Research group

Biosystems Modeling, Computational models and simulations, Ecological modelling, Machine Learning in Biology, Metabolic modelling

Eva Balsa-Canto, Nùria Campo-Manzanares, Artai R. Moimenta, Geoffrey Roudaut, Diego Troitiño-Jordedo, Quantifying and managing uncertainty in systems biology: Mechanistic and data-driven models, Current Opinion in Systems Biology, Volume 42, 2025, 100557, https://doi.org/10.1016/j.coisb.2025.100557.

Minebois, R., Henriques, D., Balsa‐Canto, E., Querol, A., & Camarasa, C. (2025). Combined Isotopic Tracer and Modelling Approach Reveals Differences in Nitrogen Metabolism in S. cerevisiae, S. uvarum and S. kudriavzevii Species. Microbial Biotechnology, 18(4). Portico. https://doi.org/10.1111/1751-7915.70087

Moimenta, A. R., Minebois, R., Henriques, D., Querol, A., & Balsa-Canto, E. (2025). Temperature-Dependent Kinetic Modeling of Nitrogen-Limited Batch Fermentation by Yeast Species. Mathematics, 13(9), 1373. https://doi.org/10.3390/math13091373

Moimenta, A. R., Henriques, D., Minebois, R., Querol, A., & Balsa‐Canto, E. (2023). Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism. Microbial Biotechnology, 16(4), 847–861. Portico. https://doi.org/10.1111/1751-7915.14211

Henriques, D., Minebois, R., dos Santos, D., Barrio, E., Querol, A., & Balsa-Canto, E. (2023). A Dynamic Genome-Scale Model Identifies Metabolic Pathways Associated with Cold Tolerance in Saccharomyces kudriavzevii. Microbiology Spectrum, 11(3). https://doi.org/10.1128/spectrum.03519-22

Scott, W. T., Henriques, D., Smid, E. J., Notebaart, R. A., & Balsa‐Canto, E. (2023). Dynamic genome‐scale modeling of Saccharomyces cerevisiae unravels mechanisms for ester formation during alcoholic fermentation. Biotechnology and Bioengineering, 120(7), 1998–2012. Portico. https://doi.org/10.1002/bit.28421

Henriques, D., & Balsa-Canto, E. (2021). The Monod Model Is Insufficient To Explain Biomass Growth in Nitrogen-Limited Yeast Fermentation. Applied and Environmental Microbiology, 87(20). https://doi.org/10.1128/aem.01084-21

Balsa-Canto, E., Bandiera, L., & Menolascina, F. (2021). Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2. Synthetic Gene Circuits, 221–239. https://doi.org/10.1007/978-1-0716-1032-9_11

Henriques, D., Minebois, R., Mendoza, S. N., Macías, L. G., Pérez-Torrado, R., Barrio, E., Teusink, B., Querol, A., & Balsa-Canto, E. (2021). A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of the Saccharomyces Genus in Fermentation. MSystems, 6(4). https://doi.org/10.1128/msystems.00260-21

Bandiera, L., Gomez-Cabeza, D., Balsa-Canto, E., & Menolascina, F. (2021). A Cyber-Physical Platform for Model Calibration. Synthetic Gene Circuits, 241–265. https://doi.org/10.1007/978-1-0716-1032-9_12

Balsa-Canto, E., Alonso-del-Real, J., & Querol, A. (2020). Temperature Shapes Ecological Dynamics in Mixed Culture Fermentations Driven by Two Species of the Saccharomyces Genus. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.00915

Bandiera, L., Gomez-Cabeza, D., Gilman, J., Balsa-Canto, E., & Menolascina, F. (2020). Optimally Designed Model Selection for Synthetic Biology. ACS Synthetic Biology, 9(11), 3134–3144. https://doi.org/10.1021/acssynbio.0c00393

Vilas, C., A. Alonso, A., Balsa-Canto, E., López-Quiroga, E., & Trelea, I. C. (2020). Model-Based Real Time Operation of the Freeze-Drying Process. Processes, 8(3), 325. https://doi.org/10.3390/pr8030325

Balsa-Canto, E., López-Núñez, A., & Vázquez, C. (2020). A two-dimensional multi-species model for different Listeria monocytogenes biofilm structures and its numerical simulation. Applied Mathematics and Computation, 384, 125383. https://doi.org/10.1016/j.amc.2020.125383

Cabeza, D. G., Bandiera, L., Balsa-Canto, E., & Menolascina, F. (2019). Information content analysis reveals desirable aspects of in vivo experiments of a synthetic circuit. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1–8. https://doi.org/10.1109/cibcb.2019.8791449

Bandiera, L., Gomez Cabeza, D., Balsa-Canto, E., & Menolascina, F. (2019). Bayesian model selection in synthetic biology: factor levels and observation functions. IFAC-PapersOnLine, 52(26), 24–31. https://doi.org/10.1016/j.ifacol.2019.12.231

Balsa-Canto, E., Alonso-del-Real, J., & Querol, A. (2019). Mixed growth curve data do not suffice to fully characterize the dynamics of mixed cultures. Proceedings of the National Academy of Sciences, 117(2), 811–813. https://doi.org/10.1073/pnas.1916774117

Henriques, D., Alonso-del-Real, J., Querol, A., & Balsa-Canto, E. (2018). Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling. Frontiers in Microbiology, 9. https://doi.org/10.3389/fmicb.2018.00088

Bandiera, L., Kothamachu, V., Balsa-Canto, E., Swain, P. S., & Menolascina, F. (2018). Optimally designed vs intuition-driven inputs: the study case of promoter activity modelling. https://doi.org/10.1101/346379

Bandiera, L., Hou, Z., Kothamachu, V. B., Balsa-Canto, E., Swain, P. S., & Menolascina, F. (2018). On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter. Processes, 6(9), 148. https://doi.org/10.3390/pr6090148

Balsa-Canto, E., López-Núñez, A., & Vázquez, C. (2017). Numerical methods for a nonlinear reaction–diffusion system modelling a batch culture of biofilm. Applied Mathematical Modelling, 41, 164–179. https://doi.org/10.1016/j.apm.2016.08.020

Balsa-Canto, E., Vilas, C., López-Núñez, A., Mosquera-Fernández, M., Briandet, R., Cabo, M. L., & Vázquez, C. (2017). Modeling Reveals the Role of Aging and Glucose Uptake Impairment in L1A1 Listeria monocytogenes Biofilm Life Cycle. Frontiers in Microbiology, 8. https://doi.org/10.3389/fmicb.2017.02118

Mosquera-Fernández, M., Sanchez-Vizuete, P., Briandet, R., Cabo, M. L., & Balsa-Canto, E. (2016). Quantitative image analysis to characterize the dynamics of Listeria monocytogenes biofilms. International Journal of Food Microbiology, 236, 130–137. https://doi.org/10.1016/j.ijfoodmicro.2016.07.015

Arias-Mendez, A., Vilas, C., Alonso, A. A., & Balsa-Canto, E. (2014). Time–temperature integrators as predictive temperature sensors. Food Control, 44, 258–266. https://doi.org/10.1016/j.foodcont.2014.04.001

Mosquera-Fernández, M., Rodríguez-López, P., Cabo, M. L., & Balsa-Canto, E. (2014). Numerical spatio-temporal characterization of Listeria monocytogenes biofilms. International Journal of Food Microbiology, 182–183, 26–36. https://doi.org/10.1016/j.ijfoodmicro.2014.05.005

Arias-Mendez, A., Warning, A., Datta, A. K., & Balsa-Canto, E. (2013). Quality and safety driven optimal operation of deep-fat frying of potato chips. Journal of Food Engineering, 119(1), 125–134. https://doi.org/10.1016/j.jfoodeng.2013.05.001

Franco-Uría, A., Otero-Muras, I., Balsa-Canto, E., Alonso, A. A., & Roca, E. (2010). Generic parameterization for a pharmacokinetic model to predict Cd concentrations in several tissues of different fish species. Chemosphere, 79(4), 377–386. https://doi.org/10.1016/j.chemosphere.2010.02.010

Otero-Muras, I., Franco-Uría, A., Alonso, A. A., & Balsa-Canto, E. (2010). Dynamic multi-compartmental modelling of metal bioaccumulation in fish: Identifiability implications. Environmental Modelling & Software, 25(3), 344–353. https://doi.org/10.1016/j.envsoft.2009.08.009

Balsa‐Canto, E., Banga, J. R., & García, M. R. (2010). Dynamic Model Building Using Optimal Identification Strategies, with Applications in Bioprocess Engineering. Process Systems Engineering, 441–467. Portico. https://doi.org/10.1002/9783527631339.ch13

Alvarez-Vázquez, L. J., Balsa-Canto, E., & Martínez, A. (2008). Optimal design and operation of a wastewater purification system. Mathematics and Computers in Simulation, 79(3), 668–682. https://doi.org/10.1016/j.matcom.2008.04.013

Lopez, R., Balsa‐Canto, E., & Oñate, E. (2008). Neural networks for variational problems in engineering. International Journal for Numerical Methods in Engineering, 75(11), 1341–1360. Portico. https://doi.org/10.1002/nme.2304

Balsa-Canto, E., Banga, J. R., & Alonso, A. A. (2002). A novel, efficient and reliable method for thermal process design and optimization. Part II: applications. Journal of Food Engineering, 52(3), 235–247. https://doi.org/10.1016/s0260-8774(01)00111-x

Balsa-Canto, E., Banga, J. R., Alonso, A. A., & Vassiliadis, V. S. (2000). Dynamic optimization of chemical and biochemical processes using restricted second order information. European Symposium on Computer Aided Process Engineering-10, 481–486. https://doi.org/10.1016/s1570-7946(00)80082-6

• Modeling microbial systems at multiple scales
• Modeling intereactions in biological systems
• Mathematical methods and software tools for systems biology
• Applications in monitoring, design, optimization and control of bioprocesses