Hydrolysis of β-casein by the cell-envelope-located PI-type protease of Lactococcus lactis: A modelling approach

Muñoz-Tamayo, Rafael; Groot, J. de; Bakx, Edwin J.; Wierenga, Peter A.; Gruppen, Harry; Zwietering, Marcel H.; Sijtsma, L.

doi:10.1016/j.idairyj.2011.03.012

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…These parameters were estimated using a global multi-response optimization method 35 , i.e., by fitting hydroperoxide and aldehyde profiles simultaneously, using MATLAB 2021b software (Mathworks, Natick, MA, USA) 36 . The optimization procedure was applied to fit experimental datasets with 1 µM BODIPY 665/676.…”

Section: Estimation Of Kinetic Constants and Initial Radical Concentr...mentioning

confidence: 99%

Droplet size dependency and spatial heterogeneity of lipid oxidation in whey protein isolate-stabilized emulsions

Yang,

ten Klooster,

Nguyen

et al. 2023

Preprint

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Spatiotemporal assessment of lipid and protein oxidation is key for understanding quality deterioration in emulsified food products containing polyunsaturated fatty acids. In this work, we first mechanistically validated the use of the lipid oxidation-sensitive fluorophore BODIPY 665/676 as a semi-quantitative marker for local peroxyl radical formation. Next, we assessed the impact of microfluidic and colloid mill emulsification on local protein and lipid oxidation kinetics in whey protein isolate (WPI)-stabilized emulsions. For that purpose, we also used BODIPY 581/591 C11 and CAMPO-AFDye 647 as colocalisation markers for lipid and protein oxidation. The polydisperse emulsions showed an inverse relation between droplet size and lipid oxidation rate. Further, we observed less protein and lipid oxidation occurring in similar sized droplets in monodisperse emulsions. This observation was linked to more heterogeneous protein packing at the droplet surface during colloid mill emulsification, resulting in larger inter-droplet heterogeneity in both protein and lipid oxidation. Our findings indicate the critical roles of emulsification methods and droplet sizes in understanding and managing lipid oxidation.

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Section: Estimation Of Kinetic Constants and Initial Radical Concentr...mentioning

confidence: 99%

Droplet size dependency and spatial heterogeneity of lipid oxidation in whey protein isolate-stabilized emulsions

Yang,

ten Klooster,

Nguyen

et al. 2023

Preprint

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“…The concepts that constitute the proposed framework to analyse parameter interpretability are defined and summarized in Table 1. For the sake of clarity, the conceptual framework is studied using a simple mathematical model that describes the dynamics of enzymatic hydrolysis of β -casein by a Lactococcus lactis bacterium in a batch system [27]. The basic structure of the model is obtained from applying a component mass balance, which results in the following unique differential equation: where x (in μ M) is the concentration of the substrate and r (⋅) ( μ M/min) is the reaction rate, using the symbol (⋅) to indicate the dependency of this structural parameter with respect to time and any other variable or parameter of the model.…”

Section: Setting a Conceptual Framework For Interpretability Analysismentioning

confidence: 99%

“…Multiple mathematical functions exist to define r (⋅) and describe the hydrolysis rate of the intact β -casein. In the study here analyzed [27], the authors evaluate four kinetic candidate functions to determine the best function for r (⋅) parameter in terms of the goodness of fit: First-order kinetics: nth -order kinetics: Michaelis-Menten kinetics: Competitive inhibition kinetics: with I = x 0 − x . This expression can be further manipulated to reduce the number of its parameters as: with where E is the enzyme concentration, measured in optical density units (OD 600 ).…”

Section: Setting a Conceptual Framework For Interpretability Analysismentioning

confidence: 99%

“…Following the case when r (⋅) is specified by the first-order kinetic rate as in (2)), let’s analyze the parameter interpretability (the analysis also applies to other candidate kinetic functions, bearing in mind that the Michaelis-Menten equation is derived from a biological hypothesis on the enzyme action and thus its parameters have a stronger level of interpretability than for instance those of the kinetic of order n ). By analyzing different experimental conditions, it was found that the hydrolysis rate of β -casein was dependent of the initial protein concentration x 0 [27]. That is, the kinetic rate was slower at higher initial protein concentrations.…”

Section: Setting a Conceptual Framework For Interpretability Analysismentioning

confidence: 99%

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On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Lema-Perez¹,

Muñoz-Tamayo

García-Tirado

et al. 2018

Preprint

Self Cite

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Empirical and phenomenological based models are used to represent biological and physiological processes. Phenomenological models are derived from the knowledge of the mechanisms that underlie the behaviour of the system under study, while empirical models are derived from analysis of data to quantify relationships between variables of interest. For studying biological systems, the phenomenological modeling approach offers the great advantage of having a structure with variables and parameters with physical meaning that enhance the interpretability of the model and its further used for decision making. The interpretability property of models, however, remains a vague concept. In this study, we tackled the interpretability property for parameters of phenomenological-based models. To our knowledge, this property has not been deeply discussed, perhaps by the implicit assumption that interpretability is inherent to the phenomenological-based models. We propose a conceptual framework to address the parameter interpretability and its implications for parameter identifiability. We use as battle horse a simple but relevant model representing the enzymatic degradation of β−casein by a Lactococcus lactis bacterium.How can we assess the capability of a mathematical model to provide mech-2 anistic insight on the system under study? That is, how the mathematical 3 structure of the model translates and captures the knowledge of the phenomena 4 taking place in the system? To what extent can we interpret mechanistically our 5 model? In biotechnology, biology, and biomedical fields two main approaches ex-6 ist to model processes of interest, namely empirical and phenomenological based 7 modeling. Empirical based models are derived from data, while phenomenolog-8 ical based models are derived from knowledge about the process. In biomedical 9 fields, phenomenological based models are more relevant than empirical based 10 models since, in addition to prediction, their parameters and variables provide 11 information that can be used to perform diagnosis, discriminate clinical risk 12 groups and guide treatment for stratifying patients by disease severity [1, 2]. In 13 spite of this, in the fields mentioned before many models have been developed 14 from an empirical point of view by using black box modeling approaches like 15 machine learning and fuzzy models. Machine learning models, for example, are 16 increasingly used in the field of medicine and healthcare but there is still an 17 inability by humans to understand how those models work and what meaning 18 their parameters have. Some approaches have been proposed to improving the 19 level of explanation and interpretability of such emprirical models, that is to 20 open the black box [3]. The deployment of the above mentioned approaches en-21 counters its first hurdle by the difficulty of formalising the definition of central 22 concepts such as transparency, explanation, and interpretability. In the present 23 work, we focus on the interpretability concept but applied to phenomeno...

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Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation

Flahaut

Wiersma

Bunt

et al. 2013

Appl Microbiol Biotechnol

106

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Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

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Hydrolysis of β-casein by the cell-envelope-located PI-type protease of Lactococcus lactis: A modelling approach

Cited by 11 publications

References 32 publications

Droplet size dependency and spatial heterogeneity of lipid oxidation in whey protein isolate-stabilized emulsions

Droplet size dependency and spatial heterogeneity of lipid oxidation in whey protein isolate-stabilized emulsions

On Parameter Interpretability of Phenomenological-Based Semiphysical Models

Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation

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