Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models
Weilandt, D. R., Salvy, P., Masid, M., Fengos, G., Hatzimanikatis, V., et al. (2023).
A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions
Vayena, E., Chiappino-Pepe, A., MohammadiPeyhani, H., Francioli, Y., Hatzimanikatis, V., et al. (2022).
Computational tools and resources for designing new pathways to small molecules
Sveshnikova, A., Mohammadi Peyhani, H., Hatzimanikatis, V. (2022).
ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds
Sveshnikova, A., Mohammadi Peyhani, H., Hatzimanikatis, V. (2022).
Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx
Mohammadi Peyhani, H., Hafner, J., Svshenikova, A., Viterbo, V., Hatzimanikatis, V. (2022).
The influence of the crowding assumptions in biofilm simulations
Angeles-Martinez, L., Hatzimanikatis, V. (2021).
Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities
Angeles-Martinez, L., Hatzimanikatis, V. (2021).