scale systems are required to solve the present and future challenges of systems biology. However, many mathematical models are still built for a single purpose and reusing them in a different context can be challenging due to an inflexible monolithic structure, confusing code, missing documentation or other issues. These challenges are very similar to those faced in the engineering of large software systems. It is therefore likely that addressing model design at the software engineering level will also be beneficial in systems biology. To do this, researchers cannot just rely on using an accepted standard language. They need to be aware of the characteristics that make this language desirable and they need guidelines on how to utilize them to make their models more reproducible, understandable, reusable, and extensible. Drawing upon our experience with translating and extending a model of the human baroreflex, we therefore propose a list of desirable language characteristics and provide guidelines and examples for incorporating them in a model: In our opinion, a mathematical modeling language used in systems biology should be modular, human-readable, hybrid (i.e. support multiple formalisms), open, declarative, and support the graphical representation of models. We compare existing modeling languages with respect to these characteristics and show that there is no single best language but that trade-offs always have to be considered. We also illustrate the benefits of the individual language characteristics by translating a monolithic model of the human cardiac conduction system to a modular version using the modeling language Modelica as an example. Our experiment can be seen as emblematic for model reuse in a multi-scale setting. It illustrates how each characteristic, when applied consistently, can facilitate the reuse of the resulting model. We therefore recommend that modelers consider these criteria when choosing a programming language for any biological modeling task and hope that our work sparks a discussion about the importance of software engineering aspects in mathematical modeling languages.
... The diagrams are available in SBML format (Keating et al, 2020), allowing computational modelling of biological processes. SBML stores visual information about encoded elements and reactions using render (Bergmann et al, 2018) and layout (Gauges et al, 2015) packages. An early version of SBML adapted by CellDesigner allows storing layout and rendering information. ...
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective. Co-authors include: Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta-Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, FrancescoMessina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, Devasahayam Arokia Balaya Rex, Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic-Milacic, Andrea Senff-Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean-Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D’Eustachio, Julio Saez-Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider
... Other useful packages for the modeling community include the network layout and render package [15,41], which allows a biological pathway model encoded in SBML to be visualized in a reproducible way. Hoksza et al. [54] offer a comprehensive review of this particular topic. ...
Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu.
