Publications

From microbiome composition to functional engineering, one step at a time
Burz, S. D., Causevic, S., Dal Co, A., Dmitrijeva, M., Vonaesch, P., Vorholt, J., et al. (2023).
https://doi.org/10.1128/mmbr.00063-23
Metabolic interaction models recapitulate leaf microbiota ecology
Schäfer, M., Pacheco, A., Künzler, R., Bortfeld-Miller, M., Field, C. M., Vayena, E., Hatzimanikatis, V., Vorholt, J. (2023).
https://doi.org/10.3929/ethz-b-000621167
Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations
Sahin, A., Weilandt, D. R., Hatzimanikatis, V. (2023).
https://doi.org/10.1038/s41467-023-38159-4
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).
https://doi.org/10.1093/bioinformatics/btac787
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).
https://doi.org/10.1073/pnas.2211197119
Computational tools and resources for designing new pathways to small molecules
Sveshnikova, A., Mohammadi Peyhani, H., Hatzimanikatis, V. (2022).
https://doi.org/10.1016/j.copbio.2022.102722
ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds
Sveshnikova, A., Mohammadi Peyhani, H., Hatzimanikatis, V. (2022).
https://doi.org/10.1016/j.ymben.2022.03.013
Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx
Mohammadi Peyhani, H., Hafner, J., Svshenikova, A., Viterbo, V., Hatzimanikatis, V. (2022).
https://doi.org/10.1038/s41467-022-29238-z
The influence of the crowding assumptions in biofilm simulations
Angeles-Martinez, L., Hatzimanikatis, V. (2021).
https://doi.org/10.1371/journal.pcbi.1009158
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