☛ Mathematical models of microbial metabolism for sustainable biotechnological applications ([P1], [P6], [P9], [P10]).
☛ Modeling, optimization & automation of bioprocesses and biosystems ([P3], [P4], [P8], [P11]).
☛ Computational tools and methods (machine learning and data mining) to analyse big-datasets coming from diverse sources (e.g., (bio)chemical data [P2], environmental [P7] and clinical/biomedical data [P5]).
- [P1] J. Pinto, J. Ramos, R. S. Costa & R. Oliveira, A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard. AI, 4(1), 303-318, (2023), doi: 10.3390/ai4010014
- [P2] Alexandre, L., Costa, R.S. & Henriques, R. DI2: prior-free and multi-item discretization of biological data and its applications. BMC Bioinformatics, 22, 426 (2021), doi: 10.1186/s12859-021-04329-8
- [P3] Gonçalves, D., Henriques, R., Costa, R.S. Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches. Computational and Structural Biotechnology Journal, 21, 4960-4973, (2023), doi: 10.1016/j.csbj.2023.10.002
- [P4] J. Pinto, J. Ramos, R. S. Costa, S. Rossell, P. Dumas and R. Oliveira. Hybrid deep modeling of a CHO-K1 fed-batch process: combining First-Principles with deep neural networks. Frontiers in Bioengineering and Biotechnology, 11, (2023), doi: 10.3389/fbioe.2023.1237963
- [P5] Alexandre, L., Costa, R.S., Santos, L. L., Henriques, R. Mining pre-surgical patterns able to discriminate post-surgical outcomes in the oncological domain. IEEE Journal of Biomedical and Health Informatics, 25(7), (2021), doi: 10.1109/JBHI.2021.3064786
- [P6] Mochao, H., Barahona, P., Costa, R.S. KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling. Database-The journal of biological database & curation, baaa093, (2020), doi: 10.1093/database/baaa093
- [P7] Patrício, A., Lopes, M. B., Costa, P.R., Costa, R.S., Henriques, R., Vinga S. Time-Lagged Correlation Analysis of Shellfish Toxicity Reveals Predictive Links to Adjacent Areas, Species, and Environmental Conditions. Toxins, 14 (10), 679, (2022), doi: 10.3390/toxins14100679
- [P8] J. Pinto, M. Mestre, J. Ramos, R. S. Costa, G. Striedner, R. Oliveira A general deep hybrid model for bioreactor systems: combining First Principles with deep neural networks. Computers & Chemical Engineering. 165, 107952, (2022), doi: 10.1016/j.compchemeng.2022.107952
- [P9] Costa, R. S., Hartmann, A., Gaspar, P., Neves, A. R. and Vinga, S. An extended dynamic model of Lactococcus lactis metabolism for mannitol and 2,3-butanediol production. Molecular BioSystems 10, 628-639 (2014), doi: C3MB70265K
- [P10] Machado, D., Costa, R.S., Rocha, I., Tidor B., Ferreira, E.C. Exploring the gap between dynamic and constraint-based models of metabolism. Metabolic Engineering 14 (2), 112-119 (2012), doi: 10.1016/j.ymben.2012.01.003
- [P11] J. Pinto †, R. S. Costa* †, L. Alexandre, J. Ramos, R. Oliveira. SBML2HYB: a Python interface for SBML compatible hybrid modeling. Bioinformatics. 39(1), btad044, (2023), doi: bioinformatics/btad044