Microbial growth on a solid substrate in which the substrate itself acts as the carbon source is defined as solid-substrate fermentation, while SSF is the fermentation process occurring in the absence or near-absence of free water among the particles using a natural substrate [1,47]. From:
Current Developments in Biotechnology and Bioengineering, 2018
Helen Louise Brown, in Encyclopedia of
Infection and Immunity, 2022 Microbial growth counts provide a highly accurate estimate of the microbial population, which is not confounded by the presence of debris or dead cells. One of the most accurate ways of determining microbial numbers in clonal population is to count a subset of the microbial population and scale that number up to give a
concentration for the whole population. Typically, this is done by producing a series of dilutions (serial dilutions are usually logarithmic, i.e., 10-fold). The dilutions are then added to agar and incubated in optimum growth conditions to allow microbial growth and colony formation (Fig. 2).Microbial Growth
Cell counting
Fig. 2. Overview of a basic method of counting the number of colony forming units within a population.
It is assumed that each single microbe within a dilution will grow by binary fission to produce a clonal colony. Colony counting methods express the concentrations as the number of colony forming units in each mL of the initial microbial culture or “CFU/mL.” The term “colony forming unit” is used in preference to a cell/mL value since although the assumption is that each colony on the plate is derived from a single cell, this is not directly known. To extrapolate the total number of cells within the starting inoculum the following formula is used:
CFU/mL= Number of colonies×dilution factorVolume spreadonagar plate
Many videos are available online demonstrating colony counting methods and how to calculate CFU/mL.
Although colony counting methods are commonly used and considered to be one of the best methods of estimating inoculum concentrations, they do have some negative aspects which should be fully considered before use. It is important to have some understanding of the growth dynamics and phenotype of the species to be worked with before counting. For example, if the species to be worked with produces aggregates which cannot be separated then it is very likely that any CFU/mL values will underestimate the actual number of viable cells. Some bacterial species which have a mucoid or swarming phenotype, for example Pseudomonas aeruginosa and Proteus mirabilis respectively, will also be difficult to count accurately since they do not produce well defined and separated colonies when grown on some agars. Similarly fungal species which form complex multicellular bodies or hyphae are difficult to count, although this technique can work well with species, such as yeast, which have a unicellular, non-hyphenated growth stage. Finally, methods for determining CFU/mL are time consuming, requiring a period of incubation to allow visible microbial growth and require a large number of agar plates. This can be mitigated slightly using direct methods such as counting cells within a hemocytometer, but again this is not suitable for all species—in particular those which are very small or motile.
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Foods, Materials, Technologies and Risks
J.N. Sofos, in Encyclopedia of Food Safety, 2014
Inhibition of Microbial Growth
Microbial growth on meat products, as well as other foods, is affected, not only by the type and level of initial contamination but also by various factors associated with the product (intrinsic) or its environment (extrinsic). Approaches aiming to inhibit microbial growth are mostly based on manipulation or changes in these factors. These include storage at low temperatures, drying (evaporation) or reduction by binding of water levels (salting and sugaring) available for microbial growth (water activity), addition of acids (low pH), fermentation (low pH and production of antimicrobials), packaging under modified atmospheres such as vacuum, and use of chemical antimicrobials. Combinations of antimicrobial technologies, applied individually at sublethal levels (hurdle technology), are frequently used in many meat products as they result in microbiologically stable and safe products of desirable eating quality. It is important to properly select and apply sublethal hurdle combinations with the objective of pathogen control without stress adaptation or selection of resistant pathogens.
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Growth Kinetics
Ghasem D. Najafpour, ... Ghasem Najafpour, in Biochemical Engineering and Biotechnology, 2007
5.1 INTRODUCTION
Microbial growth is considered for the observation of the living cell activities. It is important to monitor cell growth and biological and biocatalytic activities in cell metabolism. A variety of methods are available to predict cell growth by direct or indirect measurements. Cell dry weight, cell optical density, cell turbidity, cell respiration, metabolic rate and metabolites are quite suitable for analysing cell growth, substrate utilisation and product formation. The rate of cell growth is described in this chapter. Various bioprocesses are modelled for substrate utilisation and product formation. Growth kinetics in batch and continuous culture is examined in detail.
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ECOLOGY OF BACTERIA AND FUNGI IN FOODS | Influence of Temperature
T. Ross, D.S. Nichols, in Encyclopedia of Food Microbiology (Second Edition), 2014
Effect of Temperature on Reaction Rate
Microbial growth can be considered as a complex sequence of chemical reactions. The chemical reactions that occur within bacterial or fungal cells are geared toward either
•Provision of energy and reducing power from the environment to the cell (catabolism), or
•Synthesis of structural and other macromolecules required for growth (anabolism).
The rates of those reactions, and hence of microbial growth, are dependent on temperature as may be described by Eyring's absolute reaction rate equation:
[1]V=kTh×[r]×e(−ΔG†RT)
where
V = rate of reaction,
k = Boltzmann's constant,
T = temperature,
H = Plank's constant,
[r] = concentration of reactant,
ΔG† = Gibbs free energy of activation, or ‘activation energy,’
R = gas constant.
and which is based on the Arrhenius–van't Hoff equation.
Most metabolic reactions within cells, however, do not occur at measurable rates without the catalytic assistance of enzymes. Enzymes are proteins and are fundamental to all metabolic functions. They mediate the transformation of different forms of chemical energy.
A biochemical reaction proceeds from reactants to products via one or more ‘transition’ states that possess a higher free energy than that of the reactants (see Figure 5). Enough energy must be supplied to the system to overcome this barrier and allow the formation of the transition state. Thermodynamically, this is quantified by the free energy of activation, ΔG†. The Gibbs free energy function is derived from a combination of the first and second laws of thermodynamics:
Figure 5. An illustration of a metabolic reaction in terms of Gibbs free energy (G†). The change in free energy of the system (ΔG) determines whether the reaction is possible. The magnitude of the free energy of activation (ΔG†) influences the likelihood of the reaction and the rate. ΔG and ΔG† are both functions of temperature.
[2]ΔG=ΔH−TΔS
where
ΔG = change in free energy of the system,
ΔH = change in enthalpy of the system,
ΔS = change in entropy of the system,
T = temperature (K).
For a chemical reaction to occur spontaneously, the change in free energy, ΔG (i.e., free energy of the products minus the free energy of the reactants), must be negative. This requirement is independent of the path of the reaction (Figure 5).
Although ΔG indicates whether a given reaction is possible, ΔG† describes the amount of energy needed to ‘drive’ the reaction. The kinetic energy of the reactants determine whether they have sufficient energy to overcome the Gibbs free energy, which often is termed the ‘activation energy.’ The kinetic energy is related to the temperature of the system, but not all the reactant molecules have the same kinetic energy at a given temperature. Rather, the energies of the reactant molecules form a distribution of kinetic energies, the average of which increases with temperature. Higher temperature increases the probability that the reactants will have sufficient energy to overcome ΔG† so that the reaction can proceed to completion. Thus, the probability of reaction, and therefore the rate, is also dependent on temperature.
Enzymes accelerate biochemical reactions by decreasing ΔG†. Decreasing ΔG† effectively increases the number of substrate molecules with sufficient energy to complete the reaction. Consequently, the reaction is perceived to occur at an increased rate.
From eqn [1], the logarithm of rate is expected to be linearly related to the reciprocal of temperature, with the slope of that line being equal to the activation energy of the response. A plot of ln(rate) vs. 1/temperature is known as an Arrhenius plot. Figure 6 is an Arrhenius plot of the growth rate of Escherichia coli and is typical of Arrhenius plots of microbial growth rate. That plot, however, shows a deviation from the Arrhenius relationship at high and low temperatures. This deviation often has been attributed to the denaturation of one or several key macromolecules required by the organism for growth, as described in Box 1. An alternate hypothesis is that the coordination of catabolic and anabolic reactions within the cells breaks down at high and low temperatures, leading to a reduction in the efficiency of metabolism, and eventually to the complete breakdown of balanced growth.
Figure 6. An Arrhenius plot, based on a predictive model fitted to E. coli growth rate data, and typical of microbial growth rate responses to temperature. The dotted line is the predicted growth rate based on data in the ‘normal temperature region for growth.’ If growth rate data are collected over the full biokinetic region, however, deviations from this prediction are observed at high and low temperatures. The ‘normal temperature region’ depicted was judged subjectively, based on the deviation of the observed data from that predicted by extrapolation of eqn [1], but it does correspond to temperatures beyond which measurable changes in the composition of E. coli occur.
Box 1
A hypothetical physiological basis for the effect of temperature on microbial growth rate
Effect of Temperature on Enzyme Structure and Efficiency
The rate of enzyme-catalyzed reactions is also dependent on the concentration of active enzyme – itself a function of temperature. Enzymes are proteins. The functional activity of enzymes is dependent upon their shape, or conformation, but they are flexible – the flexibility being required to achieve their catalytic function. Temperature affects the bonds in the molecule and, if the temperature changes too much, the conformation becomes so distorted that the enzyme is no longer catalytically active. This process is called ‘denaturation.’ Denaturation can be visualized as unfolding of the protein and can occur both when temperature becomes too high and also when it becomes too low. That denaturation is reversible and the protein can refold spontaneously if the temperature returns to within the range for stability. If the temperature becomes too high, however, irreversible denaturation takes place.
A number of theoretical models have been advanced since the 1930s to explain the effect of temperature on bacterial growth rate. Most have as their basis the idea of a rate limiting, enzyme-catalyzed, ‘master reaction’ for growth. The concept of the models for the temperature dependence of poikilothermic growth mentioned earlier is that there is a single enzyme-catalyzed reaction that limits microbial growth rate under all conditions. This putative reaction and the enzyme that catalyzes it have been termed the ‘master reaction’ and the ‘master enzyme,’ respectively. The activation energy of the master reaction is considered to be the ultimate limit to growth rate at all temperatures.
The hypothesis continues that the master enzyme is subject to the effects of temperature, so that as temperature increases above the optimum for conformational stability or decreases below it, the enzyme progressively becomes denatured. The transition of the master enzyme between conformationally active and inactive states is a function of temperature. The effect of this is a reduced level of sites available for catalysis, perceived as a reduction in the rate of reaction as seen at high and low temperature beyond the normal physiological range.
Whatever the reason, the Arrhenius plot of microbial growth rate can be considered in terms of three regions related to temperature. The ‘normal’ physiological range is that region where the growth rate responds to temperature as predicted by eqn [1], that is, the central ‘straight-line’ portion. At any temperature within the normal physiological range, the chemical composition of the cell is essentially constant. Beyond this range are the high- and low-temperature regions. Cells grown in the high- and low-temperature regions not only have growth rates that deviate from that predicted by eqn [1] but increasingly are different in composition to those grown in the ‘normal’ physiological range. Transitions to the high- and low-temperature regions have been shown to result in synthesis of proteins not expressed in the normal temperature region. As will be discussed in detail, membrane lipid composition also is altered by the synthesis and incorporation into the membrane of lipids that have the effect of maintaining membrane fluidity.
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Microbiological challenges to the pharmaceuticals and healthcare
Tim Sandle, in Pharmaceutical Microbiology, 2016
22.6.1 Product stability
Microbial growth will lead to reductions in the pH of the formulation. With nonsterile products, this might affect the color of dyes and could cause acid cracking of some emulsions.
With specific product types, excipients such as surfactants can often provide sources of carbon and nitrogen for bacterial growth. If such materials are degraded then they can no longer stabilize the product. Phase separation of oil and water will occur. The texture of creams will be adversely affected by the growth of microorganisms, particularly fungi. The production of gases such as hydrogen sulfide and methane from fermentative metabolism can affect the smell of the product and also cause the creation of gas pockets.
Organic acids produced through fermentative growth of microorganisms can affect both the smell and the taste of products. Many bacteria produce brightly colored metabolites that can drastically alter the physical appearance of the product. Such changes are of particular concern when the product is a cosmetic.
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ECOLOGY OF BACTERIA AND FUNGI IN FOODS | Influence of Temperature
T. Ross, D.S. Nichols, in Encyclopedia of Food Microbiology, 1999
Interpretation of the Effect of Temperature on Microbial Growth
Effect of Temperature on Reaction Rate
Microbial growth can be considered as a complex sequence of chemical reactions. The chemical reactions that occur within bacterial or fungal cells are geared towards either:
•the provision of energy and reducing power from the environment to the cell (catabolism) or
•the synthesis of structural and other macromolecules required for growth (anabolism)
The rates of those reactions, and hence of microbial growth, are dependent on temperature, as may be described by Eyring's absolute reaction rate equation:
Equation 1V=kTh×r×e−ΔG†/RT
where: V = rate of reaction, k = Boltzmann's constant, T = temperature, h = Plank's constant, [r] =; concentration of reactant, ΔG† = Gibbs free energy of activation, or activation energy and R = gas constant. This relationship is based on the Arrhenius-van't Hoff equation.
Most metabolic reactions within cells do not occur at measurable rates without the catalytic assistance of enzymes. Enzymes are proteins and are fundamental to all metabolic functions. They mediate the transformation of different forms of chemical energy.
A biochemical reaction proceeds from reactants to products via one or more transition states that possess a higher free energy than that of the reactants (Fig. 5). Enough energy must be supplied to the system to overcome this barrier and allow the formation of the transition state. Thermodynamically, this is quantified by the free energy of activation, ΔG†. The Gibbs free energy function is derived from a combination of the first and second laws of thermodynamics:
Figure 5. Metabolic reaction in terms of Gibbs free energy (G). The change in free energy of the system (ΔG) determines whether the reaction is possible. The magnitude of the free energy of activation (ΔG†) influences the likelihood of the reaction and the rate. ΔG and ΔG† are both functions of temperature.
Equation 2ΔG=ΔH−TΔS
where: ΔG = change in free energy of the system, ΔH = change in enthalpy of the system, ΔS = change in entropy of the system and T = temperature (K).
For a chemical reaction to occur spontaneously, the change in free energy, ΔG (free energy of the products minus the free energy of the reactants), must be negative. This requirement is independent of the path of the reaction (Fig. 5).
While ΔG indicates whether a given reaction is possible, ΔG† describes the amount of energy needed to drive the reaction. The kinetic energy of the reactants determines whether they have sufficient energy to overcome the Gibbs free energy, which is also often termed the activation energy. The kinetic energy is related to the temperature of the system, but not all the reactant molecules have the same kinetic energy at a given temperature. Rather, the energies of the reactant molecules form a distribution of kinetic energies, the average of which increases with temperature. A higher temperature increases the probability that the reactants will have sufficient energy to overcome ΔG† so that the reaction can proceed to completion. Thus, the probability of reaction, and therefore the rate, is also dependent on temperature.
Enzymes accelerate biochemical reactions by decreasing ΔG†. Decreasing ΔG† effectively increases the number of substrate molecules with sufficient energy to complete the reaction. Consequently, the reaction is perceived to occur at an increased rate.
From Equation 1, the logarithm of rate is expected to be linearly related to the reciprocal of temperature, with the slope of that line being equal to the activation energy of the response. A plot of 1n(rate) vs. 1/temperature is known as an Arrhenius plot. Figure 6 is an Arrhenius plot of the growth rate of Escherichia coli, and is typical of Arrhenius plots of microbial growth rate. That plot, however, shows a deviation from the Arrhenius relationship at high and low temperatures. This deviation has often been attributed to the denaturation of one or several key macromolecules required by the organism for growth, as shown in Table 2. An alternative hypothesis is that the co-ordination of catabolic and anabolic reactions within the cell breaks down at high and low temperatures, leading to a reduction in the efficiency of metabolism, and eventually to complete breakdown of balanced growth.
Figure 6. Arrhenius plot, based on a predictive model fitted to Escherichia coli growth rate data, and typical of microbial growth rate data. The dashed line is the predicted growth rate based on data in the normal temperature region for growth. If growth rate data are collected over the full biokinetic region, however, deviations from this prediction are observed at high and low temperatures. The normal temperature region depicted was judged subjectively, based on the deviation of the observed data from that predicted by extrapolation of equation 1, but does correspond to temperatures beyond which measurable changes in the biochemical composition of E. coli occur.
Table 2. Hypothetical physiological basis for the effects of temperature on microbial growth rate
| Effect of temperature on enzyme-structure and efficiency |
| The rate of enzyme-catalysed reactions is also dependent on the concentration of active enzyme, itself a function of temperature. Enzymes are proteins. The functional activity of enzymes is dependent upon their shape or conformation, but they are flexible – the flexibility is required to achieve their catalytic function. Temperature affects the bonds in the molecule and, if the temperature changes too much, the conformation becomes so distorted that the enzyme is no longer catalytically active. This process is called denaturation. Denaturation can be visualized as unfolding of the protein, and can occur both when temperature becomes too high and also when it becomes too low. That denaturation is reversible and the protein can refold spontaneously if the temperature returns to within the range for stability. If the temperature becomes too high, however, irreversible denaturation takes place |
| Hypothetical physiological basis of temperature on microbial metabolism |
| A number of theoretical models have been advanced since the 1930s to explain the effect of temperature on bacterial growth rate. Most have as their basis the idea of a rate-limiting, enzyme-catalysed master reaction for growth. The concept of the models for the temperature dependence of poikilothermic growth mentioned earlier is that there is a single enzyme-catalysed reaction that limits microbial growth rate under all conditions. This putative reaction and the enzyme that catalyses it have been termed the master reaction and the master enzyme respectively. The activation energy of the master reaction is considered to be the ultimate limit to growth rate at all temperatures. |
| The hypothesis continues that the master enzyme itself is subject to the effects of temperature, so that as temperature increases above the optimum for conformational stability or decreases below it, the enzyme progressively becomes denatured. The transition of the master enzyme between conformationally active and inactive states is a function of temperature. The effect of this is a reduced level of sites available for catalysis, perceived as a reduction in the rate of reaction as seen at high and low temperatures beyond the normal physiological range |
Whatever the reason, the Arrhenius plot of microbial growth rate can be considered in terms of three regions related to temperature. The normal physiological range is that region where the growth rate responds to temperature as predicted by Equation 1, i.e. the central straight-line portion. At any temperature within the normal physiological range the chemical composition of the cell is essentially constant. Beyond this range are the high- and low-temperature regions. Cells grown in the high- and low-temperature region not only have growth rates that deviate from that predicted by Equation 1 but also are increasingly different in composition to those grown in the normal physiological range. Transitions to the high- and low-temperature regions have been shown to result in synthesis of proteins not expressed in the normal temperature region. As discussed in greater detail below, membrane lipid composition is also altered by the synthesis and incorporation of different fatty acids into the membrane which have the effect of maintaining membrane fluidity.
Unification of the Microbial Response to Temperature
The above observations and discussion offer a consistent interpretation of the effects of temperature on microbial growth rates and limits. The temperature limits for growth are governed by the high- and low-temperature stability of one or several key macromolecules without which growth cannot proceed.
Growth rate increases with increased temperature in accordance with Equation 1 until the increase in temperature disrupts the conformation of enzymes, and/or the integration of anabolic and catabolic rates. Thus, metabolic efficiency decreases, leading to the observed high- and low-temperature deviations. In this interpretation, the optimum growth temperature is viewed as the point of equilibrium between increasing reaction rates due to temperature, and the deleterious effects of temperature on macromolecular stability and/or integration of metabolism. This interpretation also leads to an explanation of why the temperature for maximum growth rate does not necessarily correspond to the temperature of maximum tolerance to a second, suboptimal environmental constraint, i.e. the temperature of maximum tolerance is in the mid-range of the normal temperature region, where one would expect the greatest metabolic efficiency, and greatest capacity to overcome an environmental hurdle by homeostatic mechanisms.
If the lag time is a period of metabolic adjustment, requiring synthesis of new protein, it follows that the effect of temperature on those processes will be similar to the effect of temperature on growth rate. The induction of lag times due to abrupt temperature shift corresponds to whether the temperature shift involves a transition from one temperature region to another, particularly from the normal to low region.
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WATER ACTIVITY | Effect on Food Stability
Y.H. Roos, in Encyclopedia of Food Sciences and Nutrition (Second Edition), 2003
Microbial Stability
Microbial growth requires a minimum aw, in addition to pH, temperature, and other appropriate conditions that are important for the growth of bacteria, molds, and yeasts. The water activity of high-moisture foods, especially processed foods, can be manipulated to some extent by the addition of salts and sugars or other ingredients, which are known to reduce water activity. Such compositional alterations are highly advantageous in product development and food safety control. For many products, the effects of compositional changes on water activity can be predicted on the basis of composition and confirmed by measuring water activity of the final product. This allows an understanding of storage requirements and estimation of the product shelf-life in various storage conditions. In high-moisture foods, the main role of water activity control is to govern and reduce the risk of the growth of pathogenic and spoilage bacteria. Examples of water activity limits for the growth of selected microorganisms are given in Table 2.
Table 2. Water activity (aw) limits for the growth of selected pathogenic and spoilage microorganisms
| Bacteria | |
| Bacillus cereus | 0.930 |
| Bacillus subtilis | 0.900 |
| Campylobacter jejuni | 0.990 |
| Clostridium botulinum | 0.940 |
| Clostridium perfringens | 0.945 |
| Escherichia coli | 0.935 |
| Halobacterium halobium | 0.750 |
| Lactobacillus plantarum | 0.940 |
| Listeria monocytogenes | 0.920 |
| Salmonella spp. | 0.940 |
| Shigella spp. | 0.960 |
| Staphylococcus aureus | 0.860 |
| Vibrio parahaemolyticus | 0.936 |
| Yersinia enterocolitica | 0.960 |
| Molds | |
| Aspergillus candidus | 0.750 |
| Aspergillus flavus | 0.780 |
| Aspergillus niger | 0.770 |
| Erotum echinulatum | 0.620 |
| Penicillium citrinum | 0.800 |
| Penicillium expansum | 0.830 |
| Penicillium patulum | 0.810 |
| Rhizopus nigricans | 0.930 |
| Xeromyces bisporus | 0.610 |
| Yeasts | |
| Saccharomyces bailii | 0.800 |
| Saccharomyces cerevisiae | 0.900 |
| Saccharomyces rouxii | 0.620 |
The lowest water activity limit for microbial growth of 0.60aw allows the growth of xerophilic yeasts. Above this limiting water activity, IMFs have an increasing possibility for the growth of various microorganisms with increasing water activity. However, the water activities of IMFs are such that pathogenic bacteria are unable to grow, but there is a possibility for the growth of molds and yeast. The growth of these microorganisms must be controlled by careful adjustment of product water activity, use of protective packaging to avoid contamination, and selection of appropriate humectants, pH control, and use of antimicrobial agents.
Microbial stability is an obvious, and often the most important, criterion in food preservation. The aw limits for growth of various microorganisms, as shown in Figure 1, are well established and successfully used in food product development and manufacturing as well as control of product safety. Furthermore, in high-moisture foods and several IMF products, water activity is relatively constant and dependent on composition, especially solids content, and the type of water-soluble components.
Figure 1. Growth of various microorganisms at different water activity conditions.
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Volume 1
Marco Confalonieri, ... Maria Concetta Volpe, in Encyclopedia of Respiratory Medicine(Second Edition), 2022
Regulation of Innate Immunity
Microbial growth is inhibited by ATII cells, not only due to their surfactant properties, but also as they are capable of recruiting immune effector cells, including several macrophage populations, as well as secreting a variety of antimicrobial peptides, e.g.,b-defensins-2, lipocalin-2, and lysozyme. Moreover, they provide the reducing substances in the alveolar fluid that neutralize oxidant gases, such as the surfactant phospholipids, reduced glutathione, ascorbate and urate. Furthermore, ATII cells orchestrate pulmonary innate immunity by suppressing or stimulating the macrophage inflammatory response, neutrophils and other immune cells (Whitsett and Alenghat, 2015). ATII cells can discriminately secrete a variety of chemokines and cytokines, e.g., IL-1b, IL-1a, IL-6, IL-8, epithelial neutrophil activating peptide-78, growth related oncogene-α, macrophage inflammatory protein-2, monocyte chemoattractant protein-1, exotoxin and many others. ATII also recognize unmethylated bacterial DNA by membrane TLR-9, leading to NF-B activation and the production of IL-6, IL-8, and defensing-2.
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Scientific Fundamentals of Biotechnology
N.S. Panikov, in Comprehensive Biotechnology (Third Edition), 2019
1.18.1 Introduction
Microbial growth dynamics is a subject of numerous fundamental and applied research studies in modern microbiology and biotechnology. Usually, biotechnologists want to know the time progress of a normally developing bioprocess, say, product formation associated with cell growth, nutrient uptake, and respiration, as well as all possible undesirable deviations, such as plasmid loss, contamination, cell autolysis, etc. We can predict normal growth dynamics before setting up a real experiment or technological line by using mathematical simulation models of different degrees of complexity. Such a preliminary simulation is very useful for planning and optimizing real-life experiments. Moreover, the verified dynamic mathematical models provide efficient tools to optimize the yield of target product, minimize undesirable generation of waste products, etc. Another important implication of growth dynamics is that recorded growth curves carry valuable hidden information about the bioprocess. Based on growth dynamic patterns, we can distinguish the effects of products or substrate inhibition, identify growth-limiting substrates at various stages, pinpoint the importance of positive or negative interactions in a mixed or genetically inhomogeneous culture, etc.
The scientific discipline that uses special quantitative tools to study the development of any process in time—physical, chemical, or biological—is called kinetics (from the Greek κινετιχοσ, forcing to move). Kinetic studies in microbiology cover all dynamic manifestations of microbial life: growth itself, survival and death, product formation, adaptations, mutations, cell cycles, environmental effects, and biological interactions. Kinetics provide a theoretical framework for optimal design in biotechnologies, based on fermentation and enzyme catalysis, as well as for the employment of outdoor activity of natural microbial populations (wastewater treatment, soil bioremediation, etc.).
In contrast to simple rate measurements, kinetic studies require the perception of the underlying basic mechanisms of studied processes. We will define mechanistic studies as those that interpret some complex process as an interplay of several simpler reactions; for example, cell growth can be explained through the activity of enzymes, and microbial community dynamics can be interpreted through the behavior of individual cells and populations. Ideally, mechanistic studies infer the coupling of experimental measurements with analysis of simulating mathematical models. The models formalize postulated mechanisms, so that the comparison of observations and the model's predictions allows one to discard an incorrect hypothesis.
Quantitative studies in microbiology often involve the assessment of “growth stoichiometry.” Stoichiometry in chemistry studies the quantitative relationship between reactants and products in a chemical reaction. In microbiology, growth stoichiometry stands for a quantitative relationship between substrates and products of microbial processes, including biomass formation. In practical terms, kinetics and stoichiometry are tightly linked to each other; stoichiometry mainly addresses problems of a static nature (how much? in what proportion?), whereas kinetics considers the dynamics and function questions (at what rate? by which mechanism?).
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Scientific Fundamentals of Biotechnology
N.S. Panikov, in Comprehensive Biotechnology (Second Edition), 2011
1.20.1 Introduction
Microbial growth dynamics is a subject of numerous fundamental and applied research studies in modern microbiology and biotechnology. Usually, biotechnologists want to know the time progress of, say, product formation associated with cell growth, nutrients uptake, respiration, and other metabolic processes. We can predict growth dynamics before setting up a real experiment or technological line by using mathematical simulation models of different degrees of complexity. Such a preliminary simulation is very useful for planning and optimizing real-life experiments. Moreover, the verified dynamic mathematical models provide efficient tools to optimize the yield of target product, minimize undesirable generation of waste products, etc. Another important implication of growth dynamics is that recorded growth curves carry important hidden information about microbial cells, growth regulation, and interactions. Based on growth dynamic pattern, we can distinguish the effects of products or substrate inhibition, identify growth-limiting substrates at various stages, pinpoint the importance of positive or negative interactions in a mixed or genetically inhomogeneous culture, etc.
The scientific discipline that uses special quantitative tools to study the development of any processes in time – physical, chemical, or biological – is called kinetics (from the Greek κινετικοσ, forcing to move). Kinetic studies in microbiology cover all dynamic manifestations of microbial life: growth itself, survival and death, product formation, adaptations, mutations, cell cycles, environmental effects, and biological interactions. Kinetics provide a theoretical framework for optimal design in biotechnologies, based on fermentation and enzyme catalysis, as well as on the employment of outdoor activity of natural microbial populations (wastewater treatment, soil bioremediation, etc.).
Contrary to simple rate measurements, kinetic studies require the perception of the underlying basic mechanisms of studied processes. We will define mechanistic studies as those that interpret some complex process as an interplay of several simpler reactions; for example, cell growth can be explained through the activity of enzymes and microbial community dynamics can be interpreted through the behavior of individual cells and populations. Ideally, mechanistic studies infer the coupling of experimental measurements with analysis of simulating mathematical models. The models formalize postulated mechanisms, so that the comparison of observations and the model’s predictions allows one to discard an incorrect hypothesis.
Quantitative studies in microbiology often involve the assessment of ‘growth stoichiometry’. Stoichiometry (Greek στωικηειον, element) is the quantitative relationship between reactants and products in a chemical reaction. In microbiology, stoichiometry stands for a quantitative relationship between substrates and products of microbial processes, including biomass formation (the consequence of complying with mass and energy conservation laws). In practical terms, kinetic and stoichiometry are tightly linked to each other, but stoichiometry mainly addresses problems of a static nature (how much? in what proportion?), whereas kinetics considers the dynamics questions (at what rate? by which mechanism?).
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