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, Acuña V, Garrido D, Saa PA. setCCon: A scalable algorithm for probing the conversions cone of metabolic reaction networks.

Saa PA, Sánchez, M. 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.

Van den Bogaard S, Saa PA, Alter T. Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control.

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

Bárzaga L, Aguila-Camacho N, Ibáñez F, Duarte-Mermoud M, Saa PA, Pérez-Correa R (2024)  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. 12th IFAC ICFDA Conference. Accepted.

Journal papers

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.

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.

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.

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, 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.