SrivLab/Phytoplankton_dFBA: Phytoplankton dFBA Model
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| Formato: | Recurso digital |
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2025
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| _version_ | 1866901433979240448 |
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| author | Joseph Zavorskas Srivastava Lab |
| author_facet | Joseph Zavorskas Srivastava Lab |
| contents | <h2>Arctic Diatom Dynamic Flux Balance Analysis (dFBA) Models</h2> <p>Dynamic flux balance analysis models for simulating Arctic phytoplankton bloom dynamics and climate change impacts on marine carbon sequestration. This repository contains Python implementations of genome-scale metabolic models for both non-symbiotic (<em>Thalassiosira</em> sp.) and symbiotic (<em>Chaetoceros</em> sp. with <em>Anabaena</em> sp.) diatom communities.</p> <h3>Key Features</h3> <ul> <li>Temperature-dependent RuBisCo kinetics with Arctic-specific parameterization</li> <li>Location and time-dependent solar irradiance calculations using SMARTS2</li> <li>Symbiotic nutrient transfer modeling between diatoms and cyanobacteria</li> <li>Diatom succession pattern simulation (early <em>Thalassiosira</em> to late <em>Chaetoceros</em> blooms)</li> <li>Climate change scenario analysis for Arctic Ocean conditions</li> </ul> <h3>Applications</h3> <ul> <li>Predicting phytoplankton bloom timing, intensity, and carbon fixation under warming scenarios</li> <li>Evaluating ecosystem resilience mechanisms through diatom-cyanobacteria symbiosis</li> <li>Assessing climate change impacts on Arctic marine carbon sequestration</li> </ul> <p>The models integrate empirical environmental parameters with constraint-based metabolic modeling to simulate annual phytoplankton life cycles and their response to changing Arctic conditions.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15641316 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SrivLab/Phytoplankton_dFBA: Phytoplankton dFBA Model Joseph Zavorskas Srivastava Lab <h2>Arctic Diatom Dynamic Flux Balance Analysis (dFBA) Models</h2> <p>Dynamic flux balance analysis models for simulating Arctic phytoplankton bloom dynamics and climate change impacts on marine carbon sequestration. This repository contains Python implementations of genome-scale metabolic models for both non-symbiotic (<em>Thalassiosira</em> sp.) and symbiotic (<em>Chaetoceros</em> sp. with <em>Anabaena</em> sp.) diatom communities.</p> <h3>Key Features</h3> <ul> <li>Temperature-dependent RuBisCo kinetics with Arctic-specific parameterization</li> <li>Location and time-dependent solar irradiance calculations using SMARTS2</li> <li>Symbiotic nutrient transfer modeling between diatoms and cyanobacteria</li> <li>Diatom succession pattern simulation (early <em>Thalassiosira</em> to late <em>Chaetoceros</em> blooms)</li> <li>Climate change scenario analysis for Arctic Ocean conditions</li> </ul> <h3>Applications</h3> <ul> <li>Predicting phytoplankton bloom timing, intensity, and carbon fixation under warming scenarios</li> <li>Evaluating ecosystem resilience mechanisms through diatom-cyanobacteria symbiosis</li> <li>Assessing climate change impacts on Arctic marine carbon sequestration</li> </ul> <p>The models integrate empirical environmental parameters with constraint-based metabolic modeling to simulate annual phytoplankton life cycles and their response to changing Arctic conditions.</p> |
| title | SrivLab/Phytoplankton_dFBA: Phytoplankton dFBA Model |
| url | https://doi.org/10.5281/zenodo.15641316 |