Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions
Modeling complex chemical reaction networks has inspired a considerable body of research and a variety of approaches to modeling nonlinear pathways are being developed. Here, a general methodology is formulated to convert an arbitrary reaction network into its equivalent electrical analog. The topol...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Chemical Engineering Journal Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682112200134X |
| _version_ | 1811185500126445568 |
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| author | Sarang S. Nath John Villadsen |
| author_facet | Sarang S. Nath John Villadsen |
| author_sort | Sarang S. Nath |
| collection | DOAJ |
| description | Modeling complex chemical reaction networks has inspired a considerable body of research and a variety of approaches to modeling nonlinear pathways are being developed. Here, a general methodology is formulated to convert an arbitrary reaction network into its equivalent electrical analog. The topological equivalence of the electrical analog is mathematically established for unimolecular reactions using Kirchhoff's laws. The modular approach is generalized to bimolecular and nonlinear autocatalytic reactions. It is then applied to simulate the dynamics of nonlinear autocatalytic networks without making simplifying assumptions, such as use of the quasi-steady state/Bodenstein approximation or the absence of nonlinear steps in the intermediates. This is among the few papers that quantify the dynamics of a nonlinear chemical reaction network by generating and simulating an electrical network analog. As a realistic biological application, the early phase of the spread of COVID-19 is modeled as an autocatalytic process and the predicted dynamics are in good agreement with experimental data. The rate-limiting step of viral transmission is identified, leading to novel mechanistic insights. |
| first_indexed | 2024-04-11T13:30:26Z |
| format | Article |
| id | doaj.art-d9df1730306c446fbe63f6a5028ed277 |
| institution | Directory Open Access Journal |
| issn | 2666-8211 |
| language | English |
| last_indexed | 2024-04-11T13:30:26Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chemical Engineering Journal Advances |
| spelling | doaj.art-d9df1730306c446fbe63f6a5028ed2772022-12-22T04:21:50ZengElsevierChemical Engineering Journal Advances2666-82112022-11-0112100374Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactionsSarang S. Nath0John Villadsen1The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark; Corresponding author.Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby 2800, DenmarkModeling complex chemical reaction networks has inspired a considerable body of research and a variety of approaches to modeling nonlinear pathways are being developed. Here, a general methodology is formulated to convert an arbitrary reaction network into its equivalent electrical analog. The topological equivalence of the electrical analog is mathematically established for unimolecular reactions using Kirchhoff's laws. The modular approach is generalized to bimolecular and nonlinear autocatalytic reactions. It is then applied to simulate the dynamics of nonlinear autocatalytic networks without making simplifying assumptions, such as use of the quasi-steady state/Bodenstein approximation or the absence of nonlinear steps in the intermediates. This is among the few papers that quantify the dynamics of a nonlinear chemical reaction network by generating and simulating an electrical network analog. As a realistic biological application, the early phase of the spread of COVID-19 is modeled as an autocatalytic process and the predicted dynamics are in good agreement with experimental data. The rate-limiting step of viral transmission is identified, leading to novel mechanistic insights.http://www.sciencedirect.com/science/article/pii/S266682112200134XAutocatalysisNetwork theoryModeling and computer simulationElectrical circuitsChemical kineticsNonlinear dynamics |
| spellingShingle | Sarang S. Nath John Villadsen Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions Chemical Engineering Journal Advances Autocatalysis Network theory Modeling and computer simulation Electrical circuits Chemical kinetics Nonlinear dynamics |
| title | Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions |
| title_full | Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions |
| title_fullStr | Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions |
| title_full_unstemmed | Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions |
| title_short | Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions |
| title_sort | modeling dynamics of chemical reaction networks using electrical analogs application to autocatalytic reactions |
| topic | Autocatalysis Network theory Modeling and computer simulation Electrical circuits Chemical kinetics Nonlinear dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S266682112200134X |
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