9 pages, 4 figures, 4 tables, Chondroitin sulfate (CS) is a sulfated glycosaminoglycan with diverse biological activities, which are influenced by molecular weight (Mw) and sulfation pattern. In the present work, we take advantage of the characteristic high Mw of fish CS (51–70 kDa) to obtain lower Mw fragments with hyaluronidase and chondroitinase ABC. With this aim, we present a pseudo-mechanistic model capable of reproducing the decrease in Mw of CS from five different fish species over 24 h at four enzyme to substrate ratios. The fitting parameters of the model for each species allow to establish conditions of reaction to produce CS of the desired Mw. Furthermore, the main features of the sulfation pattern of fish CS remain in the depolymerized fragments, highlighting the feasibility of the proposed approach, This study was financed by projects IBEROS (0245_IBEROS_1_E, POCTEP 2015), CVMar+I (0302_CVMAR_I_1_P, POCTEP 2015), BLUEHUMAN (EAPA_151/2016, UE-INTERREG Atlantic Area Programme) and Xunta de Galicia (Grupos de Potencial Crecimiento, IN607B 2018/19). M.R.G thanks RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), Peer reviewed

13 pages, 3 tables, 4 figures, Mechanistic models based on chemical properties of metals and body size have received substantial attention for their potential application to various metals and to different conditions without required calibration. This advantage has been demonstrated for a number of metals, such as Cd and Ag. However, the capacity of metal-specific chemical properties to explain variations in the accumulation for platinum-group elements (PGEs) has not been investigated yet, although emission of these metals is of increasing concern. Once being released, PGEs exist in the environment in mixtures with other metals. The present study attempted to model the accumulation of Pd and Pt in mixtures with Ag and Cd in the zebra mussel (Dreissena polymorpha) from the aqueous phase; and to investigate the potential application of mechanistic models to Pd and Pt. The present study showed statistically insignificant differences in metal accumulation among size groups in a narrow range of shell length (16–22 mm). Kinetic models could simulate well the accumulation of Cd, Ag, and Pt when metal-specific responses of zebra mussels are taken into consideration. These responses include enhanced immobilisation as a detoxifying mechanism and exchange between soft tissues and shells via the extrapallial fluid. Environmental conditions, e.g. the presence of abiotic ligands such as chloride, might also play an important role in metal accumulation. Significant relationships between the absorption efficiency and the covalent index indicate the potential application of mechanistic models based on this chemical property to Pt, T.T.Y.L was granted with funding from the Deutsche Forschungsgemeinschaft (DFG), Germany (LE 3716/2-1). M.R.G. acknowledgements financial funding to RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), Peer reviewed

11 pages, 5 figures, 6 tables, Smart active packaging is an innovative packaging system that combines the benefits of measuring, estimating or predicting different aspects of food quality or safety with the release of an active substance that extends product shelf life. Nevertheless, in its typical configuration, the active packaging and the smart packaging are not connected, and the information provided is not exploited to design the release of the active substance.

In this work, we demonstrate how smart active packaging systems using predictive mathematical models allow the automatic optimisation of food packaging design and the prediction of the expected shelf life along the food chain. On the one hand, the system calculates the best design of the active packaging and the concentration of the active substance in the different layers that maximise food quality and safety. On the other hand, the model allows to calculate and update shelf life values along the food chain under unexpected changes in the storage conditions. Shelf life estimations and prediction will help distributors and sellers to adjust the product market prices. For example, prices can be lowered to avoid food losses when the product is close to its use-by date.

Hake (Merluccius merluccius) represents an example of a highly relevant and perishable food that can be conserved using natural antimicrobials. Therefore, the case study selected to illustrate the proposed methodology consists of the smart active packaging of hake using carvacrol as the active substance (antimicrobial). Besides, different polymers are considered as possible active packaging materials. The Matlab™ codes required to perform the simulations of the models described in this work as well as the optimisations for packaging design are available at https://doi.org/10.5281/zenodo.3244153, C.V. acknowledges funding received from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 723575 (CoPro project). M.M.I. belongs to the Galician Competitive Research Group (ED431C 2017/029) and the CRETUS strategic partnership (AGRUP2017/01), co-funded by FEDER (EU). M.R.G. acknowledges financial support through the projects IMMICRO (201870E134) and ControlAR (RTI2018-093560-J-I00, MCIU/AEI/FEDER, UE), Peer reviewed

1 file, This data-sheet covers those disinfectants tested against SARS-CoV-2 or other coronaviruses. Data were extracted from several research articles indicated in the reference row. The data-sheet comprises a total of 11 fields with info regarding the virus (virus and strain/isolate names), formulation (substance(s) and its concentration in percentage) and test characteristics (suspension or surface tested, kind of surface, use dilution before testing, disinfectant and inoculum volumes, organic load type and concentrations and contact time) as well as their results, normalized in terms of Log10 viral infectivity reduction, This research was funded by RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE) and IN606A-2020/028 (Xunta de Galicia predoctoral grant), No

Industries of the food sector have made a great effort to control SARS-CoV-2 indirect transmission, through objects or surfaces, by updating cleaning and disinfection protocols previously focused on inactivating other pathogens, as well as food spoilage microorganisms. The information, although scarce at the beginning of the COVID-19 pandemic, has started to be sufficiently reliable to avoid over-conservative disinfection procedures. This work reviews the literature to propose a holistic view of the disinfection process where the decision variables, such as type and concentration of active substances, are optimised to guarantee the inactivation of SARS-CoV-2 and other usual pathogens and spoilage microorganisms while minimising possible side-effects on the environment and animal and human health, This research was funded by RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), IN606A2020/028 (Xunta de Galicia predoctoral grant) and EIT Food RIS Fellowships., Peer reviewed

XXVIII Congreso de la Sociedad Española de Microbiología, 28 de Junio al 2 de Julio de 2021, La evolución adaptativa en laboratorio (ALE) es una aproximación experimental enfocada al estudio de la evolución de poblaciones microbianas bajo condiciones de crecimiento específicas. La ALE no constituye solamente una herramienta para obtener de forma sencilla poblaciones con un fenotipo determinado, ya que también permite conocer, desde el punto de vista evolutivo, como esos fenotipos se desarrollan a lo largo del tiempo.Los experimentos ALE pueden ser llevados a cabo tanto en régimen continuo como discontinuo. Sin embargo, en régimen discontinuo la fluctuación incontrolada de las condiciones de cultivo a lo largo del tiempo dificulta mantener constante el nivel de presión selectiva.El morbidostato es un sistema de cultivo continuo constituido por una serie de pequeños biorreactores asistidos por computadora capaz de ajustar dinámicamente la concentración del compuesto antimicrobiano en el medio, manteniendo constante la presión selectiva a lo largo del tiempo (Toprak et al., 2013). En el Instituto de Investigaciones Marinas (IIMCSIC) hemos desarrollado un morbidostato con el que pretendemos derivar modelos matemáticos que expliquen la evolución de la resistencia a compuestos antimicrobianos empleados en la industria alimentaria . No obstante, la utilidad del morbidostato va más allá, permitiendo obtener rápidamente fenotipos resistentes y estudiar los cambios a nivel -ómico producidos en poblaciones bacterianas sometidas a niveles constantes de presión selectiva por antimicrobianos a lo largo del tiempo.Toprak, Erdal, et al. “Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition”. Nature protocols 8.3 (2013):555-567, Proyecto RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), Ayuda Ramón y Cajal (RYC2019-028006-I/AIE/10.13039/501100011033), Ayuda predoctoral de la Xunta de Galicia (IN606A-2020/028), No

11 pages, 3 figures, 4 tables.-- This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) are standard indexes for determining disinfection effectiveness. Nevertheless, they are static values disregarding the kinetics at sub-MIC concentrations where adaptation, growth, stationary, and death phases can be observed. The understanding of these dynamic mechanisms is crucial to designing effective disinfection strategies. In this study, we studied the 48 h kinetics of Bacillus cereus and Escherichia coli cells exposed to sub-MIC concentrations of didecyldimethylammonium chloride (DDAC). Two mathematical models were employed to reproduce the experiments: the only-growth classical logistic model and a mechanistic model including growth and death dynamics. Although both models reproduce the lag, exponential and stationary phases, only the mechanistic model is able to reproduce the death phase and reveals the concentration dependence of the bactericidal/bacteriostatic activity of DDAC. This model could potentially be extended to study other antimicrobials and reproduce changes in optical density (OD) and colony-forming units (CFUs) with the same parameters and mechanisms of action, This study was funded by the projects RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), RYC2019-028006-I/AIE/10.13039/501100011033, 20213AT001, and the Xunta de Galicia Grants IN606A-2020/028 and IN607B-2021/11, Peer reviewed

2 pages, 1 figure.-- Las actas del Simposio Conjunto de los Grupos Temáticos de CEA. Modelado, Simulación, Optimización e Ingeniería de Contro están sujetas a una licencia Creative Commons Attribution 4.0 International License, La capacidad que poseen las bacterias para adaptarse a su entorno y desarrollar resistencia a los compuestos antimicrobianos actualmente conocidos (antibióticos, desinfectantes y conservantes) plantea un serio problema, económico y de salud pública, el cual se agrava a pasos agigantados. Tan solo en el año 2019, informes oficiales de las Naciones Unidas cifran en 700 000 los fallecimientos que se pueden atribuir directamente a la infección provocada por algún tipo de bacteria resistente a los antibióticos. Por desgracia, algunos estudios recientes sugieren que estos datos pueden resultar incluso más desalentadores, rondando los 1.27 millones de fallecidos en este mismo año (Murray et al., 2022). El rápido desarrollo de resistencia a antimicrobianos que presentan algunas bacterias afecta ya, de manera particularmente negativa, a las industrias médica y alimentaria, entre muchas otras. Este problema, lejos de tener solución, parece avanzar mucho más rápidamente que nuestra capacidad para entender las causas e implicaciones, del mismo. Por lo tanto, se hace cada vez más necesario ampliar nuestro conocimiento sobre los mecanismos que conducen al origen, selección y transmisión de resistencia a antimicrobianos en poblaciones bacterianas, con el fin de diseñar terapias de desinfección eficaces para atajar este indeseable fenómeno, Este trabajo ha sido realizado gracias al apoyo de la Agencia Nacional (RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), RYC2019-028006-I/AIE/10.13039/501100011033) y a la 20213AT00 Atracción de Talento para RYC2019 del CSIC, Peer reviewed

8 pages, 5 figures.-- Creative Commons Attribution CC-BY-NC 4.0 license, [EN] A morbidostat is a computer-aided control device to study phenotypic and genotypic real-time dynamics of bacteria adaptation and resistance. This system includes a control mechanism to activate or deactivate the inflow of an antimicrobial substance into a series of microreactors, depending on online measurements of the population’s optical density, thus maintaining a steadystate bacterial concentration across the different cultures. In other words, the morbidostat actuates through the control component to maintain a constant drug selection pressure sufficient for bacterial adaptation or resistance to the antimicrobial substance under study.
The morbidostat control law used so far in the literature consists of a fairly simple algorithm incorporating two conditional rules to determine when the addition of the antimicrobial to the system is necessary. In this work, we show two main results in this context: (1) to carry out simulations and extract conditions under which simple control in morbidostat cannot stabilize the concentration of microorganisms to the target concentration and (2) the realtime control, with stabilization to the target point, of the bacterial concentration using model predictive control., [ES] El morbidostato es un dispositivo de control asistido por ordenador diseñado para estudiar, en tiempo real, la dinámica fenotípica y genotípica de la adaptación y resistencia bacterianas. Dicho sistema incorpora un mecanismo encargado de activar o desactivar el flujo de cierta sustancia antimicrobiana hacia una serie de microreactores, dependiendo de mediciones en línea de la densidad óptica de la población, con el fin de mantener una concentración bacteriana estable en los diferentes cultivos. Así, por medio del componente de control, el morbidostato ejerce una presión constante sobre la selección de las bacterias y su resistencia a la sustancia antimicrobiana que se estudia.
La ley de control a integrar en el morbidostato que se propone habitualmente en la literatura consiste en un algoritmo muy básico basado en dos reglas condicionales para determinar cuando se hace necesaria la incorporación de antimicrobiano al sistema. En este trabajo, se muestran dos resultados principales en este contexto: (1) desempeño de simulaciones para la extracción de condiciones bajo las cuales el control del morbidostato no logra estabilizar la población de microorganismos en la concentración objetivo y (2) el control, en tiempo real con estabilización rápida al punto de consigna, de la población bacteriana cuando se aplican técnicas de control predictivo basado en modelo, NML agradece a la ayuda del CSIC 20213AT001, AP a la ayuda de la Xunta de Galicia IN606A-2020/028 y MRG a la ayuda RTI2018-093560-J-I00 financiada por MCIN/AEI/10.13039/501100011033, Peer reviewed

26 pages, 6 tables, 1 figure.-- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, Fish freshness can be considered as the combination of different nutritional and organoleptic attributes that rapidly deteriorate after fish capture, i.e., during processing (cutting, gutting, packaging), storage, transport, distribution, and retail. The rate at which this degradation occurs is affected by several stress variables such as temperature, water activity, or pH, among others. The food industry is aware that fish freshness is a key feature influencing consumers’ willingness to pay for the product. Therefore, tools that allow rapid and reliable assessment and prediction of the attributes related to freshness are gaining relevance. The main objective of this work is to provide a comprehensive review of the mathematical models used to describe and predict the changes in the key quality indicators in fresh fish and shellfish during storage. The work also briefly describes such indicators, discusses the most relevant stress factors affecting the quality of fresh fish, and presents a bibliometric analysis of the results obtained from a systematic literature search on the subject, We thanks the grants RTI2018-093560-J-I00 funded by MCIN/AEI/10.13039/501100011033, “ERDF A way of making Europe”, the CSIC grant 20213AT001 and Project Pescando4.0 (ref. 202178002) funded by NextGenerationEU funds in the framework of the Plan de Recuperación, Transformación y Resiliencia (PRTR) managed by the Ministerio de Agricultura, Pesca y Alimentación through the RD685/2021, Peer reviewed

14 pages, 3 figures, 1 table.-- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, Unravelling the mechanisms of action of disinfectants is essential to optimise dosing regimes and minimise the emergence of antimicrobial resistance. In this work, we examined the mechanisms of action of a commonly used disinfectant—benzalkonium chloride (BAC)—over a significant pathogen—L. monocytogenes—in the food industry. For that purpose, we used modelling at multiple scales, from the cell membrane to cell population inactivation. Molecular modelling revealed that the integration of the BAC into the membrane requires three phases: (1) the approaching of BAC to the cellular membrane, (2) the absorption of BAC to its surface, and (3) the integration of the compound into the lipid bilayer, where it remains at least for several nanoseconds, probably destabilising the membrane. We hypothesised that the equilibrium of adsorption, although fast, was limiting for sufficiently large BAC concentrations, and a kinetic model was derived to describe time–kill curves of a large population of cells. The model was tested and validated with time series data of free BAC decay and time–kill curves of L. monocytogenes at different inocula and BAC dose concentrations. The knowledge gained from the molecular simulation plus the proposed kinetic model offers the means to design novel disinfection processes rationally., We acknowledge the funding received from the MCIN/AEI/10.13039/501100011033 (grant RTI2018-093560-J-I00, “ERDF A way of making Europe”), from CSIC (grant 20213AT001) and Xunta de Galicia (grant GAIN IN607B 2020/03)., Peer reviewed

10 pages, 5 figures, 1 table.-- Under a Creative Commons license, Food preservatives exhibit a weak antimicrobial effect that is difficult to describe mathematically. Preservatives are commonly added below the Minimum Inhibitory Concentration (MIC) to avoid organoleptic rejection and may exhibit very different antimicrobial effects. In most cases, they inhibit bacteria growth in the first hours, reduce the stationary phase and increase the final death phase. Mathematical descriptions of these dynamics are complex but critical to understanding and optimising food preservation, such as in the case of active packaging.
In this work, we propose a new mathematical model to describe bacteria dynamics with preservatives along the exponential, stationary and death phases. We demonstrate that unknown model parameters can be estimated from measurements of bacterial growth curves at different preservative concentrations (identifiability analysis) and find a closed-form solution of the model that allows us to infer the MIC as well as the bacteriostatic (inhibition of growth rate) and bactericidal (increase on death rate) effects.
To illustrate and validate the model capabilities, we experimentally measure 48 hours growth kinetics of Escherichia coli and Bacillus cereus bacterial strains exposed to carvacrol, a preservative and flavouring agent widely employed in the food industry. We show how the model describes the measured trend during the exponential, stationary and death phases in the log-scaling of bacterial counts and can be used to analyse the antimicrobial properties of carvacrol, We thank the grants RTI2018-093560-J-I00 funded by MCIN/AEI/10.13039/501100011033, “ERDF A way of making Europe”, the CSIC grant 20213AT001 and the Xunta de Galicia, Spain grants IN606A-2020/028 and IN607B-2021/11, Peer reviewed

6 pages, 1 figure.-- XLIV Jornadas de Automática, 6-8 septiembre 2023, Zaragoza.-- Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), [EN] The rapid development of bacterial antimicrobial resistance still promotes the research on mathematical modelling of the mechanisms leading to the emergence, transmission and selection of resistance. The most common approach to explain the dynamics of an antimicrobial-treated population is to classify bacteria into sensitive and resistant. In reality, however, we can find infinite resistance degrees in the population, known as heteroresistance. In this paper, we propose a mathematical model to explain the dynamics of a heteroresistant population, introducing the resistance degree as a random variable indicating the effectiveness of the antimicrobial. We analyse the model’s capabilities and establish its structural observability and identifiability, [ES] El rápido avance de la resistencia bacteriana a los antimicrobianos continúa fomentando la investigación en torno al modelado matemático de los mecanismos que conducen a la emergencia, transmisión y selección de resistencia. El enfoque más extendido para explicar las dinámicas de una población tratada con antimicrobiano consiste en clasificar las bacterias en sensibles y resistentes. Sin embargo, en la realidad existen infinidad de grados de resistencia intermedios en la población, lo que se conoce como heterorresistencia. En este trabajo, se propone un modelo matemático para explicar las dinámicas de una población heterorresistente, introduciendo el grado de resistencia como una variable aleatoria indicadora de la efectividad del antimicrobiano sobre la población. Se analizan las capacidades del modelo y se establecen, además, su observabilidad e identificabilidad estructurales, Este trabajo ha sido realizado gracias al apoyo de las ayudas RTI2018-093560-J-I00 financiadas por MCIN/AEI/10.13039/501100011033, “FEDER Una forma de hacer Europa”, las ayudas del CSIC 20213AT001 y 202270I193 y la ayuda de la Xunta de Galicia IN606A-2020/028, No

1- Videos with molecular dynamics of a BAC molecule integration into a L. monocytogene membrane for different repetitions. It shows that (1) the BAC approaches the cellular membrane, (2) the BAC is adsorbed on its surface, and (3) it is rapidly integrated into the lipid bilayer, where it remains at least for several nanoseconds, probably destabilising the membrane.
2- MATLAB scripts with the kinetic model and data used to simulate time-kill curves of L. monocytogenes with benzalkonium chloride (BAC), a widely used desinfectant, The model explains and predicts the interplay between disinfectant and pathogen at two different initial microbial densities (inocula) and BAC concentrations (dose), We acknowledge the funding received from the MCIN/AEI/10.13039/501100011033 (grant RTI2018-093560-J-I00, "ERDF A way of making Europe"), from CSIC (grant 20213AT001) and Xunta de Galicia (grant GAIN IN607B 2020/03), No

5 pages.-- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, Several factors influence consumers’ choices of food products. While price remains the main criterion, quality, pleasure, convenience, and health are also important driving factors in food market evolution. Food enterprises are making significant efforts to manufacture products that meet consumers’ demands without compromising on safety standards. Additionally, the food industry also aims to improve the efficiency of transformation and conservation processes by minimizing energy consumption, process duration, and waste generation. However, foods are highly complex systems in which: (i) non-linear dynamics and interactions among different temporal and spatial scales must be considered; (ii) a wide range of physical phenomena (such as evaporation, mechanical changes, thawing, energy/mass transport, and color changes) occur; (iii) different food matrices (such as meat, vegetables, cereal, milk, and juices) with different microstructures and properties are involved; and (iv) the number of quality and safety indicators (such as bacteria, total volatile basic nitrogen, color, texture, odor, and sensory characteristics) is substantial. Mathematical modeling and simulation are key elements that allow us to gain a deeper understanding of food processes and enable the use of tools such as optimization and real-time control to improve their efficiency. This special issue aims to gather research on the development of dynamic mathematical models that describe the relevant factors in food processes from the perspectives of food safety (chemical or microbiological), food quality (organoleptic or nutritional), or resource consumption. Additionally, the development of model-based tools to improve food processes is also considered. This includes decision-making and optimization tools, the characterization of uncertainty/variability in model predictions, model simulation techniques, software sensors, and software development. The contributions published in this Special Issue can be grouped into two categories according to their main research topic: the evolution of safety and quality indicators in unprocessed food systems, and transformation and preservation processes, We acknowledge receipt of the following grants: RTI2018-093560-J-I00, funded by Spanish Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 “ERDF—A way of making Europe”; Project Pescando4.0 (ref. 202178002), funded by NextGenerationEU within the framework of the Plan de Recuperación, Transformación y Resiliencia (PRTR) and managed by the Ministerio de Agricultura, Pesca y Alimentación through the RD 685/2021; and Project MODELSANO, funded by the Spanish MINECO (grant number PID2019-104212RB-I00/AEI/10.13039/501100011033), Peer reviewed

Poster.-- Close Encounters IIM (2nd Kind) Virtual Conference, 26th February 2021, Funding RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), IN6062020/028 (Xunta de Galicia predoctoral grant), No

The zip file containes two types of files:
- HTLM files. These files contain a structured bibliometric analysis regarding the most commonly used indicators to assess fish quality/freshness. The main file (Appendix_collection.html) contains an summary table. The bibliometric analysis of the different indicators can be accessed from this file in an interactive way, via the corresponding hyperlinks.
- PDF files. File "FishQualityBib.pdf" contains the bibliometric analysis of the whole collection (all quality indicators together). File "QualityFish.pdf" contains a separate bibliometric analysis for each quality indicator., This repository contains the bibliometric search results discussed in the manuscript: García, MR, Férez-Rubio, JA, Vilas, C. (2022). Assessment and Prediction of Fish Freshness using Mathematical Modelling: A Review. MDPI Foods, 11(15):2312. DOI:10.3390/foods11152312., This work has been funded by the projects RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE) and RYC2019-028006-I/AIE/10.13039/501100011033, No

12 files, A set of mathematical models (Matlab and R code) to describe and analyse the effect of sub-MIC concentrations of antimicrobials in bacterial growth: a classical logistic model with disinfectant inhibition following Weibull functions and a dynamic model based on mechanisms of adaptation, growth, inhibition and death. Both models, as well as their generated results employing didecyldimethylammonium chloride (DDAC) in Bacillus cereus and Escherichia coli, are included and commented in the work "Kinetics of bacterial adaptation, growth and death at didecyldimethylammonium chloride sub-MIC concentrations" (https://doi.org/10.3389/fmicb.2022.758237) by Adrián Pedreira, José A. Vázquez and Míriam R. García. Please refer also to this publication if using these models, This work has been funding by the projects RTI2018-093560-J-I00 (MCIU/AEI/FEDER, UE), RYC2019-028006-I/AIE/10.13039/501100011033 and the Xunta de Galicia Grants IN606A-2020/028 and IN607B-2021/11, No

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

In order to run these scripts, users will need:

- a Matlab R2015 (or later) installation, under Windows or Linux operating systems
- the AMIGO2 toolbox is available at: https://sites.google.com/site/amigo2toolbox/download (Eva Balsa-Canto IIM-CSIC)
- Originally the code was tested in Matlab2016b and AMIGO2019

Users need to make sure that the above AMIGO2 toolbox is fully functional before attempting to run the scripts. Please refer to the AMIGO2 documentation.

INSTRUCTIONS

1 Open Matlab and go to the folder where the file MAIN is
2 Run BAC_Ecoli_main and add the AMIGO path when asked
- If it does not work, you probably need to configurate AMIGO2 in matlab to use c++. See https://sites.google.com/site/amigo2toolbox for details
- Alternatively,
- edit Kinetics files (Kinetics_calibration & Kinetics_validtion') and change line inputs.model.input_model_type='charmodelC'; by inputs.model.input_model_type='charmodelM';
- To assure the optimum is achieved, increase optimization time (inputs.nlpsol.eSS.maxtime), Here we provide the scripts with the model and data (provided by Marta L. Cabo IIM-CSIC) used to simulate the time-kill curves of L. monocytogenes with benzalkonium chloride the most common quaternary ammonium compound.

The model explains and predicts the interplay between disinfectant and pathogen at different initial microbial densities (inocula) and dose concentrations. More information can be found in Mechanisms of Listeria monocytogenes disinfection with Benzalkonium chloride: from molecular dynamics to kinetics of time-kill curves. DOI: 10.1101/2022.06.21.497115, We acknowledge the funding received from the MCIN/AEI/10.13039/501100011033 (grant RTI2018-093560-J-I00, "ERDF A way of making Europe"), from CSIC (grant 20213AT001) and Xunta de Galicia (grant GAIN IN607B 2020/03, 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

Unravelling the mechanisms of action of disinfectants is essential to optimise dosing regimes and minimise the emergence of antimicrobial resistance. In this work, we examined the mechanisms of action of a commonly used disinfectant—benzalkonium chloride (BAC)—over a significant pathogen—L. monocytogenes—in the food industry. For that purpose, we used modelling at multiple scales, from the cell membrane to cell population inactivation. Molecular modelling revealed that the integration of the BAC into the membrane requires three phases: (1) the approaching of BAC to the cellular membrane, (2) the absorption of BAC to its surface, and (3) the integration of the compound into the lipid bilayer, where it remains at least for several nanoseconds, probably destabilising the membrane. We hypothesised that the equilibrium of adsorption, although fast, was limiting for sufficiently large BAC concentrations, and a kinetic model was derived to describe time–kill curves of a large population of cells. The model was tested and validated with time series data of free BAC decay and time–kill curves of L. monocytogenes at different inocula and BAC dose concentrations. The knowledge gained from the molecular simulation plus the proposed kinetic model offers the means to design novel disinfection processes rationally., Agencia Estatal de Investigación | Ref. RTI2018-093560-J-I00, Centro Superior de Investigaciones Científicas | Ref. 20213AT001, Xunta de Galicia | Ref. GAIN IN607B 2020/03