Applied Systems Biology Publications

The complete list of publications can be found at Google Scholar
Here you have some selected publications

• Moimenta A, Troitiño-Jordedo D, Henriques D, Contreras-Ruíz A, Minebois R, Morar M, Barrio E, Querol A, Balsa-Canto E. An integrated multiphase dynamic genome-scale model explain batch fermentations led by species of the Saccharomyces genus. mSystems (2025) e0161524. doi: 10.1128/msystems.01615-24.
• Scott, W. T., Henriques, D., Smid, E. J., Notebaart, R. A., & Balsa-Canto, E.  Dynamic genome-scale modeling of Saccharomyces cerevisiae unravels mechanisms for ester formation during alcoholic fermentation. (2023) Biotechnology and Bioengineering, 1– 15. https://doi.org/10.1002/bit.28421
• Moimenta A.R, Henriques D, Minebois R, Querol A, Balsa-Canto E. Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism. (2023) Microbial Biotechnology, 00, 1– 15. https://doi.org/10.1111/1751-7915.14211
• Henriques, D., Balsa-Canto, E. The Monod Model is insufficient to explain biomass growth in nitrogen-limited yeast fermentation (2021) App& Environ Microbiol, 87 (20), 1-22. https://journals.asm.org/doi/10.1128/AEM.01084-21
• Henriques, D., Minebois, R., Mendoza, S.N., Macías, L.G., Pérez-Torrado, R., Barrio, E., Teusink, B., Querol, A., Balsa-Canto, E. A multiphase multiobjective dynamic genome-scale model shows different redox balancing among yeast species of the Saccharomyces genus in fermentation (2021) mSystems, 6 (4), art. no. e00260-21. https://doi.org/10.1128/mSystems.00260-21
• Balsa-Canto, E., López-Núñez, A., Vázquez, C. A two-dimensional multi-species model for different Listeria monocytogenes biofilm structures and its numerical simulation (2020) App Math & Comput, 384, art. no. 125383. https://doi.org/10.1016/j.amc.2020.125383
• Henriques, D., Alonso-del-Real, J., Querol, A., Balsa-Canto, E. Saccharomyces cerevisiae and S. kudriavzevii synthetic wine fermentation performance dissected by predictive modeling (2018) Front Microbiol, 9, art. no. 88. https://doi.org/10.3389/fmicb.2018.00088
• Balsa-Canto, E., Vilas, C., López-Núñez, A., Mosquera-Fernández, M., Briandet, R., Cabo, M.L., Vázquez, C. Modeling reveals the role of aging and glucose uptake impairment in L1A1 Listeria monocytogenes biofilm life cycle 2017) Front Microbiol, 8, art. no. 2118. https://doi.org/10.3389/fmicb.2017.02118
• de Hijas-Liste, G.M., Klipp, E., Balsa-Canto, E., Banga, J.R. Global dynamic optimization approach to predict activation in metabolic pathways (2014) BMC Syst Biol, 8(1), art. no. 1. https://doi.org/10.1186/1752-0509-8-1

• Balsa-Canto, E., Alonso-del-Real, J., Querol, A. Temperature Shapes Ecological Dynamics in Mixed Culture Fermentations Driven by Two Species of the Saccharomyces Genus (2020) Front Bioeng & Biotechnol, 8, art. no. 915. https://doi.org/10.3389/fbioe.2020.00915
• Balsa-Canto, E., Alonso-Del-Real, J., Querol, A. Mixed growth curve data do not suffice to fully characterize the dynamics of mixed cultures (2020) PNAS, 117 (2), pp. 811-813. https://doi.org/10.1073/pnas.1916774117

• Menolascina, F., Bandiera, L., Gomez-Cabeza, D., Gilman, J., Balsa-Canto, E. Optimally designed model selection for synthetic biology (2020) ACS Synth Biol, 9 (11), pp. 3134-3144. https://doi.org/10.1021/acssynbio.0c00393
• Bandiera, L., Hou, Z., Kothamachu, V.B., Balsa-Canto, Ehttps://doi.org/10.1093/bioinformatics/btx735., Swain, P.S., Menolascina, F. On-line optimal input design increases the efficiency and accuracy of the modelling of an inducible synthetic promoter (2018) Processes, 6 (9), art. no. 148. https://doi.org/10.3390/pr6090148
• Tsiantis, N., Balsa-Canto, E., Banga, J.R. Optimality and identification of dynamic models in systems biology: An inverse optimal control framework (2018) Bioinformatics, 34 (14), pp. 2433-2440. https://doi.org/10.1093/bioinformatics/bty139
• Ligon, T.S., Fröhlich, F., Chiş, O.T., Banga, J.R., Balsa-Canto, E., Hasenauer, J. GenSSI 2.0: Multi-experiment structural identifiability analysis of SBML models (2018) Bioinformatics, 34 (8), pp. 1421-1423. https://doi.org/10.1093/bioinformatics/btx735
• Balsa-Canto, E., López-Núñez, A., Vázquez, C. Numerical methods for a nonlinear reaction–diffusion system modelling a batch culture of biofilm (2017) App Math Mod, 41, 164-179. https://doi.org/10.1016/j.apm.2016.08.020
• Chis, O.-T., Villaverde, A.F., Banga, J.R., Balsa-Canto, E. On the relationship between sloppiness and identifiability(2016) Math Biosci, 282, pp. 147-161. https://doi.org/10.1016/j.mbs.2016.10.009
• Balsa-Canto, E., Henriques, D., Gábor, A., Banga, J.R. AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology (2016) Bioinformatics, 32 (21), pp. 3357-3359. https://doi.org/10.1093/bioinformatics/btw411

• Vilas, C., Arias-Méndez, A., García, M.R., Alonso, A.A., Balsa-Canto, E. Toward predictive food process models: A protocol for parameter estimation (2018) Crit Rev Food Sci & Nut, 58 (3), pp. 436-449. https://doi.org/10.1080/10408398.2016.1186591
• García, M.R., Cabo, M.L., Herrera, J.R., Ramilo-Fernández, G., Alonso, A.A., Balsa-Canto, E. Smart sensor to predict retail fresh fish quality under ice storage (2017) J Food Eng, 197, pp. 87-97. https://doi.org/10.1016/j.jfoodeng.2016.11.006
• Villaverde, A.F., Bongard, S., Mauch, K., Balsa-Canto, E., Banga, J.R. Metabolic engineering with multi-objective optimization of kinetic models.(2016) J Biotechnol, 222, pp. 1-8. https://doi.org/10.1016/j.jbiotec.2016.01.005
• Arias-Mendez, A., Vilas, C., Alonso, A.A., Balsa-Canto, E. Time-temperature integrators as predictive temperature sensors (2014) Food Control, 44, pp. 258-266. https://doi.org/10.1016/j.foodcont.2014.04.001