An Alternative Approach to Non-Log-Linear Thermal Microbial Inactivation: Modelling the Number of Log Cycles Reduction with Respect to Temperature

A mathematical approach incorporating the shoulder effect during the quantification of microbial heat inactivation is being developed based on »the number of log cycles of reduction « concept. Hereto, the heat resistance of Escherichia coli K12 in BHI broth has been quantitatively determined in a generic and accurate way by defining the time t for x log reductions in the microbial population, i.e. txD, as a function of the treatment temperature T. Survival data of the examined microorganism are collected in a range of temperatures between 52–60.6 °C. Shoulder length Sl and specific inactivation rate kmax are derived from a mathematical expression that describes a non-log-linear behaviour. The temperature dependencies of Sl and kmax are used for structuring the txD(T) function. Estimation of the txD(T) parameters through a global identification procedure permits reliable predictions of the time to achieve a pre-decided microbial reduction. One of the parameters of the txD(T) function is proposed as »the reference minimum temperature for inactivation«. For the case study considered, a value of 51.80 °C (with a standard error, SE, of 3.47) was identified. Finally, the time to achieve commercial sterilization and pasteurization for the product at hand, i.e. BHI broth, was found to be 11.70 s (SE=5.22), and 5.10 min (SE=1.22), respectively. Accounting for the uncertainty (based on the 90 % confidence intervals, CI) a fail-safe treatment of these two processes takes 20.36 s and 7.12 min, respectively

On the design of optimal dynamic experiments for parameter estimation of a Ratkowsky-type growth kinetics at suboptimal temperatures

It is generally known that accurate model building, i.e., proper model structure selection and reliable parameter estimation, constitutes an essential matter in the field of predictive microbiology, in particular, when integrating these predictive models in food safety systems. In this context, Versyck et al. (1999) have introduced the methodology of optimal experimental design techniques for parameter estimation within the field. Optimal experimental design focuses on the development of optimal input profiles such that the resulting rich (i.e., highly informative) experimental data enable unique model parameter estimation. As a case study, Versyck ct al. (1999) [Versyck, K., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., 1999. Introducing optimal experimental design in predictive modeling. a motivating example. Int. J. Food Microbiol., 51(1), 39-51] have elaborated the estimation of Bigelow inactivation kinetics parameters tin a numerical way). Opposed to the classic (static) experimental approach in predictive modelling. an optimal dynamic experimental setup is presented. In this paper, the methodology of optimal experimental design for parameter estimation is applied to obtain uncorrelated estimates of the square roof model parameters [Ratkowshy, D.A., Olley, J., McMeckin, T.A., Ball, A., 1982, Relationship between temperature and growth rate: of bacterial cultures. J. Bacteriol. 149, 1-5] describing the effect of suboptimal growth temperatures on the maximum specific growth rate of microorganisms. These estimates are the direct result of fitting a primary growth model to cell density measurements as a function of time. Apart from the design of an optimal time-varying temperature profile based on a sensitivity study of the model output, an important contribution of this publication is a first experimental, validation of this innovative dynamic experimental approach for uncorrelated parameter identification. An optimal step temperature profile, within the range of model validity and practical feasibility, is developed for Escherichia coli K12 and successfully applied in practice. The presented experimental validation result illustrates the large potential of the dynamic experimental approach in the context of uncorrelated parameter estimation. Based on the experimental validation result, additional remarks are formulated related to future research in the field of optimal experimental design. (C) 2000 Elsevier Science B.V. All rights reserved.status: publishe

Identification of non-linear microbial inactivation kinetics under dynamic conditions

In this study dynamic microbial inactivation experiments are exploited for performing parameter identification of a non-linear microbial model. For that purpose microbial inactivation data are produced and a differential equation exhibiting a shoulder and a loglinear phase is employed. The derived parameter estimates from this method were used to perform predictions on an independent experimental set at fluctuating temperature. Joint confidence regions and asymptotic confidence intervals of the estimated parameters were compared with previous studies originating from parameter identification under isothermal conditions. The developed approach can provide more reliable estimates for realistic conditions compared to the usual or standard two step approach.[**]status: publishe

Introducing optimal experimental design in predictive modeling: a motivating example

Predictive microbiology emerges more and more as a rational quantitative framework for predicting and understanding microbial evolution in food products. During the mathematical modeling of microbial growth and/or inactivation, great, but not always efficient, effort is spent on the determination of the model parameters from experimental data. In order to optimize experimental conditions with respect to parameter estimation, experimental design has been extensively studied since the 1980s in the field of bioreactor engineering. The so-called methodology of optimal experimental design established in this research area enabled the reliable estimation of model parameters from data collected in well-designed fed-batch reactor experiments. In this paper, we introduce the optimal experimental design methodology for parameter estimation in the field of predictive microbiology. This study points out that optimal design of dynamic input signals is necessary to maximize the information content contained within the resulting experimental data. It is shown that from few dynamic experiments, more pertinent information can be extracted than from the classical static experiments. By introducing optimal experimental design into the field of predictive microbiology, a new promising frame for maximization of the information content of experimental data with respect to parameter estimation is provided. As a case study, the design of an optimal temperature profile for estimation of the parameters D-ref and z of an Arrhenius-type model for the maximum inactivation rate k(max) as a function of the temperature, T, was considered. Microbial inactivation by heating is described using the model of Geeraerd et al. (1999). The need for dynamic temperature profiles in experiments aimed at the simultaneous estimation of the model parameters from measurements of the microbial population density is clearly illustrated by analytical elaboration of the mathematical expressions involved on the one hand, and by numerical simulations on the other. (C) 1999 Elsevier Science B.V. All rights reserved.status: publishe

Modelling the combined effects of structured food model system and lactic acid on Listeria innocua and Lactococcus lactis growth in mono- and coculture

A new class of predictive model developed in a liquid system is extended in order to quantify gelatin gel matrix structure effects on the growth of Listeria innocua and Lactococcus lactis (both in mono- and coculture, and both producing mainly lactic acid). It was observed that gelatin does not only act as a structuring agent but also alters the buffering capacity of the medium. Model extension occurs in two stages, describing chemical and microbiological processes, respectively. Firstly, equations relating undissociated lactic acid concentration and total lactic acid concentration on the one hand, and undissociated lactic acid concentration and pH on the other hand, are extended to account for the effects of gelatin concentration. Secondly, these equations are incorporated into the growth model to describe the combined effect of gelatin concentration, (undissociated) lactic acid and pH on the growth of either microorganism. The description of the model is in good agreement with the experimental data acquired in monoculture conditions. In a subsequent model validation step, when gelatin concentration and total lactic acid profile of the coculture experiments are used as inputs, the developed growth model consisting of condensed knowledge extracted from the monoculture experiments, is able to predict accurately the interaction effect occurring in coculture. The study suggests that, on the one hand, the extent of the effects of undissociated lactic acid and pH on microbial growth in structured food systems can be modified by the increase in buffering capacity, which can protect microorganisms and eventually promote higher levels of cell growth in comparison with liquid culture conditions. On the other hand, food matrix structure, in casu the gelatin, reduces the rate of microbial multiplication. Both effects are incorporated in the growth model developed in this research.[**]status: publishe

Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans

Proteins secreted by Gram-positive bacteria are released into the culture medium with the obvious benefit that they usually retain their native conformation. This property makes these host cells potentially interesting for the production of recombinant proteins, as one can take full profit of established protocols for the purification of active proteins. Several state-of-the-art strategies to increase the yield of the secreted proteins will be discussed, using Streptomyces lividans as an example and compared with approaches used in some other host cells. It will be shown that approaches such as increasing expression and translation levels, choice of secretion pathway and modulation of proteins thereof, avoiding stress responses by changing expression levels of specific (stress) proteins, can be helpful to boost production yield. In addition, the potential of multi-omics approaches as a tool to understand the genetic background and metabolic fluxes in the host cell and to seek for new targets for strain and protein secretion improvement is discussed. It will be shown that S. lividans, along with other Gram-positive host cells, certainly plays a role as a production host for recombinant proteins in an economically viable way. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.publisher: Elsevier articletitle: Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans journaltitle: Biochimica et Biophysica Acta (BBA) - Molecular Cell Research articlelink: http://dx.doi.org/10.1016/j.bbamcr.2013.12.023 content_type: article copyright: Copyright © 2014 Elsevier B.V. All rights reserved.status: publishe

Recombinant protein production and Streptomycetes

The biopharmaceutical market has come a long way since 1982, when the first biopharmaceutical product, recombinant human insulin, was launched. Just over 200 biopharma products have already gained approval. The global market for biopharmaceuticals which is currently valued at over US$99 billion has been growing at an impressive compound annual growth rate over the previous years. To produce these biopharmaceuticals and other industrially important heterologous proteins, different prokaryotic and eukaryotic expression systems are used. All expression systems have some advantages as well as some disadvantages that should be considered in selecting which one to use. Choosing the best one requires evaluating the options--from yield to glycosylation, to proper folding, to economics of scale-up. No host cell from which all the proteins can be universally expressed in large quantities has been found so far. Therefore, it is important to provide a variety of host-vector expression systems in order to increase the opportunities to screen for the most suitable expression conditions or host cell. In this overview, we focus on Streptomyces lividans, a Gram-positive bacterium with a proven excellence in secretion capacity, as host for heterologous protein production. We will discuss its advantages and disadvantages, and how with systems biology approaches strains can be developed to better producing cell factories.status: publishe

Influence of pH, water activity and acetic acid concentration on Listeria monocytogenes at 7 degrees C: data collection for the development of a growth/no growth model

Growth/no growth models can be used to determine the chance that microorganisms will grow in specific environmental conditions. As a consequence, these models are of interest in the assessment of the safety of foods which can be contaminated with food pathogens. In this paper, growth/no growth data for Listeria monocytogenes (in a monoculture and in a mixed strain culture) are presented. The data were gathered at 7 degrees C in Nutrient Broth with different combinations of environmental factors pH (5.0-6.0, six levels), water activity (0.960-0.990, six levels) and acetic acid concentration (0-0.8% (w/w), five levels). This combination of environmental factors for the development of a growth/no growth model was based on the characteristics of sauces and mayonnaise based salads. The strains used were chosen from screening experiments in which the pH, water activity and acetic acid resistance of 26 L. monocytogenes strains (LFMFP culture collection) was determined at 30 degrees C in Brain Heart Infusion broth. The screening showed that most L. monocytogenes strains were not able to grow at a(w)0.4% (w/w). Among these strains, the ones chosen were the most resistant to one of these factors in the hope that, if the resulting model predicted no growth at certain conditions for those more resistant strains, then these predictions would also be valid for the less resistant strains. A mixed strain culture was also examined to combine the strains that were most resistant to one of the factors. A full factorial design with the selected strains was tested. The experiments were performed in microtiter plates and the growth was followed by optical density measurements at 380 nm. The plates were inoculated with 6 log CFU/ml and twenty replicates were made for each treatment combination. These data were used (1) to determine the growth/no growth boundary and (2) to estimate the influence of the environmental conditions on the time to detection. From the monoculture and mixed strain data, the growth boundary of L. monocytogenes is shown not to be a straight cut-off but a rather narrow transition zone. The experiments also showed that in the studied region, a(w) did not have a pronounced influence on the position of the growth/no growth boundary while a low concentration of acetic acid (0.2% (w/w)) and a pH decrease from 6.0 to 5.8 was sufficient to significantly reduce the possibility of growth. The determination of the time to detection showed a significant increase at the combinations of environmental conditions near the 'no growth zone'. For example, at 0.2% (w/w) acetic acid, there was an increase from +/-10 days to 30 days by lowering pH from 5.8 to 5.6 at a(w) values of 0.985 and 0.979, while at pH 5.4 less than 50% growth occurred for all a(w) values.[*]status: publishe

Validation of a model for growth of Lactococcus lactis and Listeria innocua in a structured gel system: effect of monopotassium phosphate

The effect of monopotassium phosphate (KH2PO4) on the chemical environment and on growth of Listeria innocua and Lactococcus lactis in coculture were investigated in a liquid and in a gelled Microbiological medium at 12 degrees C and an initial pH of 6.2. As expected, addition of KH2PO4 to both the liquid and gelled media resulted in an increase in buffering capacity. This effect on buffering capacity changed the profiles of lactic acid dissociation and pH evolution. At all gelatin concentrations Studied, addition of KH2PO4 increased the growth rate and the stationary cell concentration of L. lactis. In addition, the growth rate of L. innocua slightly increased but, in contrast, the stationary cell concentration remained unchanged. A new class of predictive models developed previously in our research team to quantify the effect of food model gel structure on microbial growth [Antwi, M.. Bernaerts. K., Van Impe, J. F., Geeraerd, A. H., 2007. Modelling the combined effect of food model system and lactic acid on L. innocua and L lactis growth in mono- and coculture. international journal of Food Microbiology 120, 71-84] was applied. Our analysis indicate that KH2PO4 influenced the parameters of the chemical and microbiological subprocesses of the model. Nonetheless, the growth model satisfactorily predicted the stationary cell concentration when (i) the Undissociated lactic acid concentrations at which L. innocua and L. lactis growth cease were chosen as previously reported, and (ii) all other parameters of the chemical and microbiological subprocesses were computed for each medium. This confirms that the undissociated lactic acid concentrations at which growth ceases is a unique property of a bacterium and does not, within our case study, depend on growth medium. The study indicates that microbial growth depends on the interplay between the individual food components which affect the physicochemical properties of the food, such as the buffering capacity. Towards future research, it can be concluded that mathematical models which embody the effect of buffering capacity are needed for accurate predictions of microbial growth in food systems. (c) 2008 Elsevier B.V. All rights reserved.[**]status: publishe