Publicaciones de proyectos

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11968/6946

Publicaciones vinculadas a proyectos con financiamiento externo.

Now showing 1 - 12 of 12

Typechecking dependently sorted nominal signatures
(2025) Fernández, Maribel; Pagano, Miguel; Szasz, Nora; Tasistro, Álvaro
The authors present a type-checker algorithm for Dependently Sorted Nominal Signatures. They prove its correctness and completeness with respect the sorting system.
CRISPR tools in bacterial whole-cell biocatalysis
(2023) Mulet, Ana Paula; Ripoll, Magdalena; Betancor, Lorena
Biocatalysis has emerged as a promising alternative to conventional chemical processes for the production of a wide range of chemicals, providing a sustainable solution to the problem of limited resources due to an ever-increasing global population. This approach involves the use of biobased catalysts, such as whole microorganisms or enzymes, to perform chemical conversions. While whole-cell biocatalysts offer advantages over the use of free enzymes, limitations related to productivity and undesired compound production have been observed when using microorganisms. Offering high specificity, broad applicability, and increased efficiency over traditional genetic engineering methods, CRISPR-based technologies may be the quintessential tool for the fit-for-purpose design of efficient bacterial biocatalysts. In this work, we aim to demonstrate the potential of CRISPR-based technologies to enhance whole-cell bacterial biotransformations for a more sustainable obtention of industrially important products. We have included a comprehensive and in-depth analysis of the current state of the art, emphasizing challenges and opportunities for future research. Through a critical analysis of reported examples, we intend to highlight the opportunities and advantages offered by CRISPR-based technologies in the field of biocatalysis for more efficient, sustainable, and translational processes.
New perspectives into Gluconobacter-catalysed biotransformations
(2023) Ripoll, Magdalena; Lerma Escalera, Jordy Alexis; Morones Ramírez, José Rubén; Rios Solis, Leonardo; Betancor, Lorena
Different from other aerobic microorganisms that oxidise carbon sources to water and carbon dioxide, Gluconobacter catalyses the incomplete oxidation of various substrates with regio- and stereoselectivity. This ability, as well as its capacity to release the resulting products into the reaction media, place Gluconobacter as a privileged member of a non-model microorganism class that may boost industrial biotechnology. Knowledge of new technologies applied to Gluconobacter has been piling up in recent years. Advancements in its genetic modification, application of immobilisation tools and careful designs of the transformations, have improved productivities and stabilities of Gluconobacter strains or enabled new bioconversions for the production of valuable marketable chemicals. In this work, the latest advancements applied to Gluconobacter-catalysed biotransformations are summarised with a special focus on recent available tools to improve them. From genetic and metabolic engineering to bioreactor design, the most recent works on the topic are analysed in depth to provide a comprehensive resource not only for scientists and technologists working on/with Gluconobacter, but for the general biotechnologist.
Bacteria-polymer composite material for glycerol valorization
(2023) Ripoll, Magdalena; Soriano, Nicolás; Ibarburu, Sofía; Dalies, Malena; Mulet, Ana Paula; Betancor, Lorena
Bacterial immobilization is regarded as an enabling technology to improve the stability and reusability of biocatalysts. Natural polymers are often used as immobilization matrices but present certain drawbacks, such as biocatalyst leakage and loss of physical integrity upon utilization in bioprocesses. Herein, we prepared a hybrid polymeric matrix that included silica nanoparticles for the unprecedented immobilization of the industrially relevant Gluconobacter frateurii (Gfr). This biocatalyst can valorize glycerol, an abundant by-product of the biodiesel industry, into glyceric acid (GA) and dihydroxyacetone (DHA). Different concentrations of siliceous nanosized materials, such as biomimetic Si nanoparticles (SiNps) and montmorillonite (MT), were added to alginate. These hybrid materials were significantly more resistant by texture analysis and presented a more compact structure as seen by scanning electron microscopy. The preparation including 4% alginate with 4% SiNps proved to be the most resistant material, with a homogeneous distribution of the biocatalyst in the beads as seen by confocal microscopy using a fluorescent mutant of Gfr. It produced the highest amounts of GA and DHA and could be reused for up to eight consecutive 24 h reactions with no loss of physical integrity and negligible bacterial leakage. Overall, our results indicate a new approach to generating biocatalysts using hybrid biopolymer supports.
Combined learning and optimal power flow for storage dispatch in grids with renewables
(2024) Porteiro, Rodrigo; Paganini, Fernando; Bazerque, Juan Andres
We propose an optimization and learning technique for controlling energy storage in power systems with renewables. A reinforcement learning (RL) approach is employed to bypass the need for an accurate stochastic dynamic model for wind and solar power; at the same time, the presence of the grid is explicitly accounted for through the “DC” approximation to the Optimal Power Flow (OPF) to impose line constraints. The key idea that allows the inclusion of such instantaneous constraints within the RL framework is to take as control actions the storage operational prices, which may be suitably discretized. A policy to select these actions as a function of the state is parameterized by a neural network model and trained based on traces of demand and renewables. We call this combined strategy RL-OPF. We test it on a trial network with real data records for demand and renewables, showing convergence to a control policy that induces arbitrage of energy across space and time.
Queueing analysis of imbalance between multiple server pools with an application to 3-phase EV charging
(2023) Ferragut, Andres; Paganini, Fernando
We consider systems where multiple servers operate in parallel, with a particular feature: servers are classified into d classes, and we wish to keep approximate balance between the load allocated to each class. We introduce a relevant imbalance metric, and study its behavior under stochastic demands with different task routing policies. For random routing, we analyze two cases of interest, depending on whether capacity constraints are operative: we obtain expressions for the stationary distribution and analyze the scaling behavior of our metric as a function of system size. Subsequently, we analyze active routing to the least loaded class, obtaining sharp bounds for the imbalance metric. As a practical application, we study the problem of imbalance between d = 3 phases, for the service of electrical vehicle charging. We show the engineering relevance of our imbalance metric in this context, and validate the theoretical results with simulations and real traces from EV charging data.
Dynamic load balancing of selfish drivers between spatially distributed electrical vehicle charging stations
(2023) Paganini, Fernando; Ferragut, Andres
This paper considers an electrical vehicle recharging infrastructure made up of physically separate stations serving spatially distributed requests for charge. Arriving EVs receive feedback on transport times to each station, and waiting times at congested stations, based on which they make a selfish selection. We present a fluid model of the resulting dynamics, in particular modeling queueing delays as a function of fluid queues, and two different models of client departures: given sojourn times, or given service times. In each case, the joint load balancing dynamics is related to a convex program, suitable variant of a centralized optimal transport problem. In particular, we use Lagrange duality to show the correspondence between equilibrium points and optima, and to analyze the convergence properties of the dynamics. The results have similarities and differences with classical work on selfish routing for transportation networks. We present illustrative simulations, which also explore the alidity of the model beyond the fluid assumption.
Nominal sets in Agda
(2022) Pagano, Miguel; Solsona, Jose E.
In this paper we present our current development on a new formalization of nominal sets in Agda. Our first motivation in having another formalization was to understand better nominal sets and to have a playground for testing type systems based on nominal logic. Not surprisingly, we have independently built up the same hierarchy of types leading to nominal sets. We diverge from other formalizations in how to conceive finite permutations: in our formalization a finite permutation is a permutation (i.e. a bijection) whose domain is finite. Finite permutations have different representations, for instance as compositions of transpositions (the predominant in other formalizations) or compositions of disjoint cycles. We prove that these representations are equivalent and use them to normalize (up to composition order of independent transpositions) compositions of transpositions.
Accurate reduced-order models for heterogeneous coherent generators
(2021) Min, Hancheng; Paganini, Fernando; Mallada, Enrique
We introduce a novel framework to approximate the aggregate frequency dynamics of coherent generators. By leveraging recent results on dynamics concentration of tightly connected networks, and frequency weighted balanced truncation, a hierarchy of reduced-order models is developed. This hierarchy provides increasing accuracy in the approximation of the aggregate system response, outperforming existing aggregation techniques.
Automatic cloud instance provisioning with quality and efficiency
(2021) Goldsztajn, Diego; Ferragut, Andrés; Paganini, Fernando
A distinctive feature of cloud computing is that it enables customers to dynamically summon server instances. Service providers facing uncertain demand patterns may exploit this feature by setting automatic provisioning rules for right-sizing the capacity contracted from the cloud. This situation can be modeled by a queueing system where the numbers of both jobs and servers evolve in time, the latter subject to delays in creation and deletion. We study in this context different feedback rules with the objective of efficiently matching capacity and load, while simultaneously providing a high quality of service. These rules are analyzed by means of fluid and diffusion limits for Markov chains. In particular we develop suitable extensions of the classical literature on this topic, required to accommodate non-homogeneous intensity scalings and non-differentiable drift fields. With these tools, our final proposal is shown to exhibit properties akin to the Halfin-Whitt regime, achieved automatically without knowledge of the system load. We further investigate by simulation its behavior under time-varying load, demonstrating the capabilities of our design to provide quality and efficiency in highly dynamic scenarios.
Proximal regularization for the saddle point gradient dynamics
(2021) Goldsztajn, Diego; Paganini, Fernando
This paper concerns the solution of a convex optimization problem through the saddle point gradient dynamics. Instead of using the standard Lagrangian as is classical in this method, we consider a regularized Lagrangian obtained through a proximal minimization step.We show that, without assumptions of smoothness or strict convexity in the original problem, the regularized Lagrangian is smooth and leads to globally convergent saddle point dynamics. The method is demonstrated through an application to resource allocation in cloud computing.
Lacasa inmovilizada para la degradación de residuos de antibióticos en leche
(2019) Vaccaro, Víctor; Betancor, Lorena; Jackson, Erienne
Los residuos de medicamentos veterinarios, especialmente los antimicrobianos, son considerados un peligro y potencial riesgo para los procesos de industrialización lechera, la salud pública y el medio ambiente. Una posible solución biotecnológica para degradar antibióticos contaminantes en leche surge de la utilización de enzimas. Las lacasas son oxidasas que contienen cobre y pueden catalizar la oxidación de una amplia variedad de compuestos fenólicos y no fenólicos.

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