Articles | Volume 16, issue 6
https://doi.org/10.5194/gmd-16-1683-2023
https://doi.org/10.5194/gmd-16-1683-2023
Model description paper
 | 
27 Mar 2023
Model description paper |  | 27 Mar 2023

CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics

Heewon Jung, Hyun-Seob Song, and Christof Meile

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Cited articles

Alemani, D., Chopard, B., Galceran, J., and Buffle, J.: LBGK method coupled to time splitting technique for solving reaction-diffusion processes in complex systems, Phys. Chem. Chem. Phys., 7, 3331–3341, https://doi.org/10.1039/b505890b, 2005. 
Ataman, M. and Hatzimanikatis, V.: lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites, PLOS Comput. Biol., 13, 1–21, https://doi.org/10.1371/journal.pcbi.1005513, 2017. 
Ataman, M., Hernandez Gardiol, D. F., Fengos, G., and Hatzimanikatis, V.: redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models, PLOS Comput. Biol., 13, 1–22, https://doi.org/10.1371/journal.pcbi.1005444, 2017. 
Bauer, E., Zimmermann, J., Baldini, F., Thiele, I., and Kaleta, C.: BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities, PLOS Comput. Biol., 13, 1–22, https://doi.org/10.1371/journal.pcbi.1005544, 2017. 
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Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
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