Novel methods to monitor the biodegradation of polylactic acid (PLA) by Amycolatopsis orientalis and Amycolatopsis thailandensis

Plastics are essential in modern life, but their conventional production is problematic due to environmental pollution and waste management issues. Polylactic acid (PLA) is a widely used bioplastic that is bio-based and biodegradable, making it a key player in the bioeconomy. PLA has been proven to be degradable in various settings, including aqueous, soil, and compost environments. However, monitoring and optimizing PLA biodegradation remains challenging. This study proposes methods to improve the quantification of PLA biodegradation by Amycolatopsis spp. Ultrasound treatments (10 s) significantly improved the enumeration of viable Amycolatopsis cells by breaking the pellets into quantifiable individual cells. A separation technique combining ultrasound (120 s) and 40 μm cell strainers effectively isolated PLA particles from biomass to quantify PLA weight loss. This enabled the monitoring of PLA biofragmentation. Finally, CO2 production was measured according to ISO 14852 to quantify mineralization. Integrating these methods provides an improved quantification for PLA biodegradation along its different stages. In a case study, this led to the construction of a carbon balance where 85.1% of initial carbon content was successfully tracked. The developed techniques for monitoring of PLA biodegradation are essential to design future waste management strategies for biodegradable plastics.

Macroscopic modeling of the growth and substrate consumption of wild type and genetically modified Pichia pastoris

Pichia pastoris is a popular yeast platform to generate several industrially relevant products which have applications in a wide range of sectors. The complexities in the processes due to the addition of a foreign gene are not widely explored. Since these complexities can be dependent on the strain characteristics, promoter, and type of protein produced, it is vital to investigate the growth and substrate consumption patterns of the host to facilitate customized process optimization. In this study, the growth rates of P. pastoris GS115 wild type (WT) and genetically modified (GM) strains grown on glycerol and methanol in batch cultivation mode were estimated and the model providing the best representation of the true growth kinetics based on substrate consumption was identified. It was observed that the growth of P. pastoris exhibits Haldane kinetics on glycerol rather than the most commonly used Monod kinetics due to the inability of the latter to describe growth inhibition at high concentrations of glycerol. Whereas, the cardinal parameter model, a newly proposed model for this application, was found to be the best fitting to describe the growth of P. pastoris on methanol due to its ability to describe methanol toxicity. Interestingly, the findings from this study concluded that in both substrates, the genetically engineered strain exhibited a higher growth rate compared to the WT strain. Such an observation has not been established yet in other published works, indicating an opportunity to further optimize the carbon source feeding strategies when the host is grown in fed-batch mode.

Gut microbiota of the small intestine as an antimicrobial barrier against foodborne pathogens: Impact of diet on the survival of S. Typhimurium and L. monocytogenes during in vitro digestion

The human gastrointestinal tract employs an assortment of chemical, enzymatic and immune barriers to impede pathogen colonization. An essential component of these barriers is the gut microbiota, which infers protection against ingested pathogens through its colonization resistance mechanisms. Specifically, the gut microbiota of the distal small intestine (ileum) renders a crucial line of defense, given that this location is regarded as an important interaction site. This study aimed to evaluate the impact of the ileal microbiota on the survival of the foodborne pathogens Salmonella enterica serotype Typhimurium and Listeria monocytogenes, utilizing an in vitro digestion model system. Moreover, the effect of diet on the gut microbiota colonization resistance mechanisms was assessed, by comparing a healthy (high fiber/low sugar) and a western diet (low fiber/high sugar). For S. Typhimurium, the results revealed that the digestion of a healthy diet led to a similar inactivation compared to the western diet, with the values of total log reduction being 0.83 and 0.82 log(CFU), respectively; yet the lack of readily accessible nutrients in the healthy diet combined with the acidic shock during gastric digestion caused the induction of stress tolerance to the pathogen. This resulted in increased pathogen survival in the presence of gut microbiota, with S. Typhimurium proliferating during the ileal phase with a maximum specific growth rate of 0.16 1/h. On the contrary, for L. monocytogenes, the healthy diet was associated with a greater inactivation than the western diet (total log reduction values: 3.08 and 1.30 log(CFU), respectively), which appeared strongly influenced by the encounter of the pathogen with the gut microbiota. Regarding the latter, the species Escherichia coli and Bacteroides thetaiotaomicron appeared to be the most prevalent in most cases. Finally, it was also demonstrated that the ileal microbiota colonization resistance mechanisms largely relied on competitive responses. The obtained knowledge of this research can contribute to the development and/or complementation of defensive strategies against pathogen infection, while also underlining the value of in vitro approaches.

Effect of food structure and buffering capacity on pathogen survival during in vitro digestion

Even though a plethora of barriers are employed by the human gastrointestinal tract (GIT) to cope with invading pathogens, foodborne diseases are still a common problem. The survival of food pathogens in the GIT is known to depend on food carrier properties. The aim of this study was to investigate the influence of food buffering capacity and food structure on the survival of Salmonella Typhimurium and Listeria monocytogenes during simulated digestion, following contamination of different food model systems that had different combinations of fat and protein content. The results illustrated the strong protective properties of proteins, acting either as a strong buffering agent or as a physical barrier against gastric acidity, for both pathogens. In comparison, fat manifested a lower buffering capacity and weaker protective effects against the two pathogens. Intriguingly, a low fat content was often linked with increased microbial resistance. Nonetheless, both pathogens survived their transit through the simulated GIT in all cases, with S. Typhimurium exhibiting growth during intestinal digestion and L.monocytogenes demonstrating a healthy residual population at the end of the intestinal phase. These results corroborate the need for a deeper understanding regarding the mechanisms with which food affects bacterial survival in the human GIT.

Enhancing the biodegradation of (bio)plastic through pretreatments: A critical review

As plastic packaging becomes nearly indispensable in the plastic economy, rigorous efforts have been made to recapture the material value form this waste stream, which is mostly composed of highly resistant plastics. Biodegradation offers an attractive alternative for conventional plastic waste treatment as this approach is environmentally friendly, has low cost and facilitates valorisation. Moreover, there is also an increasing interest in plastic pretreatments waste to enhance biodegradation. This review investigates the pretreatment methods that optimise plastic biodegradation by examining the process's mechanisms and key influencing factors, which can be categorised into: biotic factors, abiotic factors and polymer characteristics. Various types of chemical and physical pretreatments have demonstrated to effectively enhance biodegradation through oxidation and surface changes on the plastics, leading to increased bioconversion rates and biogas production. A critical evaluation of the various categories of pretreatment methods is presented. This evaluation leads to the conclusion that the category of non-thermal physical treatments is most promising, due to the relatively low energy requirements and the absence of a need for chemical additions. Moreover, non-thermal physical treatments have demonstrated application potential at large scale. Based on these conclusions, pretreatments are expected to be an integral part of the biodegradation of plastics within a circular economy approach.

Processing Method for the Quantification of Methanol and Ethanol from Bioreactor Samples Using Gas Chromatography-Flame Ionization Detection

Methanol, a simple polar solvent, has been widely identified as an attractive carbon source to produce chemicals and fuels in bioprocesses. Specifically, to achieve recombinant protein production from methylotrophic yeasts, such as Pichia pastoris, this organic solvent can be used as a sole carbon source for growth and maintenance as well as an inducer for protein expression. However, if methanol feeding is not controlled well in such a fermentation process, accumulation of the solvent in the growth media will have a detrimental effect on the cells. Hence, monitoring the levels of methanol in these fermentation processes is a crucial step to ensure a healthy culture and maximum protein production. There are various techniques elaborated in the literature for monitoring methanol in cell cultures, but often, they appear to be expensive methods that are less affordable for many laboratories. This is because, in addition to the sophisticated equipment that is required for the analysis, the complexity of the samples retrieved from the bioprocesses necessitates laborious processing steps often involving expensive tools. In this study, a fast, simple, and sensitive method is developed to process biological samples by using the salting-out-assisted liquid-liquid extraction technique to quantify the concentration of methanol and ethanol using gas chromatography. On comparing the combinations of widely available salts and solvents, it was noticed that salting out using potassium carbonate followed by the liquid-liquid extraction of the analyte using ethyl acetate showed the best recovery. Followed by this, a validation test for the developed method was performed, which resulted in good peak resolution, linearity, and limit of detection for the quantitation of methanol and ethanol. By further assessing the tested combination, it was confirmed that its application could be extended to other matrices. Such an approach facilitates the possibility to monitor and control the methanol levels in fermentation and aids in bioprocess optimization.

A protocol for the cultivation and monitoring of ileal gut microbiota surrogates

AIMS

This research aimed to develop and validate a cultivation and monitoring protocol that is suitable for a surrogate microbial community that accounts for the gut microbiota of the ileum of the small intestine.

METHODS AND RESULTS

Five bacterial species have been selected as representatives of the ileal gut microbiota and a general anaerobic medium (MS-BHI, as minimally supplemented BHI) has been constructed and validated against BCCM/LGM recommended and commercial media. Moreover, appropriate selective/differential media have been investigated for monitoring each ileal gut microbiota surrogate. Results showed that MS-BHI was highly efficient in displaying individual and collective behavior of the ileal gut microbiota species, when compared with other types of media. Likewise, the selective/differential media managed to identify and describe the behavior of their targeted species.

CONCLUSIONS

MS-BHI renders a highly efficient, inexpensive and easy-to-prepare cultivation and enumeration alternative for the surrogate ileal microbiota species. Additionally, the selective/differential media can identify and quantify the bacteria of the surrogate ileal microbial community.

SIGNIFICANCE AND IMPACT OF STUDY

The selected gut microbiota species can represent an in vitro ileal community, forming the basis for future studies on small intestinal microbiota. MS-BHI and the proposed monitoring protocol can be used as a standard for gut microbiota studies that utilize conventional microbiological techniques.

An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations

Variability in the behavior of microbial foodborne pathogens and spoilers causes difficulties in predicting the safety and quality of food products during their shelf life. Therefore, the quantification of the individual microbial lag phase distribution is of high relevance to the field of quantitative microbial risk assessment. To construct models that predict the effect of changes in environmental conditions on the individual lag, an accurate determination of these distributions is required. Therefore, the current research focuses on the development of an experimental and computational method for accurate determination of individual lag phase distribution. The experimental method is unique in the sense that full liquid volumes are sampled without using dilutions to detect the final population, thereby minimizing experimental errors. Moreover, the method does not aim at the isolation of single cells but at a low number of cells. The fact that several cells can be present in the initial samples instead of having a single cell is considered by the computational method. This method relies on Monte Carlo simulation to predict the individual lag phase distribution for a given set of distribution parameters and maximum likelihood estimation to find the parameters that describe the experimental data best. The method was validated both through simulation and experiments and was found to deliver a desired accuracy.

Visible Light as an Antimicrobial Strategy for Inactivation of Pseudomonas fluorescens and Staphylococcus epidermidis Biofilms

The increase of antimicrobial resistance is challenging the scientific community to find solutions to eradicate bacteria, specifically biofilms. Light-Emitting Diodes (LED) represent an alternative way to tackle this problem in the presence of endogenous or exogenous photosensitizers. This work adds to a growing body of research on photodynamic inactivation using visible light against biofilms. Violet (400 nm), blue (420 nm), green (570 nm), yellow (584 nm) and red (698 nm) LEDs were used against Pseudomonas fluorescens and Staphylococcus epidermidis. Biofilms, grown on a polystyrene surface, were irradiated for 4 h. Different irradiance levels were investigated (2.5%, 25%, 50% and 100% of the maximum irradiance). Surviving cells were quantified and the inactivation kinetic parameters were estimated. Violet light could successfully inactivate P. fluorescens and S. epidermidis (up to 6.80 and 3.69 log10 reduction, respectively), while blue light was effective only against P. fluorescens (100% of maximum irradiance). Green, yellow and red irradiation neither increased nor reduced the biofilm cell density. This is the first research to test five different wavelengths (each with three intensities) in the visible spectrum against Gram-positive and Gram-negative biofilms. It provides a detailed study of the potential of visible light against biofilms of a different Gram-nature.

Bioproduction of the Recombinant Sweet Protein Thaumatin: Current State of the Art and Perspectives

There is currently a worldwide trend to reduce sugar consumption. This trend is mostly met by the use of artificial non-nutritive sweeteners. However, these sweeteners have also been proven to have adverse health effects such as dizziness, headaches, gastrointestinal issues, and mood changes for aspartame. One of the solutions lies in the commercialization of sweet proteins, which are not associated with adverse health effects. Of these proteins, thaumatin is one of the most studied and most promising alternatives for sugars and artificial sweeteners. Since the natural production of these proteins is often too expensive, biochemical production methods are currently under investigation. With these methods, recombinant DNA technology is used for the production of sweet proteins in a host organism. The most promising host known today is the methylotrophic yeast, Pichia pastoris. This yeast has a tightly regulated methanol-induced promotor, allowing a good control over the recombinant protein production. Great efforts have been undertaken for improving the yields and purities of thaumatin productions, but a further optimization is still desired. This review focuses on (i) the motivation for using and producing sweet proteins, (ii) the properties and history of thaumatin, (iii) the production of recombinant sweet proteins, and (iv) future possibilities for process optimization based on a systems biology approach.

A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli

Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.

Modeling the effect of pH, water activity, and ethanol concentration on biofilm formation of Staphylococcus aureus

In this work, the effect of environmental factors on Staphylococcus aureus (ATCC 13150) biofilm formation in tryptic soy broth was investigated under different ranges of pH (3.0-9.5), ethanol concentration (EtOH 0.0-20.0%), and a_w (NaCl, 0.866-0.992). Biofilm formation was quantified using the crystal violet staining method and optical density (OD: 590 nm) measurements. Biofilm formation was significantly stronger at pH and a_w close to S. aureus optimal growth conditions, while it was high at EtOH around 2.5-3.5%. Data sets from the difference between the OD measurements of the test and control (ΔOD) were fitted to the cardinal parameter model (CPM) and cardinal parameter model with inflection (CPMI) to describe the effect of the environmental factors. The models showed good quality of fit for the experimental data in terms of calculated RMSE, with the latter ranging from 0.276 to 0.455. CPM gave a good quality of fit compared to CPMI for the environmental factors tested. Optimal pH was close to neutral (6.76-6.81) and biofilm formation was possible till pH = 3.81-3.78 for CPM and CPMI, respectively. Optimum EtOH and a_w conditions for biofilm formation were in the range of 1.99-2.75 and 0.98-0.97, respectively. Predicted OD values observed using strain 13150 were very closely correlated to the OD values predicted with strain 12600 with R² of 0.978, 0.991, and 0.947 for pH, EtOH, and a_w, respectively. The cultivable bacterial cells within the biofilm were enumerated using standard plate counting and a linear model was applied to correlate the attached biofilm cells to ΔOD of biofilm formation. It was found that the biofilm formation correlated with S. aureus population growth. At 2.5-3.5% of EtOH the maximum population density was lower than that observed at 0.0% of EtOH. As 2.5-3.5% of EtOH initiated a stronger biofilm formation, biofilm formation seems to be induced by ethanol stress. The development of cardinal parameter models to describe the effect environmental factors of importance to biofilm formation, offers a promising predictive microbiology approach to decrypting the S. aureus population growth and survival ability on food processing surfaces.

A tutorial on uncertainty propagation techniques for predictive microbiology models: A critical analysis of state-of-the-art techniques

Building mathematical models in predictive microbiology is a data driven science. As such, the experimental data (and its uncertainty) has an influence on the final predictions and even on the calculation of the model prediction uncertainty. Therefore, the current research studies the influence of both the parameter estimation and uncertainty propagation method on the calculation of the model prediction uncertainty. The study is intended as well as a tutorial to uncertainty propagation techniques for researchers in (predictive) microbiology. To this end, an in silico case study was applied in which the effect of temperature on the microbial growth rate was modelled and used to make simulations for a temperature profile that is characterised by variability. The comparison of the parameter estimation methods demonstrated that the one-step method yields more accurate and precise calculations of the model prediction uncertainty than the two-step method. Four uncertainty propagation methods were assessed. The current work assesses the applicability of these techniques by considering the effect of experimental uncertainty and model input uncertainty. The linear approximation was demonstrated not always to provide reliable results. The Monte Carlo method was computationally very intensive, compared to its competitors. Polynomial chaos expansion was computationally efficient and accurate but is relatively complex to implement. Finally, the sigma point method was preferred as it is (i) computationally efficient, (ii) robust with respect to experimental uncertainty and (iii) easily implemented.

Occurrence, distribution and contamination levels of heat-resistant moulds throughout the processing of pasteurized high-acid fruit products

Heat-resistant moulds (HRMs) are well known for their ability to survive pasteurization and spoil high-acid food products, which is of great concern for processors of fruit-based products worldwide. Whilst the majority of the studies on HRMs over the last decades have addressed their inactivation, few data are currently available regarding their contamination levels in fruit and fruit-based products. Thus, this study aimed to quantify and identify heat-resistant fungal ascospores from samples collected throughout the processing of pasteurized high-acid fruit products. In addition, an assessment on the effect of processing on the contamination levels of HRMs in these products was carried out. A total of 332 samples from 111 batches were analyzed from three processing plants (=three processing lines): strawberry puree (n = 88, Belgium), concentrated orange juice (n = 90, Brazil) and apple puree (n = 154, the Netherlands). HRMs were detected in 96.4% (107/111) of the batches and 59.3% (197/332) of the analyzed samples. HRMs were present in 90.9% of the samples from the strawberry puree processing line (1-215 ascospores/100 g), 46.7% of the samples from the orange juice processing line (1-200 ascospores/100 g) and 48.7% of samples from the apple puree processing line (1-84 ascospores/100 g). Despite the high occurrence, the majority (76.8%, 255/332) of the samples were either not contaminated or presented low levels of HRMs (<10 ascospores/100 g). For both strawberry puree and concentrated orange juice, processing had no statistically significant effect on the levels of HRMs (p > 0.05). On the contrary, a significant reduction (p < 0.05) in HRMs levels was observed during the processing of apple puree. Twelve species were identified belonging to four genera - Byssochlamys, Aspergillus with Neosartorya-type ascospores, Talaromyces and Rasamsonia. N. fumigata (23.6%), N. fischeri (19.1%) and B. nivea (5.5%) were the predominant species in pasteurized products. The quantitative data (contamination levels of HRMs) were fitted to exponential distributions and will ultimately be included as input to spoilage risk assessment models which would allow better control of the spoilage of heat treated fruit products caused by heat-resistant moulds.

Mechanistic modelling of the inhibitory effect of pH on microbial growth

Modelling and simulation of microbial dynamics as a function of processing, transportation and storage conditions is a useful tool to improve microbial food safety and quality. The goal of this research is to improve an existing methodology for building mechanistic predictive models based on the environmental conditions. The effect of environmental conditions on microbial dynamics is often described by combining the separate effects in a multiplicative way (gamma concept). This idea was extended further in this work by including the effects of the lag and stationary growth phases on microbial growth rate as independent gamma factors. A mechanistic description of the stationary phase as a function of pH was included, based on a novel class of models that consider product inhibition. Experimental results on Escherichia coli growth dynamics indicated that also the parameters of the product inhibition equations can be modelled with the gamma approach. This work has extended a modelling methodology, resulting in predictive models that are (i) mechanistically inspired, (ii) easily identifiable with a limited work load and (iii) easily extended to additional environmental conditions.

Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information

Clustered microbial communities are omnipresent in the food industry, e.g., as colonies of microbial pathogens in/on food media or as biofilms on food processing surfaces. These clustered communities are often characterized by metabolic differentiation among their constituting cells as a result of heterogeneous environmental conditions in the cellular surroundings. This paper focuses on the role of metabolic differentiation due to oxygen gradients in the development of Escherichia coli cell communities, whereby low local oxygen concentrations lead to cellular secretion of weak acid products. For this reason, a metabolic model has been developed for the facultative anaerobe E. coli covering the range of aerobic, microaerobic, and anaerobic environmental conditions. This metabolic model is expressed as a multiparametric programming problem, in which the influence of low extracellular pH values and the presence of undissociated acid cell products in the environment has been taken into account. Furthermore, the developed metabolic model is incorporated in MICRODIMS, an in-house developed individual-based modeling framework to simulate microbial colony and biofilm dynamics. Two case studies have been elaborated using the MICRODIMS simulator: (i) biofilm growth on a substratum surface and (ii) submerged colony growth in a semi-solid mixed food product. In the first case study, the acidification of the biofilm environment and the emergence of typical biofilm morphologies have been observed, such as the mushroom-shaped structure of mature biofilms and the formation of cellular chains at the exterior surface of the biofilm. The simulations show that these morphological phenomena are respectively dependent on the initial affinity of pioneer cells for the substratum surface and the cell detachment process at the outer surface of the biofilm. In the second case study, a no-growth zone emerges in the colony center due to a local decline of the environmental pH. As a result, cellular growth in the submerged colony is limited to the colony periphery, implying a linear increase of the colony radius over time. MICRODIMS has been successfully used to reproduce complex dynamics of clustered microbial communities.

An interaction model for the combined effect of temperature, pH and water activity on the growth rate of E. coli K12

Previous research has indicated that more complex model structures than the commonly used gamma model are needed to obtain an accurate prediction of the effect of multiple environmental conditions on the microbial growth rate. Due to the complexity associated with the development of such model structures, it is recommended that the model structure is compatible with a modular model building method. In this research, a gamma-interaction model was built to describe the combined effect of temperature, pH and water activity on the microbial growth rate of E. coli K12 based on a dataset of 68 bioreactor experiments. This novel interaction model was compared with the standard gamma model. The model structures were tested separately for the combined effects of (i) temperature and pH, (ii) pH and water activity, (iii) temperature and water activity and (iv) temperature, pH and water activity. Based on the results of this research, it was concluded that models for the combined effect of environmental conditions need to allow for sufficient flexibility for the description of combined effects of environmental conditions to obtain accurate model predictions. In the current study, this flexibility was successfully introduced by using the gamma-interaction model. A cross-validation study also demonstrated that the predictions of the interaction model are more robust with respect to the specific data used than the gamma model. As such, the gamma-interaction model provides food producers and food safety authorities with a more accurate and reliable tool for the prediction of the microbial growth rate as a function of multiple environmental conditions.

Introducing a novel interaction model structure for the combined effect of temperature and pH on the microbial growth rate

Efficient modelling of the microbial growth rate can be performed by combining the effects of individual conditions in a multiplicative way, known as the gamma concept. However, several studies have illustrated that interactions between different effects should be taken into account at stressing environmental conditions to achieve a more accurate description of the growth rate. In this research, a novel approach for modeling the interactions between the effects of environmental conditions on the microbial growth rate is introduced. As a case study, the effect of temperature and pH on the growth rate of Escherichia coli K12 is modeled, based on a set of computer controlled bioreactor experiments performed under static environmental conditions. The models compared in this case study are the gamma model, the model of Augustin and Carlier (2000), the model of Le Marc et al. (2002) and the novel multiplicative interaction model, developed in this paper. This novel model enables the separate identification of interactions between the effects of two (or more) environmental conditions. The comparison of these models focuses on the accuracy, interpretability and compatibility with efficient modeling approaches. Moreover, for the separate effects of temperature and pH, new cardinal parameter model structures are proposed. The novel interaction model contributes to a generic modeling approach, resulting in predictive models that are (i) accurate, (ii) easily identifiable with a limited work load, (iii) modular, and (iv) biologically interpretable.