Our group builds computational tools rooted in advanced optimization and machine learning to redesign and understand biological systems.
Based in the Department of Chemical and Bioprocess Engineering and the Institute for Mathematical and Computational Engineering at the Pontificia Universidad Católica de Chile, and led by Prof. Pedro Saa, we combine genome-scale network reconstruction, stoichiometric and kinetic metabolic modelling, computational statistics, and bioprocess optimization. Our work spans metabolic engineering, systems biotechnology, and the sustainable production of food ingredients and high-value chemicals — bridging computational frameworks with experimental validation to rediscover biology.
Read more about our research →Group Leader · Department of Chemical & Bioprocess Engineering
Institute for Mathematical and Computational Engineering, PUC Chile
Faculty member at the Department of Chemical and Bioprocess Engineering and the Institute for Mathematical and Computational Engineering of PUC Chile. His interests include genome-scale network reconstruction and analysis, metabolic modelling and engineering, and bioprocess and biosystems optimization.
PhD — The University of Queensland, 2017 · MEng — PUC Chile, 2012 · BEng — PUC Chile, 2012
Genome-scale network reconstruction, construction and analysis of stoichiometric and kinetic models of cellular metabolism. Our flagship tools — GRASP, LooplessFluxSampler, and METACONE — underpin this research direction.
Engineering biological systems for sustainable production of food ingredients and high-value chemicals.
Advanced optimization frameworks for bioreactor design, control, and process scale-up.
Computational statistics and ML techniques to infer, predict, and design biological behavior from sparse data.
Publicly released computational tools — METACONE, GRASP, LooplessFluxSampler, Fast-SNP, ll-ACHRB, among others.
Featured Work
All Papers
Márquez C, Saa P, Pérez-Correa JR
Evaluación por simulación de estrategias de control automático para fermentadores industriales de cervezaIEEE ICA-ACCA, Santiago, Chile
Saa PA
Rational metabolic pathway prediction and design: Computational tools and their applications for yeast systems and synthetic biologySynthetic Biology of Yeasts. Springer, Cham
Mendoza S, Saa PA, Teusink B, Agosin E
Metabolic modelling of wine fermentation at genome scaleMethods in Molecular Biology vol. 2399, Springer
Under Review
Vargas-Luna C, Tapia I, Godoy L, Saa PA, Franco W.
Characterization and kinetic modeling of Levilactobacillus brevis malolactic fermentation under oenological conditions.
Under Review
Tapia I, Torrealba C, Luna R, Pérez-Correa JR, Saa PA.
Dynamic balance of sparse flux vectors for efficient simulation of culture dynamics and network reduction.
Under Review
Torrealba C, Luna R, Saa PA, Pérez-Correa JR.
A systematic transfer workflow for robust model identification across scales: Application to wine fermentation.
Under Review
In Preparation
Zapararte S, Marcellin E, Nielsen LK, Saa PA.
Topologically-constrained sampling of thermodynamically feasible mass-balanced states in metabolic reaction networks.
In Preparation
Saa PA, Miranda G, Drovandi C, Nielsen LK.
Bayesian parameter inference on the simplex: Convenient transformations and perturbation kernels for Sequential Monte Carlo sampling.
In Preparation
Efficient framework for dynamic simulation and reduction of metabolic flux models.
Scalable framework for exploring the conversions cone of metabolic networks.
Efficient algorithm for sampling the loopless flux solution space of genome-scale metabolic models.
Computational platform for building thermodynamically consistent kinetic models of cell metabolism.
Matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models.
Browse all open-source repositories, models, datasets, and supporting code released by the lab.
Principal Investigator
Basal Center for Artificial Intelligence Research
2021–ongoing
Principal Investigator
FONDECYT Regular No. 1262500 — Scalable prediction of metabolic fluxes informed by enzyme kinetics and omics data
2026–2029
Co-Investigator
FONDECYT Exploración No. 13250023 — Impact of GABA-producing bacterial consortia in neuroprotection
2025–2029
Co-Investigator
FONDECYT Regular No. 1230764 — Model-guided analysis of microbial interactions in gut microbiota
2023–2027
Principal Investigator
Avanza UC AV25292 — EPS production from mining waste via S. acidophilus
2026–2027
Adjunct Investigator
Center for Research and Innovation VitiScience
2025–ongoing
Our lab uses both wet- and dry-lab approaches to better understand biological systems. We welcome motivated students and researchers excited about developing new computational approaches for rediscovering biology. We have openings for domestic and international applicants at all levels.
Contact Prof. Pedro Saa directly at pnsaa@uc.cl · +56 2 2354 9674