Publications

In preparation

Zapararte S, Marcellin E, Nielsen LK, Saa PA. Topologically-constrained sampling of thermodynamically feasible mass-balanced states in metabolic reaction networks.
Altamirano A, Tapia I, Acuña V, Garrido D, Saa PA. METACONE: A scalable framework for exploring the conversions cone of metabolic reaction networks.
Dazzarola C, Tighe R, González MO , Pérez-Correa R, Saa PA. A digital twin for the prediction of beer quality combining industrial process data and a phenomenological fermentation model.
Saa PA. Fast computation of metabolic steady states and dynamic properties of kinetic models.
Ribbeck M, Saa PA, Agosin E. AutoPAD: An automated intra and extracellular pH adjustment tool for generating contextualized mass- and charge-balanced metabolic models.

Under review

Saa PA, Drovandi C, Pettitt A, Nielsen LK. Bayesian parameter inference on the simplex: Convenient transformations and perturbation kernels for Sequential Monte Carlo sampling.
Silva-Andrade C, Hernández-Galaz S, Saa PA, Pérez-Rueda E, Garrido D, Martin A. A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information.
Elizondo B, Saa PA. Complex kinetic models predict β-carotene production and reveal flux limitations in recombinant Saccharomyces cerevisiae strains.

Book chapters

Saa PA (2022) Rational metabolic pathway prediction and design: Computational tools and their applications for yeast systems and synthetic biology. In: Darvishi Harzevili F. (eds.) Synthetic Biology of Yeasts. Springer, Cham. https://doi.org/10.1007/978-3-030-89680-5_1.
Mendoza S, Saa PA, Teusink B, Agosin E (2022) Metabolic modelling of wine fermentation at genome scale. In: Sonia Cortassa and Miguel Aon (eds.) Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols. Methods in Molecular Biology series, vol. 2399. Springer. https://doi.org/10.1007/978-1-0716-1831-8_16.

Conference papers

Márquez C, Saa P, Pérez-Correa JR (2024) Evaluación por simulación de estrategias de control automático para fermentadores industriales de cerveza. https://doi.org/10.1109/ICA-ACCA62622.2024.10766766. IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA), Santiago, Chile, 2024. 1-6.

Journal papers

Van den Bogaard S, Saa PA, Alter T (2024) Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control. https://doi.org/10.1093/bioinformatics/btae691. Bioinformatics. btae691.
Bárzaga-Martell L, Aguila-Camacho N, Ibáñez-Espinel F, Duarte-Mermoud M, Saa PA, Pérez-Correa R (2024) Fractional adaptive observer for variable structure high cell density fed-batch cultures. https://doi.org/10.1016/j.ifacol.2024.08.163. IFAC-PapersOnLine. 58(12): 37-42.
Ibáñez F, Puentes-Cantor H, Bárzaga-Martell L, Saa PA, Agosin E, Pérez-Correa R (2024) Reliable calibration and validation of phenomenological and hybrid models of high-cell-density fed-batch cultures subject to metabolic overflow. https://doi.org/10.1016/j.compchemeng.2024.108706. Computers & Chemical Engineering. 186: 108706.
Deantas-Jahn C, Mendoza S, Licona-Casani C, Orellana C, Saa PA (2024) Genome-scale metabolic modelling of the haloalkaliphilic bacterium Halomonas campaniensis provides insights into higher poly-hydroxybutyrate production under nitrogen limitation. https://doi.org/10.1007/s00253-024-13111-8. Applied Microbiology and Biotechnology. 108: 310.
Román L, Melis-Arcos F, Pröschle T, Saa PA, Garrido D (2024) Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis. https://doi.org/10.1128/msystems.00715-23. mSystems. 9: e00715-23.
Saa PA, Zapararte S, Drovandi CC, Nielsen LK (2024) LooplessFluxSampler: An efficient algorithm for sampling the loopless flux solution space of metabolic models. https://doi.org/10.1186/s12859-023-05616-2. BMC Bioinformatics. 25: 3.
Martin AJ, Riquelme E, Saa PA, Garrido D (2023) Importance of microbial interactions in colonization resistance and gut dysbiosis: role of Bifidobacterium. https://dx.doi.org/10.20517/mrr.2023.10. Microbiome Research Reports. 2:17.
Matos M, Saa PA, Cowie N, Volkova S, de Leeuw M, Nielsen LK (2022) GRASP: A computational platform for building kinetic models of cell metabolism. https://doi.org/10.1093/bioadv/vbac066. Bioinformatics Advances. vbac066.
Hirmas B, Gasaly N, Orellana G, Saa PA, Gotteland M, Garrido D (2022) Metabolic modeling and bidirectional culturing of two gut microbes reveal cross-feeding interactions and protective effects on intestinal cells. https://doi.org/10.1128/msystems.00646-22. mSystems. e00646-22.
Martínez VS, Saa PA, Jooste J, Tiwari K, Quek L, Nielsen LK (2022) The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility. https://doi.org/10.1371/journal.pcbi.1010203. PLOS Computational Biology. 18(6): e1010203.
Saa PA, Urrutia A, Silva-Andrade C, Martín AJ, Garrido D (2022) Modeling approaches for probing cross-feeding interactions in the gut microbiome. https://doi.org/10.1016/j.csbj.2021.12.006. Computational and Structural Biotechnology Journal. 20: 79-89.
Eyheramendy S, Saa PA, Undurraga E, Valencia C, Méndez L, Pizarro-Berdichevsky J, Finkelstein-Kulka A, Solari S, Salas N, Bahamondes P, Ugarte M, Barceló P, Arenas M, Agosin, E (2021) Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test. https://doi.org/10.1016/j.isci.2021.103419. iScience. 24(12): 103419.
Ibáñez-Espinel F, Saa PA, Bárzaga L, Duarte M, Fernández M, Agosin E, Pérez-Correa R (2021) Robust control of fed-batch high-cell density cultures: a simulation-based assessment. https://doi.org/10.1016/j.compchemeng.2021.107545. Computers & Chemical Engineering. 155: 107545.
Bárzaga L, Duarte-Mermoud M, Ibáñez-Espinel F, Gamboa-Labbé B, Saa PA, Pérez-Correa R (2021) A robust hybrid observer for monitoring high-cell density cultures exhibiting overflow metabolism. https://doi.org/10.1016/j.jprocont.2021.06.006. Journal of Process Control. 104: 112-125.
Altamirano A, Saa PA, Garrido D (2020) Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools. https://doi.org/10.1016/j.csbj.2020.11.035. Computational and Structural Biotechnology Journal. 18: 3897-3904.
López L, Bustos D, Conrado C, Arenas N, Saa PA, Agosin E (2020) Engineering Saccharomyces cerevisiae for the overproduction of β-ionone and its precursor β-carotene. https://doi.org/10.3389/fbioe.2020.578793. Frontiers in Bioengineering and Biotechnology. 8: 578793.
Cataldo V, Salgado V, Saa PA, Agosin E (2020) Genomic integration of unclonable gene expression cassettes in Saccharomyces cerevisiae using rapid cloning-free workflows. https://doi.org/10.1002/mbo3.978. MicrobiologyOpen. 00:e978.
Torres P, Saa PA, Albiol J, Ferrer P, Agosin E (2019) Contextualized genome-scale model unveils high-order metabolic effects of the specific growth rate and oxygenation level in recombinant Pichia pastoris. https://doi.org/10.1016/j.mec.2019.e00103. Metabolic Engineering Communications. 9: e00103.
López L, Cataldo V, Peña M, Saitua F, Ibaceta M, Saa PA, Agosin E (2019). Build your bioprocess on a solid strain-β-carotene production in recombinant Saccharomyces cerevisiae. https://doi.org/10.3389/fbioe.2019.00171. Frontiers in Bioengineering and Biotechnology. 7: 171.
Saa PA, Cortés M, López J, Bustos D, Maass A, Agosin E (2019) Expanding metabolic capabilities through novel pathway designs: computational tools and case studies. https://doi.org/10.1002/biot.201800734. Biotechnology Journal. 14: 1800734.
Dal’Molin C, Quek L, Saa PA, Payfreyman R, Nielsen LK (2018) From reconstruction to C4 metabolic engineering: a case study for overproduction of PHB in bioenergy grasses. https://doi.org/10.1016/j.plantsci.2018.03.027. Plant Science 273: 50-60.
Saa PA, Nielsen LK (2017) Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. https://doi.org/10.1016/j.biotechadv.2017.09.005. Biotechnology Advances 35(8): 981-1003.
Saa PA, Nielsen LK (2016) Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models. http://dx.doi.org/10.1093/bioinformatics/btw555. Bioinformatics 32(24): 3807–3814.
Saa PA, Nielsen LK (2016) Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach. http://dx.doi.org/10.1038/srep29635. Scientific Reports 6: 29635.
Saa PA, Nielsen LK (2016) ll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks. http://dx.doi.org/10.1093/bioinformatics/btw132. Bioinformatics 32(15): 2330–2337.
Saa PA, Nielsen LK (2016) A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis. http://dx.doi.org/10.1016/j.bbagen.2015.12.015. Biochimica et Biophysica Acta General Subjects 1860(3): 576-587.
Saa P, Nielsen L (2015) A general framework for thermodynamically consistent parameterization and efficient sampling of metabolic reactions. http://dx.doi.org/1371/journal.pcbi.1004195. PLOS Computational Biology 11(4): e1004195.
Dal’Molin C, Quek L, Saa PA, Nielsen LK (2015) A multi-tissue genome-scale metabolic modelling for the analysis of whole plant systems. http://dx.doi.org/10.3389/fpls.2015.00004. Frontiers in Plant Science 6: 4.
Cárcamo M, Saa P, Torres J, Torres S, Mandujano P, Pérez-Correa R, Agosin E (2014) Effective dissolved oxygen control strategy for high cell-density cultures. http://dx.doi.org/10.1109/TLA.2014.6827863. IEEE Latin America Transactions 12(3): 389-394.
Moenne I, Saa P, Laurie F, Pérez-Correa R, Agosin E (2014) Oxygen incorporation and dissolution during industrial-scale red wine fermentations. http://dx.doi.org/10.1007/s11947-014-1257-2. Food and Bioprocess Technology 7: 2627-2636.
Saa P, Pérez-Correa R, Celentano D, Agosin E (2013) Impact of carbon dioxide injection on oxygen dissolution rate during oxygen additions in a bubble column. http://dx.doi.org/10.1016/j.cej.2013.07.081. Chemical Engineering Journal 232: 157-166.
Saa P, Moenne I, Pérez-Correa R, Agosin E (2012) Modeling oxygen dissolution and biological uptake during pulse oxygen additions in oenological fermentations. http://dx.doi.org/10.1007/s00449-012-0703-7. Bioprocess and Biosystems Engineering 35(7): 1167-1178.
Sacher J, Saa P, Cárcamo M, López J, Pérez-Correa R, Gelmi C (2011) Improved calibration of a solid substrate fermentation model. http://dx.doi.org/10.2225/vol14-issue5-fulltext-7. Electronic Journal of Biotechnology 14: 5.