J Olley

University of Tasmania, Hobart Town, Tasmania, Australia

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Publications (20)49.37 Total impact

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    ABSTRACT: Life on Earth is capable of growing from temperatures well below freezing to above the boiling point of water, with some organisms preferring cooler and others hotter conditions. The growth rate of each organism ultimately depends on its intracellular chemical reactions. Here we show that a thermodynamic model based on a single, rate-limiting, enzyme-catalysed reaction accurately describes population growth rates in 230 diverse strains of unicellular and multicellular organisms. Collectively these represent all three domains of life, ranging from psychrophilic to hyperthermophilic, and including the highest temperature so far observed for growth (122°C). The results provide credible estimates of thermodynamic properties of proteins and obtain, purely from organism intrinsic growth rate data, relationships between parameters previously identified experimentally, thus bridging a gap between biochemistry and whole organism biology. We find that growth rates of both unicellular and multicellular life forms can be described by the same temperature dependence model. The model results provide strong support for a single highly-conserved reaction present in the last universal common ancestor (LUCA). This is remarkable in that it means that the growth rate dependence on temperature of unicellular and multicellular life forms that evolved over geological time spans can be explained by the same model.
    PLoS ONE 01/2014; 9(5):e96100. · 3.53 Impact Factor
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    ABSTRACT: We review early work on the microbial growth curve and the concept of balanced growth followed by commentary on the stringent response and persister cells. There is a voluminous literature on the effect of antibiotics on resistance and persistence and we call for a greater focus in food microbiology on the effect of biocides in the same context. We also raise potential issues in development of resistance arising from “source–sink” dynamics and from horizontal gene transfer. Redox potential is identified as crucial in determining microbial survival or death, and the recently postulated role for reactive oxygen species in signalling also considered.“Traditional” predictive microbiology is revisited with emphasis on temperature dependence. We interpret the temperature vs growth rate curve as comprising 11 regions, some well-recognised but others leading to new insights into physiological responses. In particular we are intrigued by a major disruption in the monotonic rate of inactivation at a temperature, slightly below the actual maximum temperature for growth. This non-intuitive behaviour was earlier reported by other research groups and here we propose that it results from a rapid metabolic switch from the relaxed growth state to the stringent survival state.Finally, we envision the future of predictive microbiology in which models morph from empirical to mechanistic underpinned by microbial physiology and bioinformatics to grow into Systems Biology.
    Food Control. 02/2013; 29(2):290–299.
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    ABSTRACT: Mathematical models exist that quantify the effect of temperature on poikilotherm growth rate. One family of such models assumes a single rate-limiting 'master reaction' using terms describing the temperature-dependent denaturation of the reaction's enzyme. We consider whether such a model can describe growth in each domain of life. A new model based on this assumption and using a hierarchical Bayesian approach fits simultaneously 95 data sets for temperature-related growth rates of diverse microorganisms from all three domains of life, Bacteria, Archaea and Eukarya. Remarkably, the model produces credible estimates of fundamental thermodynamic parameters describing protein thermal stability predicted over 20 years ago. The analysis lends support to the concept of universal thermodynamic limits to microbial growth rate dictated by protein thermal stability that in turn govern biological rates. This suggests that the thermal stability of proteins is a unifying property in the evolution and adaptation of life on earth. The fundamental nature of this conclusion has importance for many fields of study including microbiology, protein chemistry, thermal biology, and ecological theory including, for example, the influence of the vast microbial biomass and activity in the biosphere that is poorly described in current climate models.
    PLoS ONE 01/2012; 7(2):e32003. · 3.53 Impact Factor
  • International Journal of Food Microbiology. 05/2011; 147(1):83–84.
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    ABSTRACT: Inferential techniques of numerical classification and principal coordinate analysis have been used to interpret data obtained on the Zn, Cd, and Cu concentration of 48 samples of oysters, comprising 473 individuals, grown at a variety of places around the Tasmanian coastline. A close association was obtained between proximity to heavily urbanized areas and concentration of metals found, oysters growing nearest urban areas having the highest concentrations of one or more of the metals. It appears that areas for commercial oyster growing should be sought in regions far from centers of urbanization and industrialization. Examination of samples of native oysters could be useful in providing an index or measure of environmental pollution.
    Journal of the Fisheries Research Board of Canada. 04/2011; 31(7):1165-1171.
  • International journal of food microbiology 02/2011; 147(1):78-80; author reply 81-2; discussion 83-4. · 3.01 Impact Factor
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    ABSTRACT: D.A. RATKOWSKY, T. ROSS, T.A. WCMEEKIN AND J. OLLEY. 1991. The development of Arrhenius-type (‘Schoolfield’) and Bêlehrádek-type (square root) models that describe microbial growth rates is briefly described. Both types of model have been advocated for use in predictive microbiology. On the basis of published data sets for the growth of bacteria, the consequences of mathematical transformation of data and the use of invalid stochastic assumptions upon model predictions are demonstrated. Mean square error is shown to be an inappropriate criterion by which to compare the performance of predictive models. The data show that bacterial growth responses such as generation time and lag time become more variable as their mean magnitude increases. The practical consequences of such variability for predictive microbiology are discussed.
    Journal of Applied Microbiology 03/2008; 71(5):452 - 459. · 2.20 Impact Factor
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    ABSTRACT: The specific growth rate constant for bacterial growth does not obey the Arrhenius-type kinetics displayed by simple chemical reactions. Instead, for bacteria, steep convex curves are observed on an Arrhenius plot at the low- and high-temperature ends of the biokinetic range, with a region towards the middle of the growth range loosely approximating linearity. This central region has been considered by microbiologists to be the "normal physiological range" for bacterial growth, a concept whose meaningfulness we now question. We employ a kinetic model incorporating thermodynamic terms for temperature-induced enzyme denaturation, central to which is a term to account for the large positive heat capacity change during unfolding of the proteins within the bacteria. It is now widely believed by biophysicists that denaturation of complex proteins and/or other macromolecules is due to hydrophobic hydration of non-polar compounds. Denaturation is seen as the process by which enthalpic and entropic forces becomes imbalanced both at high and at low temperatures resulting in conformational changes in the enzyme structure that expose hydrophobic amino acid groups to the surrounding water molecules. The "thermodynamic" rate model, incorporating the heat capacity change and its effect on the enthalpy and entropy of the system, fitted 35 sets of data for psychrophilic, psychrotrophic, mesophilic and thermophilic bacteria well, resulting in biologically meaningful estimates for the important thermodynamic parameters. As these results mirror those obtained by biophysicists for globular proteins, it appears that the same or a similar mechanism applies to bacteria as applies to proteins.
    Journal of Theoretical Biology 05/2005; 233(3):351-62. · 2.35 Impact Factor
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    ABSTRACT: The fatty acid composition of Listeria monocytogenes Scott A was determined by close-interval sampling over the entire biokinetic temperature range. There was a high degree of variation in the percentage of branched-chain fatty acids at any given temperature. The percentage of branched C17 components increased with growth temperature in a linear manner. However, the percentages of iso-C15:0 (i15:0) and anteiso-C15:0 (a15:0) were well described by third-order and second-order polynomial curves, respectively. There were specific temperature regions where the proportion of branched-chain fatty acids deviated significantly from the trend established over the entire growth range. In the region from 12 to 13 degrees C there were significant deviations in the percentages of both i15:0 and a15:0 together with a suggested deviation in a17:0, resulting in a significant change in the total branched-chain fatty acids. In the 31 to 33 degrees C region the percentage of total branched-chain components exhibited a significant deviation. The observed perturbations in fatty acid composition occurred near the estimated boundaries of the normal physiological range for growth.
    Applied and Environmental Microbiology 07/2002; 68(6):2809-13. · 3.95 Impact Factor
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    ABSTRACT: This review considers the concept and history of predictive microbiology and explores aspects of the modelling process including kinetic and probability modelling approaches. The "journey" traces the route from reproducible responses observed under close to optimal conditions for growth, through recognition and description of the increased variability in responses as conditions become progressively less favourable for growth, to defining combinations of factors at which growth ceases (the growth/no growth interface). Death kinetics patterns are presented which form a basis on which to begin the development of nonthermal death models. This will require incorporation of phenotypic, adaptive responses and may be influenced by factors such as the sequence in which environmental constraints are applied. A recurrent theme is that probability (stochastic) approaches are required to complement or replace kinetic models as the growth/no growth interface is approached and microorganisms adopt a survival rather than growth mode. Attention is also drawn to the interfaces of predictive microbiology with microbial physiology, information technology and food safety initiatives such as HACCP and risk assessment.
    International Journal of Food Microbiology 04/2002; 73(2-3):395-407. · 3.43 Impact Factor
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    ABSTRACT: The shelf life of Atlantic salmon (Salmo salar) portions produced for retail distribution is examined and the dominant aerobic spoilage organism is identified. Characterization of the harvesting and processing operations allow the development of a stochastic mathematical model, a process risk model (PRM), which predicts the range of the possible shelf life for the portions under normal retail and distribution. The considered risk is the failure to achieve the nominal 'use by' date. Bacterial counts from surface swabs, water, ice, and fish samples, collected over a period of 9 months, are fitted to distribution functions for use within the model. Comparisons are made between the distributions fitted to the observed bacterial levels and the predicted levels for the slurry water, initial surface contamination on the fish, and for the predicted and observed shelf life. Storage temperature of the packaged salmon portions has the greatest influence on shelf life, with contamination from contact surfaces and other sources being the next most important. The range of bacterial counts on the portions was between -0.6 and 5 log10 cfu/cm2. The model predicts bacterial counts in the slurry water to have an average value of 3.36 log10 cfu/ml, whereas the observed slurry water bacterial counts were 3.35 log10 cfu/ml. The predicted average initial bacterial contamination is 3.31 log10 cfu/cm2 on the fish surface and 3.23 log10 cfu/cm2 on the observed. The average predicted shelf life is 6.5 days, compared to an observed value of 6.2 days at 4 degrees C.
    International Journal of Food Microbiology 03/2002; 73(1):47-60. · 3.43 Impact Factor
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    ABSTRACT: The maximum growth temperature, the optimal growth temperature, and the estimated normal physiological range for growth of Shewanella gelidimarina are functions of water activity (aw), which can be manipulated by changing the concentration of sodium chloride. The growth temperatures at the boundaries of the normal physiological range for growth were characterized by increased variability in fatty acid composition. Under hyper- and hypoosmotic stress conditions at an aw of 0.993 (1.0% [wt/vol] NaCl) and at an aw of 0.977 (4.0% [wt/vol] NaCl) the proportion of certain fatty acids (monounsaturated and branched-chain fatty acids) was highly regulated and was inversely related to the growth rate over the entire temperature range. The physical states of lipids extracted from samples grown at stressful aw values at the boundaries of the normal physiological range exhibited no abrupt gel-liquid phase transitions when the lipids were analyzed as liposomes. Lipid packing and adaptational fatty acid composition responses are clearly influenced by differences in the temperature-salinity regime, which are reflected in overall cell function characteristics, such as the growth rate and the normal physiological range for growth.
    Applied and Environmental Microbiology 07/2000; · 3.95 Impact Factor
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    ABSTRACT: The growth rates of four strains of Vibrio parahaemolyticus were measured and compared in a model broth system. The results for the fastest growing strain, based on 77 combinations of temperature and water activity (aw) using NaCl as the humectant, were summarised in the form of a predictive mathematical model. The model, of the square-root type includes a novel term to describe the effects of super-optimal water activity, and can be used to predict generation times for the temperature range (8-45 degrees C) and water activity range (0.936-0.995) which permit growth of Vibrio parahaemolyticus. Predicted generation times from the model were compared to literature data, using bias and accuracy factors, for both laboratory media and foods. The model was shown to give realistic growth estimates, with a bias value of 1.01, and an accuracy factor of 1.38.
    International Journal of Food Microbiology 09/1997; 38(2-3):133-42. · 3.43 Impact Factor
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    ABSTRACT: Because microorganisms are easily dispersed, display physiologic diversity, and tolerate extreme conditions, they are ubiquitous and may contaminate and grow in many food products. The behavior of microbial populations in foods (growth, survival, or death) is determined by the properties of the food (e.g., water activity and pH) and the storage conditions (e.g., temperature, relative humidity, and atmosphere). The effect of these properties can be predicted by mathematical models derived from quantitative studies on microbial populations. Temperature abuse is a major factor contributing to foodborne disease; monitoring temperature history during food processing, distribution, and storage is a simple, effective means to reduce the incidence of food poisoning. Interpretation of temperature profiles by computer programs based on predictive models allows informed decisions on the shelf life and safety of foods. In- or on-package temperature indicators require further development to accurately predict microbial behavior. We suggest a basis for a "universal" temperature indicator. This article emphasizes the need to combine kinetic and probability approaches to modeling and suggests a method to define the bacterial growth/no growth interface. Advances in controlling foodborne pathogens depend on understanding the pathogens' physiologic responses to growth constraints, including constraints conferring increased survival capacity.
    Emerging infectious diseases 01/1997; 3(4):541-9. · 5.99 Impact Factor
  • Food Microbiology. 12/1989; 6(4):304–308.
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    ABSTRACT: The combined effect of temperature and NaCl concentration/water activity on the growth rate of a strain of halotolerant Staphylococcus is described by the square-root models which had been used previously to model temperature dependence only. The model square root r = b(T-T min) is shown to be a special case of the Bĕlehrádek temperature function which is given by r = a(T-alpha)d. The constant alpha is the socalled 'biological zero' and equivalent to T min in the square-root models. This and the exponent d = 2 were unaffected by changing NaCl concentration/water activity. The Bĕlehrádek-type equations are preferable to the Arrhenius equation in that their parameters do not change with temperature. The constancy of T min allows derivation of a simple expression relating growth rate of strain CM21/3 to temperature and salt concentration/water activity within the range of linear response to temperature predicted by the square-root model.
    The Journal of applied bacteriology 07/1987; 62(6):543-50.
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    ABSTRACT: The Arrhenius Law, which was originally proposed to describe the temperature dependence of the specific reaction rate constant in chemical reactions, does not adequately describe the effect of temperature on bacterial growth. Microbiologists have attempted to apply a modified version of this law to bacterial growth by replacing the reaction rate constant by the growth rate constant, but the modified law relationship fits data poorly, as graphs of the logarithm of the growth rate constant against reciprocal absolute temperature result in curves rather than straight lines. Instead, a linear relationship between in square root of growth rate constant (r) and temperature (T), namely, square root = b (T - T0), where b is the regression coefficient and T0 is a hypothetical temperature which is an intrinsic property of the organism, is proposed and found to apply to the growth of a wide range of bacteria. The relationship is also applicable to nucleotide breakdown and to the growth of yeast and molds.
    Journal of Bacteriology 02/1982; 149(1):1-5. · 3.19 Impact Factor
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    ABSTRACT: The rate of spoilage of chicken tissues, the development of spoilage bacteria, and the utilization of amino acids by spoilage bacteria as a function of temperature were more accurately described by the general spoilage curve of Olley and Ratkowsky (Food Technol. Aust. 25:66-73, 1973; Food Technol. N.Z. 8:13-17, 1973) than by the linear equation of Spencer and Baines (Food Technol. [Chicago] 18:175-179, 1964). Remaining shelf life of poultry tissues may be predicted at temperatures up to 16 degrees C by using a temperature function integrator which incorporates the general spoilage curve.
    Applied and Environmental Microbiology 12/1978; 36(5):650-4. · 3.95 Impact Factor
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    ABSTRACT: Summary
    XF2006203139. 01/1978;
  • T A McMeekin, T Ross, J Olley
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    ABSTRACT: Predictive microbiology offers an alternative to traditional microbiological assessment of food quality and safety. The concept is that a detailed knowledge of the microbial ecology of a food product can be expressed as a mathematical model to enable objective evaluation of the effect of processing, storage and distribution operations on microbial development. Experience to date indicates the need initially to derive a mathematical model in laboratory studies, to validate the model in food products and to incorporate the information into monitoring devices. These may be chemical or physical indicators or electronic integrators or loggers. To enable a correct decision on quality or safety, it is essential that the response of the monitoring device to environmental changes mimics exactly that of the organism of concern. Most monitoring devices currently available record temperature history, but not other environmental factors that influence growth and that, in some circumstances, change during storage. The next generation of monitoring devices may be required to monitor several parameters to take full advantage of increasingly accurate and sophisticated models.
    International Journal of Food Microbiology 15(1-2):13-32. · 3.43 Impact Factor