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., Troitiño-Jordedo, D., Henriques, D., Contreras-Ruíz, A., Minebois, R., Morard, M., Barrio, E., Querol, A., & Balsa-Canto, E. (2025). An integrated multiphase dynamic genome-scale model explains batch fermentations led by species of the Saccharomyces genus. MSystems, 10(2). https://doi.org/10.1128/msystems.01615-24

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

Pérez-Rodríguez, M., López Cabo, M., Balsa-Canto, E., & García, M. R. (2023). Mechanisms of Listeria monocytogenes Disinfection with Benzalkonium Chloride: From Molecular Dynamics to Kinetics of Time-Kill Curves. International Journal of Molecular Sciences, 24(15), 12132. https://doi.org/10.3390/ijms241512132

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

Le, T. T. Y., Grabner, D., Nachev, M., García, M. R., Balsa-Canto, E., Peijnenburg, W. J. G. M., Hendriks, A. J., & Sures, B. (2021). Development of a toxicokinetic-toxicodynamic model simulating chronic copper toxicity to the Zebra mussel based on subcellular fractionation. Aquatic Toxicology, 241, 106015. https://doi.org/10.1016/j.aquatox.2021.106015

Le, T. T. Y., Nachev, M., Grabner, D., Garcia, M. R., Balsa-Canto, E., Hendriks, A. J., Peijnenburg, W. J. G. M., & Sures, B. (2021). Modelling chronic toxicokinetics and toxicodynamics of copper in mussels considering ionoregulatory homeostasis and oxidative stress. Environmental Pollution, 287, 117645. https://doi.org/10.1016/j.envpol.2021.117645

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

Yen Le, T. T., García, M. R., Grabner, D., Nachev, M., Balsa-Canto, E., Hendriks, A. J., Zimmermann, S., & Sures, B. (2020). Mechanistic simulation of bioconcentration kinetics of waterborne Cd, Ag, Pd, and Pt in the zebra mussel Dreissena polymorpha. Chemosphere, 242, 124967. https://doi.org/10.1016/j.chemosphere.2019.124967

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

Tsiantis, N., Balsa-Canto, E., & Banga, J. R. (2018). Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics, 34(21), 3780–3780. https://doi.org/10.1093/bioinformatics/bty438

Tsiantis, N., Balsa-Canto, E., & Banga, J. R. (2018). Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics, 34(14), 2433–2440. https://doi.org/10.1093/bioinformatics/bty139

Le, T. T. Y., García, M. R., Nachev, M., Grabner, D., Balsa-Canto, E., Hendriks, A. J., & Sures, B. (2018). Development of a PBPK Model for Silver Accumulation in Chub Infected with Acanthocephalan Parasites. Environmental Science & Technology, 52(21), 12514–12525. https://doi.org/10.1021/acs.est.8b04022

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

Ligon, T. S., Fröhlich, F., Chiş, O. T., Banga, J. R., Balsa-Canto, E., & Hasenauer, J. (2017). GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models. Bioinformatics, 34(8), 1421–1423. https://doi.org/10.1093/bioinformatics/btx735

García, M. R., Alonso, A. A., & Balsa-Canto, E. (2017). A Normalisation Strategy to Optimally Design Experiments in Computational Biology. 11th International Conference on Practical Applications of Computational Biology & Bioinformatics, 126–136. https://doi.org/10.1007/978-3-319-60816-7_16

García, M. R., Cabo, M. L., Herrera, J. R., Ramilo-Fernández, G., Alonso, A. A., & Balsa-Canto, E. (2017). Smart sensor to predict retail fresh fish quality under ice storage. Journal of Food Engineering, 197, 87–97. https://doi.org/10.1016/j.jfoodeng.2016.11.006

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

Balsa-Canto, E., Henriques, D., Gábor, A., & Banga, J. R. (2016). AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology. Bioinformatics, 32(21), 3357–3359. https://doi.org/10.1093/bioinformatics/btw411

Villaverde, A. F., Bongard, S., Mauch, K., Balsa-Canto, E., & Banga, J. R. (2016). Metabolic engineering with multi-objective optimization of kinetic models. Journal of Biotechnology, 222, 1–8. https://doi.org/10.1016/j.jbiotec.2016.01.005

Chis, O.-T., Villaverde, A. F., Banga, J. R., & Balsa-Canto, E. (2016). On the relationship between sloppiness and identifiability. Mathematical Biosciences, 282, 147–161. https://doi.org/10.1016/j.mbs.2016.10.009

Balsa-Canto, E., Alonso, A. A., Arias-Méndez, A., García, M. R., López-Núñez, A., Mosquera-Fernández, M., Vázquez, C., & Vilas, C. (2016). Modeling and Optimization Techniques with Applications in Food Processes, Bio-processes and Bio-systems. Numerical Simulation in Physics and Engineering, 187–216. https://doi.org/10.1007/978-3-319-32146-2_4

Vilas, C., Arias-Méndez, A., García, M. R., Alonso, A. A., & Balsa-Canto, E. (2016). Toward predictive food process models: A protocol for parameter estimation. Critical Reviews in Food Science and Nutrition, 1–14. https://doi.org/10.1080/10408398.2016.1186591

de Hijas-Liste, G. M., Balsa-Canto, E., Ewald, J., Bartl, M., Li, P., Banga, J. R., & Kaleta, C. (2015). Optimal programs of pathway control: dissecting the influence of pathway topology and feedback inhibition on pathway regulation. BMC Bioinformatics, 16(1). https://doi.org/10.1186/s12859-015-0587-z

Villaverde, A. F., Henriques, D., Smallbone, K., Bongard, S., Schmid, J., Cicin-Sain, D., Crombach, A., Saez-Rodriguez, J., Mauch, K., Balsa-Canto, E., Mendes, P., Jaeger, J., & Banga, J. R. (2015). BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology. BMC Systems Biology, 9(1). https://doi.org/10.1186/s12918-015-0144-4

Villaverde, A. F., Bongard, S., Mauch, K., Müller, D., Balsa-Canto, E., Schmid, J., & Banga, J. R. (2015). A consensus approach for estimating the predictive accuracy of dynamic models in biology. Computer Methods and Programs in Biomedicine, 119(1), 17–28. https://doi.org/10.1016/j.cmpb.2015.02.001

García, M. R., Vilas, C., Herrera, J. R., Bernárdez, M., Balsa-Canto, E., & Alonso, A. A. (2015). Quality and shelf-life prediction for retail fresh hake (Merluccius merluccius). International Journal of Food Microbiology, 208, 65–74. https://doi.org/10.1016/j.ijfoodmicro.2015.05.012

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

Villaverde, A. F., Bongard, S., Mauch, K., Müller, D., Balsa-Canto, E., Schmid, J., & Banga, J. R. (2014). High-Confidence Predictions in Systems Biology Dynamic Models. 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014), 161–171. https://doi.org/10.1007/978-3-319-07581-5_20

de Hijas-Liste, G. M., Klipp, E., Balsa-Canto, E., & Banga, J. R. (2014). Global dynamic optimization approach to predict activation in metabolic pathways. BMC Systems Biology, 8(1). https://doi.org/10.1186/1752-0509-8-1

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

Becker, K., Balsa-Canto, E., Cicin-Sain, D., Hoermann, A., Janssens, H., Banga, J. R., & Jaeger, J. (2013). Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster. PLoS Computational Biology, 9(10), e1003281. https://doi.org/10.1371/journal.pcbi.1003281

de Hijas-Liste, G. M., Balsa-Canto, E., Banga, J. R., & Kaleta, C. (2013). Optimal regulatory programs for the control of metabolic pathways: The case of feedback inhibition. 21st Mediterranean Conference on Control and Automation, 237–242. https://doi.org/10.1109/med.2013.6608728

García, M. G., Balsa‐Canto, E., Alonso, A. A., & Banga, J. R. (2013). Dynamic Optimization of Nonlinear Bioreactors. Taming Heterogeneity and Complexity of Embedded Control, 307–327. Portico. https://doi.org/10.1002/9780470612217.ch18

Alonso, A. A., Arias-Méndez, A., Balsa-Canto, E., García, M. R., Molina, J. I., Vilas, C., & Villafín, M. (2013). Real time optimization for quality control of batch thermal sterilization of prepackaged foods. Food Control, 32(2), 392–403. https://doi.org/10.1016/j.foodcont.2013.01.002

Vilas, C., Balsa-Canto, E., Banga, J. R., & Alonso, A. A. (2012). Robust and efficient numerical methods for the optimal control of spatially distributed biological systems. 2012 20th Mediterranean Conference on Control & Automation (MED), 163–168. https://doi.org/10.1109/med.2012.6265632

Vilas, C., Balsa-Canto, E., García, M.-S. G., Banga, J. R., & Alonso, A. A. (2012). Dynamic optimization of distributed biological systems using robust and efficient numerical techniques. BMC Systems Biology, 6(1). https://doi.org/10.1186/1752-0509-6-79

Villaverde, A. F., Ross, J., Morán, F., Balsa-Canto, E., & Banga, J. R. (2011). Use of a Generalized Fisher Equation for Global Optimization in Chemical Kinetics. The Journal of Physical Chemistry A, 115(30), 8426–8436. https://doi.org/10.1021/jp203158r

Chis, O.-T., Banga, J. R., & Balsa-Canto, E. (2011). Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods. PLoS ONE, 6(11), e27755. https://doi.org/10.1371/journal.pone.0027755

De Hijas-Liste, G. M., Balsa-Canto, E., & Banga, J. R. (2011). Prediction of activation of metabolic pathways via dynamic optimization. 21st European Symposium on Computer Aided Process Engineering, 1386–1390. https://doi.org/10.1016/b978-0-444-54298-4.50056-8

Chis, O., Banga, J. R., & Balsa-Canto, E. (2011). Methods for checking structural identifiability of nonlinear biosystems: A critical comparison. IFAC Proceedings Volumes, 44(1), 10585–10590. https://doi.org/10.3182/20110828-6-it-1002.00800

Chiş, O., Banga, J. R., & Balsa-Canto, E. (2011). GenSSI: a software toolbox for structural identifiability analysis of biological models. Bioinformatics, 27(18), 2610–2611. https://doi.org/10.1093/bioinformatics/btr431

Balsa-Canto, E., & Banga, J. R. (2011). AMIGO, a toolbox for advanced model identification in systems biology using global optimization. Bioinformatics, 27(16), 2311–2313. https://doi.org/10.1093/bioinformatics/btr370

Balsa-Canto, E., Banga, J. R., Egea, J. A., Fernandez-Villaverde, A., & de Hijas-Liste, G. M. (2011). Global Optimization in Systems Biology: Stochastic Methods and Their Applications. Advances in Systems Biology, 409–424. https://doi.org/10.1007/978-1-4419-7210-1_24

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

Vera, J., Rath, O., Balsa-Canto, E., Banga, J. R., Kolch, W., & Wolkenhauer, O. (2010). Investigating dynamics of inhibitory and feedback loops in ERK signalling using power-law models. Molecular BioSystems, 6(11), 2174. https://doi.org/10.1039/c0mb00018c

Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2010). An iterative identification procedure for dynamic modeling of biochemical networks. BMC Systems Biology, 4(1). https://doi.org/10.1186/1752-0509-4-11

Balsa-Canto, E., & Banga, J. R. (2010). AMIGO: A model identification toolbox based on global optimization and its applications in biosystems. IFAC Proceedings Volumes, 43(6), 132–137. https://doi.org/10.3182/20100707-3-be-2012.0053

Egea, J. A., Balsa-Canto, E., García, M.-S. G., & Banga, J. R. (2009). Dynamic Optimization of Nonlinear Processes with an Enhanced Scatter Search Method. Industrial & Engineering Chemistry Research, 48(9), 4388–4401. https://doi.org/10.1021/ie801717t

Hirmajer, T., Balsa-Canto, E., & Banga, J. R. (2009). DOTcvpSB, a software toolbox for dynamic optimization in systems biology. BMC Bioinformatics, 10(1). https://doi.org/10.1186/1471-2105-10-199

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

Banga, J. R., Balsa‐Canto, E., & Alonso, A. A. (2008). Quality and Safety Models and Optimization as Part of Computer‐Integrated Manufacturing. Comprehensive Reviews in Food Science and Food Safety, 7(1), 168–174. Portico. https://doi.org/10.1111/j.1541-4337.2007.00023.x

Banga, J. R., & Balsa-Canto, E. (2008). Parameter estimation and optimal experimental design. Essays in Biochemistry, 45, 195–210. https://doi.org/10.1042/bse0450195

Balsa-Canto, E., Peifer, M., Banga, J. R., Timmer, J., & Fleck, C. (2008). Hybrid optimization method with general switching strategy for parameter estimation. BMC Systems Biology, 2(1). https://doi.org/10.1186/1752-0509-2-26

BALSA‐CANTO, E., ALONSO, A. A., & BANGA, J. R. (2008). COMPUTING OPTIMAL DYNAMIC EXPERIMENTS FOR MODEL CALIBRATION IN PREDICTIVE MICROBIOLOGY. Journal of Food Process Engineering, 31(2), 186–206. Portico. https://doi.org/10.1111/j.1745-4530.2007.00147.x

Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2008). Computational procedures for optimal experimental design in biological systems. IET Systems Biology, 2(4), 163–172. https://doi.org/10.1049/iet-syb:20070069

Vera, J., Balsa-Canto, E., Wellstead, P., Banga, J. R., & Wolkenhauer, O. (2007). Power-law models of signal transduction pathways. Cellular Signalling, 19(7), 1531–1541. https://doi.org/10.1016/j.cellsig.2007.01.029

Balsa-Canto, E., Rodriguez-Fernandez, M., & Banga, J. R. (2007). Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation. Journal of Food Engineering, 82(2), 178–188. https://doi.org/10.1016/j.jfoodeng.2007.02.006

García, M.-S. G., Balsa-Canto, E., Wouwer, A. V., & Banga, J. R. (2007). OPTIMAL CONTROL of the SIMULATED MOVING BED (SMB) CHROMATOGRAPHIC SEPARATION PROCESS. IFAC Proceedings Volumes, 40(5), 183–188. https://doi.org/10.3182/20070606-3-mx-2915.00029

Rodríguez-Fernández, M., Balsa-Canto, E., Egea, J. A., & Banga, J. R. (2007). Identifiability and robust parameter estimation in food process modeling: Application to a drying model. Journal of Food Engineering, 83(3), 374–383. https://doi.org/10.1016/j.jfoodeng.2007.03.023

García, M.-S. G., Balsa-Canto, E., Banga, J. R., & Vande Wouwer, A. (2006). Dynamic Optimization of a Simulated Moving Bed (SMB) Chromatographic Separation Process. Industrial & Engineering Chemistry Research, 45(26), 9033–9041. https://doi.org/10.1021/ie060576i

García, M.-S. G., Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2006). Computing optimal operating policies for the food industry. Journal of Food Engineering, 74(1), 13–23. https://doi.org/10.1016/j.jfoodeng.2005.02.011

Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2005). Dynamic Optimization of Complex Distributed Process Systems. Chemical Engineering Research and Design, 83(6), 724–729. https://doi.org/10.1205/cherd.04378

Banga, J. R., Balsa-Canto, E., Moles, C. G., & Alonso, A. A. (2005). Dynamic optimization of bioprocesses: Efficient and robust numerical strategies. Journal of Biotechnology, 117(4), 407–419. https://doi.org/10.1016/j.jbiotec.2005.02.013

García, M. R., Balsa-Canto, E., Vilas, C., Banga, J. R., & Alonso, A. A. (2005). An efficient real-time dynamic optimization architecture for the control of non-isothermal tubular reactors. European Symposium on Computer-Aided Process Engineering-15, 38th European Symposium of the Working Party on Computer Aided Process Engineering, 1333–1338. https://doi.org/10.1016/s1570-7946(05)80064-1

García, M.-S. G., Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2005). A software toolbox for the dynamic optimization of nonlinear processes. European Symposium on Computer-Aided Process Engineering-15, 38th European Symposium of the Working Party on Computer Aided Process Engineering, 121–126. https://doi.org/10.1016/s1570-7946(05)80142-7

Vilas, C., García, M. R., Fernández, M. R., Balsa-Canto, E., Banga, J. R., & Alonso, A. A. (2004). On systematic model reduction techniques for dynamic optimization and robust control of distributed process systems. European Symposium on Computer-Aided Process Engineering-14, 37th European Symposium of the Working Party on Computer-Aided Process Engineering, 841–846. https://doi.org/10.1016/s1570-7946(04)80206-2

Banga, J. R., Balsa-Canto, E., Moles, C. G., & Alonso, A. A. (2003). Improving food processing using modern optimization methods. Trends in Food Science & Technology, 14(4), 131–144. https://doi.org/10.1016/s0924-2244(03)00048-7

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. (2002). Restricted second order information for the solution of optimal control problems using control vector parameterization. Journal of Process Control, 12(2), 243–255. https://doi.org/10.1016/s0959-1524(01)00008-7

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

Vassiliadis, V. S., Canto, E. B., & Banga, J. R. (1999). Second-order sensitivities of general dynamic systems with application to optimal control problems. Chemical Engineering Science, 54(17), 3851–3860. https://doi.org/10.1016/s0009-2509(98)00432-1

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.

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