6 pages, 1 figure, 1 table.-- III CEA symposium of the Groups: Control Engineering – Modelling, Simulation and Optimization. 10-12 April, 2024, Vigo, Spain, [EN] Developing predictive antimicrobial resistance (AMR) models for supporting optimal treatment design poses one of the biggest challenges for mathematical biology. Birth-death (BD) processes constitute an intuitive and robust approach for modelling stochastic dynamics of biological systems, which are regaining attention due to rapid advances in computational techniques. In this work, we propose a BD model of bacterial heteroresistance, defined as the presence of many subpopulations with reduced antimicrobial sensitivities. The model accounts for competition between subpopulations so that the equations for the moment dynamics of the BD process are coupled, forming an infinite system. We propose a moment closure assuming a log-normal distribution to approximate the behaviour of the heteroresistant population under antimicrobial stress and compare the results with stochastic simulations of the actual BD process for a typical time-kill assay, [ES] El desarrollo de modelos predictivos de Resistencia Antimicrobiana (RAM) que apoyen el diseño óptimo de tratamientos para combatir las “superbacterias” es uno de los mayores retos que debe afrontar actualmente la biología matemática. Los procesos de Nacimiento-Muerte (NM) constituyen un enfoque intuitivo y potente para modelar las dinámicas estocásticas propias de los sistemas biológicos, el cual está recuperando atención gracias a los rápidos avances en las técnicas computacionales. En este trabajo, se presenta un modelo NM de heterorresistencia bacteriana, un fenotipo según el cual se distinguen diferentes subpoblaciones con sensibilidad reducida al antimicrobiano en comparación con la población principal. El modelo considera competencia entre las distintas subpoblaciones, provocando que las dinámicas para los momentos del proceso NM se encuentren totalmente acopladas, formando un sistema infinito de ecuaciones. Así, proponemos una clausura de momentos basada en la distribución log-normal para aproximar el comportamiento de la población heterorresistente bajo estrés antimicrobiano, y comparamos los resultados con simulaciones estocásticas del proceso NM real para un típico ensayo de tiempo-muerte, Este trabajo ha sido realizado gracias al apoyo del proyecto de investigación PID2022-136817OB-I00 financiado por MCIN/AEI /10.13039/501100011033/ y por “FEDER Una manera de hacer Europa” y a la ayuda del CSIC 20213AT001, No

6 pages, 3 figures, 2 tables.-- III CEA symposium of the Groups: Control Engineering – Modelling, Simulation and Optimization. 10-12 April, 2024, Vigo, Spain, [EN] In this study, an industrial washing process for harvested products is modeled, simulated, and optimized. For this purpose, a flume washing tank is considered, where we assume that the longitudinal spatial dimension retains the fundamental dynamics of free chlorine (FC), organic matter (COD), and bacterial load (Xw). The model is used to optimize the addition of the disinfectant, which enters the tank from three injection ports, ensuring that the microbial load at the end of the flume tank is below a considered safe threshold while minimizing the maximum amounts of disinfectant that could produce hazardous byproducts, [ES] En este estudio se modela, simula y optimiza un proceso de lavado industrial de productos de cosecha. Para ello se considera un tanque de lavado en forma de canal asumiendo que la dimensión espacial longitudinal retiene las dinámicas fundamentales de las dinámicas de cloro libre (FC), materia orgánica (COD) y carga bacteriana (Xw). El modelo se utiliza para optimizar la adición del desinfectante, que entra a lo largo del tanque en tres puntos de inyección, de forma que se garantice que la carga microbiana a la salida del tanque está por debajo de un umbral considerado seguro, mientras se minimiza las cantidades máximas de desinfectante que pudieran producir productos secundarios peligrosos, Este trabajo ha sido realizado gracias al apoyo del proyecto de investigación PID2022-136817OB-I00 financiado por MCIN/AEI /10.13039/501100011033/ y por “FEDER Una manera de hacer Europa” y la ayuda del CSIC 20213AT001, No

16 pages, 4 tables, 3 figures, Antimicrobial tolerance is a significant concern in the food industry, as it poses risks to food safety and public health. To overcome this challenge, synergistic combinations of antimicrobials have emerged as a potential solution. In this study, the combinations of two essential oil constituents (EOCs), namely carvacrol (CAR) and eugenol (EUG), with the quaternary ammonium compounds (QACs) benzalkonium chloride (BAC) and didecyldimethylammonium chloride (DDAC) were evaluated for their antimicrobial effects against Escherichia coli and Bacillus cereus, two common foodborne bacteria. The checkerboard assay was employed to determine the fractional inhibitory concentration index (FICI) and the fractional bactericidal concentration index (FBCI), indicating the presence of bactericidal, but not bacteriostatic, synergy in all QAC–EOC combinations. Bactericidal synergism was clearly supported by Bliss independence analysis. The bactericidal activity of the promising synergistic combinations was further validated by time–kill curves, achieving a >4-log10 reduction of initial bacterial load, which is significant compared to typical industry standards. The combinations containing DDAC showed the highest efficiency, resulting in the eradication of bacterial population in less than 2–4 h. These findings emphasize the importance of considering both bacteriostatic and bactericidal effects when evaluating antimicrobial combinations and the potential of EOC–QAC combinations for sanitization and disinfection in the food industry., This research was funded by Xunta de Galicia under grants IN606A-2020/028 and IN607B 2023/04, by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” under grant PID2022-136817OB-I00, by FEDER funds through the Operational Programme Competitiveness Factors-COMPETE, and by national funds by FCT—Foundation for Science and Technology under research grants EXPL/MED-QUI/1156/2021, LA/P/0045/2020 (ALiCE), UIDB/00511/2020, UIDP/00511/2020 (LEPABE)., Peer reviewed

15 pages, 2 tables, 4 figures.-- Under a Creative Commons license, Antimicrobial heteroresistance refers to the presence of different subpopulations with heterogeneous antimicrobial responses within the same bacterial isolate, so they show reduced susceptibility compared with the main population. Though it is widely accepted that heteroresistance can play a crucial role in the outcome of antimicrobial treatments, predictive Antimicrobial Resistance (AMR) models accounting for bacterial heteroresistance are still scarce and need to be refined as the techniques to measure heteroresistance become standardised and consistent conclusions are drawn from data. In this work, we propose a multivariate Birth-Death (BD) model of bacterial heteroresistance and analyse its properties in detail. Stochasticity in the population dynamics is considered since heteroresistance is often characterised by low initial frequencies of the less susceptible subpopulations, those mediating AMR transmission and potentially leading to treatment failure. We also discuss the utility of the heteroresistance model for practical applications and calibration under realistic conditions, demonstrating that it is possible to infer the model parameters and heteroresistance distribution from time-kill data, i.e., by measuring total cell counts alone and without performing any heteroresistance test, We thank the grants PID2022-136817OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, the grant IN607B 2023/04 by Xunta de Galicia, Spain and the grant 20213AT001 by CSIC, Spain, Peer reviewed

Data are provided in the Excel file "qac_eoc_tck_data.xlsx" and in the Matlab file "get_data.m" (with only relevant data for model calibration). Model parameter estimations and simulations are run by calling "main.m" in Matlab. Results can be also seen without running the code in results.fig/mat/xlsx, This repository provides data (📈) and mathematical models (🖳) to study the Synergistic Bactericidal Effects between Quaternary Ammonium Compounds and Essential Oil Constituents, This research was funded by Xunta de Galicia under grants IN606A-2020/028 and IN607B 2023/24; by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe" under grant PID2022-136817OB-I00; and by national funds by FCT – Foundation for Science and Technology under research grants EXPL/MED-QUI/1156/2021, LA/P/0045/2020 (ALiCE), UIDB/00511/2020, UIDP/00511/2020 (LEPABE), Peer reviewed

16 pages, 7 figures, 1 table, During adaptive laboratory evolution experiments, any unexpected interruption in data monitoring or control could lead to the loss of valuable experimental data and compromise the integrity of the entire experiment. Most homemade mini-bioreactors are built employing microcontrollers such as Arduino. Although affordable, these platforms lack the robustness of the programmable logic controller (PLC), which enhances the safety and robustness of the control process. Here, we describe the design and validation of a PLC-controlled morbidostat, an innovative automated continuous-culture mini-bioreactor specifically created to study the evolutionary pathways to drug resistance in microorganisms. This morbidostat includes several improvements, both at the hardware and software level, for better online monitoring and a more robust operation. The device was validated employing Escherichia coli, exploring its adaptive evolution in the presence of didecyldimethylammonium chloride (DDAC), a quaternary ammonium compound widely used for its antimicrobial properties. E. coli was subjected to increasing concentrations of DDAC over 3 days. Our results demonstrated a significant increase in DDAC susceptibility, with evolved populations exhibiting substantial changes in their growth after exposure, This research was funded by Xunta de Galicia (grants IN606A-2020/028 and IN607B 2021/11) and MCIN/AEI/10.13039/501100011033 “ERDF A way of making Europe” (grants PID2022-136817OB-I00 and DPI2014-54085-JIN), Peer reviewed

22 pages, 5 figures, This study details a biorefinery approach to valorize Dosidicus gigas squid pen waste. The process starts with the enzymatic deproteinization of squid pens, which prove effective with both Alcalase and Novozym, with the latter exhibiting a slightly higher efficiency to yield a material with 73% chitin content. Subsequent alkaline hydrolysis produces highly deacetylated chitosan (>90% degree of deacetylation), followed by controlled depolymerization to obtain polymers with molecular weights ranging from 50 to 251 kDa. Both native and depolymerized chitosan exhibit antimicrobial activity against Escherichia coli and Bacillus cereus, with B. cereus demonstrating greater resistance to chitosan compared to E. coli. The research also explores the bioconversion of deproteinization and deacetylation effluents. Deproteinization effluents prove superior in sustaining microbial growth, supporting comparable growth and lactic acid production for human probiotic strains (Lactobacillus plantarum and Leuconostoc mesenteroides) when substituting commercial peptones. Marine bacteria (Pseudomonas fluorescens and Phaeobacter sp.) show lower productivity. Integrating these processes into a biorefinery framework enables the conversion of 1 kg of dry squid pens into 350 g of chitosan, and facilitates the production of 937–949 g of lactic acid using human lactic acid bacteria cultures in media formulated with squid pen-derived effluents, glucose, yeast extract, and mineral salts. This integrated approach highlights the potential for maximizing resource utilization from squid pen waste, reducing environmental impact and generating high-value bioproducts., This research was funded by FCT/MCTES (PIDDAC): LEPABE, UIDB/00511/2020 (DOI: 10.54499/UIDB/00511/2020) and UIDP/00511/2020 (DOI: 10.54499/UIDP/00511/2020) and ALiCE, LA/P/0045/2020 (DOI: 10.54499/LA/P/0045/2020). Authors from IIM-CSIC are grateful to the EU Interreg Program (IBEROS+) (0072_IBEROS_MAIS_1_E, Interreg-POCTEP 2021–2027), GRC support program (ED431C 2021/49) and (GPC_IN607B 2024/010) from Xunta de Galicia, the project LIFE-REFISH (Project 101074323, LIFE21-ENV-ES-LIFE REFISH), and Grant PID2022-136817OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” for financial support. Marta Lima thanks the Portuguese Foundation for Science and Technology (FCT) for the financial support of her Ph.D. grant (2022.11196.BD)., Peer reviewed

209 pages, A resistencia a compostos antimicrobianos (RAM) constitúe unha das máis graves ameazas para a saúde pública a nivel mundial. O termo RAM emprégase para describir a capacidade dun microorganismo (bacteria, fungo ou virus) para proliferar en presenza dun determinado composto antimicrobiano. A resistencia a antimicrobianos pode ser intrínseca ou adquirida. A resistencia intrínseca refírese á capacidade natural e inherente de certos microorganismos para resistir compostos antimicrobianos específicos. Este tipo de resistencia responde a características estruturais ou fisiolóxicas inherentes do microbio, como a falta dunha diana para un axente antimicrobiano concreto. Emporiso, é a resistencia adquirida a máis preocupante e o foco desta tese. A resistencia adquirida (de aquí en diante referida simplemente como "resistencia") defínese como a capacidade dos microorganismos para multiplicarse en presenza dun axente antimicrobiano nunha concentración que inicialmente sería dabondo para inactivalos., Xunta de Galicia Predoctoral Fellowship (IN606A-2020/028). Axudas do Programa de Consolidación e Estruturación de Unidades de Investigación Competitiva, Grupo de Potencial Crecimiento (IN607B2021/11). Axudas do Programa de Consolidación e Estruturación de Unidades de Investigación Competitiva, Grupo de Potencial Crecimiento (IN607B 2023/04). Axudas do Programa de Consolidación e Estruturación de Unidades de Investigación Competitiva, Grupo de Potencial Crecimiento (IN607B 2024/10). LIFE REFISH project. Research Project (2023-2026) SAFE “Digital Twins for SAnitation in Food industry procEsses”. PID2022-136817OB-I00 (MCIU/AEI/FEDER, UE). Research Project (2019-2022) “ControlAR: Model-based process engineering to CONTROL Antimicrobial Resistance for the food industry 4.0″. RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), Peer reviewed

11 pages, 6 figures, 4 tables, This work presents a mechanistic mathematical model describing the dynamics of microbial contamination in water during the washing of fresh produce. The model accounts for key processes, including microbial transfer from food product to water, dilution effects, and inactivation by chlorine-based antimicrobials under varying organic matter loads. These mechanisms reflect those previously identified and endorsed by the European Food Safety Authority (EFSA) as relevant for assessing microbial risks in wash water systems. To enable its application across diverse industrial settings, we propose a methodology for case-specific model adaptation. This involves conducting a targeted experiment designed using principles of Optimal Experimental Design (OED), allowing for the collection of data that are sufficiently informative to confidently estimate unknown model parameters. Industrial relevance: The proposed framework provides the food industry with a flexible and efficient tool for estimating unknown parameters essential for microbial risk assessment and for optimizing fresh produce washing processes, This work was funded by EFSA, Contract number OC/EFSA/ BIOCONTAM/2021/02 on “Microbiological hazards associated with the use of water in the post-harvest handling and processing operations of fresh and frozen fruits, vegetables and herbs (ffFVHs)” and the opinions expressed are those of the contractors only and do not represent the contracting authority’s (EFSA’s) official position.
We thank the grants PID2022-136817OB-I00 funded by MCIN/AEI/10.13039/501100011033/FEDER and the grant IN607B 2023/04 by Xunta de Galicia, Peer reviewed

The scripts are developed in the MATLAB environment and utilize the AMIGO2 toolbox for optimization and optimal experimental design. AMIGO2 can be downloaded from https://sites.google.com/site/amigo2toolbox and should be placed alongside the repository files., This repository provides data (📈) and code (🖳) associated with the study:
Moreno-Razo, Ari S., et al. "Virtual Representation of Fresh Produce Washing in 4.0 Industry: Modelling and Calibration through Optimal Experimental Design" Innovative Food Science & Emerging Technologies (2025): 104307. https://doi.org/10.1016/j.ifset.2025.104307, This work was funded by EFSA, Contract number OC/EFSA/BIOCONTAM/2021/02 on “Microbiological hazards associated with the use of water in the post-harvest handling and processing operations of fresh and frozen fruits, vegetables and herbs (ffFVHs)” and the opinions expressed are those of the contractors only and do not represent the contracting authority’s (EFSA’s) official position.
We thank the grants PID2022-136817OB-I00 funded by MCIN/AEI/10.13039/501100011033/FEDER and the grant IN607B 2023/04 by Xunta de Galicia, Peer reviewed

Comunicaciones presentadas en las XLVI Jornadas de Automática, celebradas en Cartagena, del 3 al 5 de septiembre de 2025, [EN] Washing efficiency is essential to ensure the quality and safety of harvested fresh produces. At the industrial scale, this process is commonly carried out in channel-type washing tanks, where the produce is introduced and transported by a continuous water flow to which a disinfectant is added. Spatio-temporal variations in the incoming product flow pose a challenge to the effective implementation of the process. This study addresses the dynamic optimization of the injection flow to minimize the disinfectant dosage, ensuring that the concentration of bacteria in the washing water remains within the ranges considered safe, even when dealing with fluctuations in the amount of incoming product, [ES] La eficiencia del lavado es esencial para garantizar la calidad y seguridad alimentaria de productos de cosecha, especialmente en aquellos que se consumen frescos (productos de cuarta gama). A nivel industrial, esta práctica se lleva a cabo en tanques en forma de canal, donde los productos cosechados son introducidos y transportados mediante un flujo de agua corriente al que se a˜nade un desinfectante. Las variaciones espacio-temporales en el flujo de entrada de producto representan un desafío para una implementación eficiente del proceso. Este estudio se centra en la optimización dinámica del flujo de inyección para minimizar las dosis de desinfectante, garantizando que la concentración de bacterias en el agua de lavado se mantenga dentro de los rangos considerados seguros, incluso ante fluctuaciones en la cantidad de producto entrante, Agradecemos la financiación recibida de los proyectos PID2022-136817OB-I00 y PID2021-123654OB-C32, financiadas por MCIN/AEI/10.13039/501100011033/FEDER, y la ayuda IN607B 2023/04 de la Xunta de Galicia, Peer reviewed

10 pages, 6 figures, 1 table, Washing fresh produce is essential to remove soil and to inactivate microorganisms that may pose risks in products commonly consumed raw. However, operating continuous flume-tank systems is challenging because of variable produce loads and the spatiotemporal interplay between water quality and sanitiser chemistry.
This research addresses this challenge by developing a digital twin that optimally controls water and sanitiser additions during continuous flume tank operation. Its main component is a mechanistic model describing water quality dynamics, sanitiser decay, and microbial inactivation along the tank. This model is used offline to solve a multi-objective optimisation problem that balances water replenishment and sanitiser dosing while enforcing hygiene limits defined as a maximum allowable bacterial concentration at the tank outlet. The resulting Pareto front highlights how optimal solutions shift depending on whether water savings or sanitiser reductions are prioritised, and reveals the impractical volumes of water required when relying solely on water replenishment. For online use, the digital twins adjust the optimal solutions based on real-time measurements of incoming produce mass, ensuring the hygiene requirement under fluctuating loads. This work demonstrates how digital twins can enhance both resource efficiency and microbiological safety in industrial produce washing, We thank the grants PID2021-123654OB-C32, PID2022-136817OB-I00, PID2022-1417080B-I00, and PID2024-157718OB-C32, funded by MCIN/AEI/10.13039/501100011033/FEDER. Additionally, the authors are thankful for the financial support from AGROALNEXT program (MCIN and NextGenerationEU funds, PRTR–C17.I1) and Xunta de Galicia (IN607B 2023/04), Peer reviewed

19 pages, 6 figures, 1 table, Adaptive laboratory evolution (ALE) is a powerful tool for understanding and controlling the evolutionary trajectories of microorganisms. The scope of applications extends widely, including areas such as: biotechnology, synthetic biology, microbial ecology, and fundamental evolutionary research. In this work, we systematically explore the implementation and advantages of mini-bioreactors, defined as reactors with working volumes below 0.5 L, in ALE experiments. Mini-bioreactors offer substantial improvements over traditional large-scale reactors, including: reduced costs, enhanced parallelization capabilities, customizable configurations, and ease of automation. Through the utilization of illustrative case studies, which facilitate a comparative and critical evaluation of: batch, chemostat, turbidostat, and morbidostat operational modes, this review underscores the distinct capabilities of mini-bioreactors in enabling precise, dynamic control of evolutionary pressures. The novelty of this review lies in its comprehensive synthesis of recent advancements in mini-bioreactor technologies and operational strategies, particularly emphasizing innovations, such as: integrated automation, advanced sensors, and novel control algorithms adapted or specially designed for ALE. The ultimate objective is to provide both novices and experienced researchers with an updated, in-depth resource that addresses current technological limitations and future directions of mini-bioreactors in ALE, This work received funding from PID2022-136817OB-I00, AEI/10.13039/501100011033/FEDER, UE and Xunta de Galicia IN606A-2020/028 and IN607B 2021/11, Peer reviewed

1 file, Adaptive laboratory evolution (ALE) is a powerful tool for understanding and controlling the evolutionary trajectories of microorganisms. The scope of applications extends widely, including areas such as: biotechnology, synthetic biology, microbial ecology, and fundamental evolutionary research. In this work, we systematically explore the implementation and advantages of mini-bioreactors, defined as reactors with working volumes below 0.5 L, in ALE experiments. Mini-bioreactors offer substantial improvements over traditional large-scale reactors, including: reduced costs, enhanced parallelization capabilities, customizable configurations, and ease of automation. Through the utilization of illustrative case studies, which facilitate a comparative and critical evaluation of: batch, chemostat, turbidostat, and morbidostat operational modes, this review underscores the distinct capabilities of mini-bioreactors in enabling precise, dynamic control of evolutionary pressures. The novelty of this review lies in its comprehensive synthesis of recent advancements in mini-bioreactor technologies and operational strategies, particularly emphasizing innovations, such as: integrated automation, advanced sensors, and novel control algorithms adapted or specially designed for ALE. The ultimate objective is to provide both novices and experienced researchers with an updated, in-depth resource that addresses current technological limitations and future directions of mini-bioreactors in ALE.

Previous literature reviews have discussed ALE and mini-bioreactors separately but have not explored in detail the use of mini-bioreactors in the context of ALE. Earlier works either partially addressed the topic or focused narrowly on specific aspects, missing the breadth of mini-bioreactor applications in ALE. In addition, recent advancements in the design and use of mini-bioreactors for ALE experiments emphasize the need for a comprehensive review. This article provides an up-to-date overview of mini-bioreactors in ALE, discussing their fundamentals, characteristics, modes of operation, and future perspectives, with the aim of filling a significant knowledge gap in the field, This work received funding from PID2022-136817OB-I00, AEI/10.13039/501100011033/FEDER, UE and Xunta de Galicia IN606A-2020/028 and IN607B 2021/11, Peer reviewed

Nowadays the employ of mathematical predictive microbiology models is proving critical to determine the conditions of operation, transport and distribution in the food industry, as well as European legislation in this area. These models are used by industry and to develop scientific opinions in institutions as EFSA (European Food Safety Authority).

Most of these predictive microbiology models are focused on the bacterial growth , both pathogenic and food-spoiler, at different temperatures, pH and water activities. However, inactivation models of these bacteria are scarce, and almost exclusively focused on heat treatments at high temperatures, with almost no examples in the literature of models predicting the behavior of bacteria against antimicrobial agents for use in the food industry.

Antimicrobial based microbiological inactivaction models are scarce mainly due to the highly variable behaviours depending on the type of the antimicrobial-bacterial pair and, above all, because the difficulty of modeling bacterial resistance to these agents. This last challenge is especially complex and critical. In fact, bacterial resistance to antimicrobial compounds is a global scale problem qualified by the World Health Organization as one of the biggest health challenges facing today's society, and is expected to increase much faster than our ability to design new drugs or antimicrobial agents.

Currently, 700,000 deaths per year (25,000 in the EU) are attributable to infections by antibiotic-resistant pathogens. In the worst scenario, deaths will grow to 10 million by 2050 This resistance is mainly caused by the misuse and dosage of these compounds, including those used in the food industry, allowing the appearance of mutant strains with genetic characteristics that make them immune to their action.

In this thesis, will be obtained data and algorithms for the development and optimization of mathematical models (based on predictive microbiology) of response of a set of bacteria common to several antimicrobials of different origin (some of them from natural sources), employed in the food industry. In addition, the possibility of developing a software, accessible through a web application, for the simulation and optimization of the obtained models will be studied.

The following sub-goals are required to obtain the data and algorithms:

1. Development of an automatic kinetic inactivation / death data acquisition system for dynamic (time-changing) antimicrobial profiles.

2. Study of the antimicrobial capacity of alfa-chitosans of different molecular weights produced from cephalopod residues.

3. Prediction of bacterial inactivation by antimicrobials using models based on predictive microbiology.

4. Determination of antimicrobial dynamic profiles that minimize or maximize the occurrence of antimicrobial resistance.

The present work aims to develop tools, both experimental and theoretical, that allow us to deep into the knowledge of the bacterial resistances against toxic compounds with different chemical structure. From an applied point of view, a battery of tertiary models will be obtained in this way, which will evaluate the dynamics of inactivation with various antimicrobials in the food industry. This will enable food companies to calculate optimal food disinfection and conservation policies that minimize the occurrence of resistance. In this sense, the software to be developed will be a basic tool for this purpose., En la actualidad, el uso de modelos matemáticos de microbiología predictiva están resultando críticos para determinar las condiciones de operación, transporte y distribución en la industria alimentaria, así como la legislación Europea en este ámbito. Estos modelos son empleados tanto por la industria como para elaborar opiniones científicas en organismos institucionales como la EFSA, Autoridad Europea para la Seguridad Alimentaria.

La mayoría de estos modelos de microbiología predictiva se centran en el crecimiento de bacterias, tanto patógenas como deteriorantes de alimentos, a diferentes temperaturas, pH y actividades de agua. Por ello, los modelos de inactivación de estas bacterias son escasos y, casi exclusivamente, centrados en tratamientos térmicos a altas temperaturas, sin existir casi ejemplos en la literatura de modelos que predigan el comportamiento de bacterias ante antimicrobianos de uso en la industria alimentaria.

Los modelos de inactivación microbiológica por antimicrobianos son muy escasos, principalmente porque los comportamientos son muy variables dependiendo del tipo de antimicrobiano y bacteria, y sobre todo, por la dificultad de modelar la resistencia bacteriana a estos agentes. Este último reto es especialmente complejo y crítico. De hecho, la resistencia bacteriana a los compuestos antimicrobianos es un problema de escala global calificado por la Organización Mundial de la Salud, como uno de los mayores retos en materia de salud a los que se enfrenta la sociedad actual, previéndose que aumente mucho más rápido que nuestra capacidad para diseñar nuevos fármacos o agentes antimicrobianos. Actualmente, 700.000 muertes anuales (25.000 en la UE) son atribuibles a infecciones por patógenos con resistencia a antibióticos. En el peor de los casos, las predicciones estiman que las muertes crecerán hasta los 10 millones en el año 2050. Dicha resistencia, está causada principalmente por una dosificación incorrecta de estos compuestos, entre ellos, los empleados en la industria alimentaria, permitiendo la aparición de cepas mutantes con características genéticas que las hacen inmunes a su acción.

En esta tesis, se obtendrán datos y algoritmos para el desarrollo y la optimización de los modelos matemáticos, basados en la microbiología predictiva, de respuesta de un conjunto de bacterias comunes ante varios agentes antimicrobianos diferentes, algunos de ellos naturales, principalmente empleados en la industria alimentaria. Además, se estudiará la posibilidad de desarrollar un software, accesible a través de una aplicación web, para la simulación y optimización de los modelos obtenidos. Los siguiente sub-objetivos son necesarios para obtener los datos y algoritmos:

1. Desarrollo de un sistema automático de adquisición de datos cinéticos de inactivación/muerte por perfiles dinámicos (cambiantes en el tiempo) de antimicrobianos.

2. Estudio de la capacidad antimicrobiana de alfa-quitosanos de diferentes pesos moleculares producidos por residuos de cefalópodos.

3. Predicción de la inactivación de bacterias por antimicrobianos empleando modelos basados en microbiología predictiva.

4. Determinación de los perfiles dinámicos de antimicrobianos que minimicen o maximicen la aparición de resistencia a antimicrobianos.

El presente trabajo pretende desarrollar herramientas, tanto experimentales como teóricas, que nos permitan mejorar el conocimiento de las resistencias bacterianas a tóxicos de diferente estructura química. Desde un punto de vista aplicado, de esta forma se obtendrán una batería de modelos terciarios que evalúen las dinámicas de inactivación ante diversos antimicrobianos de la industria alimentaria. Esto permitirá a las empresas alimentarias el cálculo de políticas óptimas de desinfección y de conservación de alimentos que minimicen la aparición de resistencias. En este sentido, el software a desarrollar supondrá una herramienta básica para llevar a cabo este propósito., Na actualidade o uso de modelos matemáticos de microbioloxía preditiva están resultando críticos para determinar as condicións de operación, transporte e distribución na industria alimentaria, así como a lexislación Europea neste ámbito. Estes modelos son empregados tanto pola industria coma para elaborar opinións científicas en organismos institucionais como a EFSA, Autoridade Europea para a Seguridade Alimentaria

As maioría destes modelos de microbioloxía preditiva céntranse no crecemento de bacterias, tanto patóxenas como deteriorantes de alimentos, a diferentes temperaturas, pH e actividades de auga. Porén, os modelos de inactivación destas bacterias son escasos, e case exclusivamente centrados en tratamentos térmicos a altas temperaturas, sen existir case exemplos na literatura de modelos que predigan o comportamento de bacterias ante axentes antimicrobianos de uso na industria alimentaria

Os modelos de inactivación microbiolóxica por antimicrobianos son escasos principalmente porque os comportamentos son moi variables dependendo do tipo de antimicrobiano e bacteria, e sobre todo, pola dificultade de modelar a resistencia bacteriana a estes axentes. Este último reto é especialmente complexo e crítico. De feito a resistencia bacteriana aos compostos antimicrobianos é un problema de escala global cualificado pola Organización Mundial da Saúde como un dos maiores retos en materia de saúde aos que se enfronta a sociedade actual, prevéndose que aumente moito máis rápido que a nosa capacidade de deseñar novos fármacos ou axentes antimicrobianos. Actualmente, 700.000 mortes anuais (25.000 na UE) son atribuíbeis a infeccións por patóxenos resistentes a antibióticos. No peor dos casos, as predicións estiman que as mortes crecerán ata os 10 millóns no 2050 (O´Neill, 2006). Dita resistencia é principalmente causada polo uso e dosificación incorrecta destes compostos, entre eles os empregados na industria alimentaria, permitindo a aparición de cepas mutantes con características xenéticas que as fan inmunes a súa acción.

Nesta tese, obteranse datos e algoritmos para o desenrolo e optimización de modelos matemáticos, baseados na microbioloxía preditiva, de resposta dun conxunto de bacterias comúns ante varios antimicrobianos de diferente orixe, algún deles naturais, e principalmente empregados na industria alimentaria. Ademais, estudarase a posibilidade de desenrolar un software, accesible a través dunha aplicación web, para a simulación e optimización dos modelos obtidos.

Os seguintes subobxectivos son necesarios para obter os datos e algoritmos:

1. Desenrolo dun sistema automático de adquisición de datos cinéticos de inactivación/morte por perfís dinámicos (cambiantes no tempo) de antimicrobianos.

2. Estudo da capacidade antimicrobiana de alfa-quitosanos de diferentes pesos moleculares producidos de residuos de cefalópodos.

3. Predición da inactivación de bacterias por antimicrobianos usando modelos baseados na microbioloxía preditiva.

4. Determinación dos perfís dinámicos de antimicrobianos que minimicen ou maximicen a aparición de resistencia a antimicrobianos

O presente traballo pretende desenrolar ferramentas, tanto experimentais como teóricas, que nos permitan mellorar no coñecemento das resistencias bacterianas a tóxicos de diferente estrutura química. Dende un punto de vista aplicado, obteranse deste xeito unha batería de modelos terciarios que avalíen as dinámicas de inactivación ante diversos antimicrobianos da industria alimentaria. Isto permitirá ás empresas alimentarias o cálculo de políticas óptimas de desinfección e de conservación de alimentos que minimicen a aparición de resistencias. Neste senso, o software a desenrolar suporá unha ferramenta básica para levar a cabo este propósito., Agencia Estatal de Investigación | Ref. PID2022-136817OB-I00, Agencia Estatal de Investigación | Ref. RTI2018-093560-J-I00, Xunta de Galicia | Ref. IN606A-2020/028