kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models
Abstract Background Computational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boo...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2023
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| Online Access: | https://hdl.handle.net/1721.1/150930 |
| _version_ | 1826199878018007040 |
|---|---|
| author | Carretero Chavez, Willow Krantz, Marcus Klipp, Edda Kufareva, Irina |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Carretero Chavez, Willow Krantz, Marcus Klipp, Edda Kufareva, Irina |
| author_sort | Carretero Chavez, Willow |
| collection | MIT |
| description | Abstract
Background
Computational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon (“reaction-contingency”) formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called “combinatorial explosion” of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to support rxncon model development, especially for large, intricate systems.
Results
We present the kboolnet toolkit (
https://github.com/Kufalab-UCSD/kboolnet
, complete documentation at
https://github.com/Kufalab-UCSD/kboolnet/wiki
), an R package and a set of scripts that seamlessly integrate with the python-based rxncon software and collectively provide a complete workflow for the verification, validation, and visualization of rxncon models. The verification script VerifyModel.R checks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. In particular, ScoreNet.R compares model predictions to a cloud-stored MIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entire kboolnet toolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined “modules”.
Conclusion
The kboolnet toolkit provides a modular, cloud-enabled workflow for the development of rxncon models, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using the rxncon formalism in the future. |
| first_indexed | 2024-09-23T11:27:14Z |
| format | Article |
| id | mit-1721.1/150930 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:20:07Z |
| publishDate | 2023 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1509302024-11-05T15:53:28Z kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models Carretero Chavez, Willow Krantz, Marcus Klipp, Edda Kufareva, Irina Massachusetts Institute of Technology. Department of Biology Abstract Background Computational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon (“reaction-contingency”) formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called “combinatorial explosion” of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to support rxncon model development, especially for large, intricate systems. Results We present the kboolnet toolkit ( https://github.com/Kufalab-UCSD/kboolnet , complete documentation at https://github.com/Kufalab-UCSD/kboolnet/wiki ), an R package and a set of scripts that seamlessly integrate with the python-based rxncon software and collectively provide a complete workflow for the verification, validation, and visualization of rxncon models. The verification script VerifyModel.R checks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. In particular, ScoreNet.R compares model predictions to a cloud-stored MIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entire kboolnet toolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined “modules”. Conclusion The kboolnet toolkit provides a modular, cloud-enabled workflow for the development of rxncon models, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using the rxncon formalism in the future. 2023-06-20T19:40:44Z 2023-06-20T19:40:44Z 2023-06-12 2023-06-18T03:10:40Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/150930 BMC Bioinformatics. 2023 Jun 12;24(1):246 PUBLISHER_CC en https://doi.org/10.1186/s12859-023-05329-6 Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf BioMed Central BioMed Central |
| spellingShingle | Carretero Chavez, Willow Krantz, Marcus Klipp, Edda Kufareva, Irina kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models |
| title | kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models |
| title_full | kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models |
| title_fullStr | kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models |
| title_full_unstemmed | kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models |
| title_short | kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models |
| title_sort | kboolnet a toolkit for the verification validation and visualization of reaction contingency rxncon models |
| url | https://hdl.handle.net/1721.1/150930 |
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