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

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

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