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Effects Strain Predicting Likely
This is a more general form of Michaelis—Menten kinetics that involves extensive reaction stoichiometries Liebermeister and Klipp, These expressions are approximate kinetics that involves the representation of a family of semi-mechanistic approaches Liebermeister et al.
Biochemical networks and their processes can be modeled with cooperativity and saturation by using a canonical formalism including equations similar to Hill rate laws. They include a local representation given an operating point, based on a functional form derived from Taylor series approximations in a special transformation space, defined by power-inverses and logarithms of power-inverses Sorribas et al.
Moreover, the formalism can be used as an extension of power laws with a bigger accuracy for numerical simulations, and to explore predicted solutions coming from constraints of local sensitivities and different saturation fractions. Deterministic formulations of reaction kinetics are realistic when the number of reacting molecules is large per reactant, which is the case of the most common modeled cell factories.
However, for small numbers of chemical species in relevant applications, stochastic behavior may happen, such as in signaling or gene expression, for which stochastic simulation approaches are taken into account. The most common formulation for stochastic models is the chemical master equation Ullah and Wolkenhauer, This approach introduces new insights to the field since solutions can satisfy conditions that change in time.
Deviations, in the form of noise, are included in a chemically reacting system, which can explain connections between stochastic equations and deterministic rate laws. This kind of biochemical networks can exist in continuous or discrete state spaces as explained next. For continuous spaces, stochastic simulations use analytic approximations for the influence of randomness on the behavior of a system.
The representation is through stochastic differential or Langevin equations, which can be derived from corresponding deterministic partial differential equations for the kinetics of the probability distribution of the molecules Gillespie, In the case of discrete spaces, the basic idea is that the state of the system comes from the exact numbers of molecules, where the changes of reaction states are described by the probabilities of the transitions between every possible state property known as reaction propensity.
The formulation also includes a master differential equation that holds the time evolution of the state probabilities, which can be described by a non-trivial stochastic simulation algorithm Gillespie, To summarize approaches for phenotype prediction, Table 1 provides an overview of the dynamic modeling methods described in this review.
It provides information about the reasons for using the different algorithms, their advantages and disadvantages, and proposes illustrative examples of their application. In the three following paragraphs, one example from each class of methods—mechanistic, approximate and stochastic—is detailed showing how each modeling approach helped to better understand metabolic pathways for phenotype prediction.
Mechanistic modeling methods do not demand knowledge of the detailed mechanisms of a system by using conventional expressions to describe structural features of metabolic systems, as well as to model the added effect of two or more reversible inhibitors or activators.
This approach is combined with the fact of kinetic parameters able to be estimated with traditional methods since they are not always available. One notable example is the generalized Hill function that can be used when molecular mechanisms are not well understood. For instance, available data was used to describe the expression regulation of the cydAB operon in E.
The method describes the changes of transcription factor concentrations that affect the rate of enzymatic reactions, depending on oxygen concentrations. The result is a model predicting the level of cydAB expression in agreement with available experimental data and simulation results. The use of generalized Hill functions allowed to bypass the problems of reconstructing the detailed mechanisms of the molecular subsystems.
Approximate modeling methods are typically used to facilitate the analysis and design of strongly non-linear pathways, using simpler universal expressions in the form of analytical functions. One example of their use is the log-linear approach, used together with available data on elasticities and control coefficients, to understand glycolytic pathways in yeast, which present a strong non-linear behavior Hatzimanikatis and Bailey, The analytical solution of the log-linear model for a number of metabolites and enzymatically catalyzed reactions, depends explicitly on information from metabolic control analysis MCA Fell, This solution considers a linearization around a steady-state using logarithmic deviations of the state variables and parameters.
Studies can be performed regarding the effect of modifications of the catalytic properties of an enzyme with respect to its substrate or regulatory effector , by changing the value of the corresponding elasticity. Time-response of fluxes show an excellent agreement between the original non-linear model and the log-linear model Hatzimanikatis and Bailey, However, the average performance has limitations under quasi steady-state, for which the prediction of metabolic functions can be deteriorated.
Stochastic modeling methods are commonly applied for systems with small number of chemical species, to describe processes that present deviations, e. Moreover, applied as a non-deterministic approach in continuous spaces, these methods can be used to describe common stiff reaction motifs in cellular metabolic systems, for instance, the enzyme-catalyzed conversion of a substrate into a product or its decay into its original constituents. The reactions can be divided in fast and slow time scales, and the simulations can reach very accurate levels under certain conditions Cao et al.
The time trajectories of the species of the model are simulated using random sampling, an approach that results in satisfying the formulation given by the deterministic Michaelis-Menten derivation.
Thus, this stochastic method is useful when a difference of speed can be found in the stages of the reaction, with the advantage of having simulations dramatically faster without noticeable loss of computational accuracy. The different classes of kinetics-based methods used for phenotype prediction explained in the previous subsections are illustrated in Figure 2 , and a qualitative comparison is made with respect to interaction networks, constraint-based methods and hybrid approaches.
Interaction networks are placed at the top in terms of network complexity, but contain few information and details of the behavior of the entities. Similarly, constraint-based models not only consider a big amount of interactions, but also provide more information about the properties of reaction rates happening; however, the detail on how the behavior of the reactions in time is low.
On the other hand, kinetics-based models are split in the graph space according to their deterministic or non-deterministic nature.
Non-deterministic methods refer to stochastic approaches, which can describe the operation of the systems with high detail and accuracy, but are typically limited in network size. Finally, hybrid approaches, a combination of stoichiometric and dynamic information, are positioned with a high level of accuracy on the description of high-sized networks, since the best features of two approaches are integrated into a single one.
The trend of research is to find methods that can predict behavior of bigger networks with more detail and accuracy. Examples of applications of kinetics-based and hybrid approaches are shown in the graph. Interaction networks and constraint-based approaches are only included in the qualitative comparisons and are not considered further, since they are out of the scope of this review and have been thoroughly examined in other reviews Markowetz and Spang, ; Lewis et al.
Position toward upper levels means genome-scale networks. Constraint-based and kinetics-based approaches can be joined in hybrid methods that aim at taking the best advantages of each of them. Examples of applications of kinetics-based and hybrid approaches are given from Tables 1 , 3 , respectively. The measurements obtained from experiments help to determine the values of individual parameters for kinetic rate expressions, initial conditions and outputs.
These values can be found in data repositories that compile this information such as BRENDA, which gives a collection of enzyme and metabolic information Schomburg et al. Kinetic parameters are found either all simultaneously, by making the model fit the measurements of the whole system, or one by one considering individual components and processes. Furthermore, both approaches are usually combined by fixing parameters to already known values and fitting the remaining ones.
However, different parameter values are often found from different sources, in distinct experimental conditions, which brings compatibility problems. In addition to the parameter estimation, a study can be done on how changes on parameters affect the behavior of a model. Both methods will be reviewed in the next subsections. To avoid compatibility problems from finding parameters from different sources, parameter estimation techniques are used, indirect methods allowing optimal values of parameters to be calibrated as a solution to an estimation problem, making the model reproduce experimental measurements of different values instead of the parameter themselves.
Moreover, there are numerical optimization algorithms, with stochastic or deterministic approaches, that allow to determine the quality of experimental data in an efficient and automated way, making the data generated by different measurement methods reliable for quantitative dynamic modeling Raue et al. The critical consequences of the limited availability of kinetic data in metabolic dynamic modeling have been discussed with respect to specific organisms.
The study concludes a remarkable necessity for producing curated data to approximate in vitro conditions to the in vivo ones, so that an integration of available kinetic data into a complete large scale model is possible Costa et al. Parameter estimation follows an optimization algorithm that searches through a large set of possible values, under certain constraints and non-linear structures, which can imply complex objective functions with multiple solutions in the form of local optima.
The goal of optimization algorithms is to locate a global optimal in a feasible time, using local or global methods. Local methods have to initiate the optimization with reference parameters that can be measured experimentally or found in literature, and then improved after repeating the execution of the algorithm. The algorithms are commonly based on the Hessian and gradient of an objective function, usually computed by numerical methods, such as finite difference approximations.
However, this can bring problems with speed of convergence for complex structures. In addition, local methods find optimal solutions in some feasible neighborhood that are not always the global solution, unless the region of feasible solutions is convex Nocedal and Wright, On the other hand, global methods are based on metaheuristics, such as simulated annealing Kirkpatrick et al.
The combination of global and local methods has been the most successful tool to explore the parameter space when solutions are close to an optimal Moles et al. The objective of metaheuristic methods used for the parameter estimation task is to accelerate the process for large-scale systems biology models that are usually non-linear dynamic systems. This can be achieved with parallel and self-adaptive cooperative strategies based on scatter search optimization, which can significantly reduce computation times, and improve performance and robustness Penas et al.
The classical approach for the objective function, when performing the estimation of parameters, consists in minimizing the difference between the model output and the experimental data Chou and Voit, The formulation of the objective function is described as follows:. Furthermore, the parameter estimation can be seen as a geometrical problem, as stated previously, or also as a statistical formulation Ljung.
Likelihood theory is equivalent to least squares theory, and it yields identical estimators of the structural parameters except for the variance for linear and nonlinear models when the error terms are assumed to be independent and normally distributed Burnham and Anderson, Additionally, optimal parameters can be found by exploiting the local geometry of the steady-state manifold and its stability properties, due to the dynamics of the process restricted by steady-state constraints, such as initial conditions at equilibrium Fiedler et al.
Further, there are computational optimization tools available that implement metaheuristic methods for parameter estimation that can be applied to multiple domains of systems biology and bioinformatics, such as the MEIGO toolbox Egea et al. Finally, global optimization for non-linear dynamic models has been presented as a solution for improving computation times in comparison with deterministic global methods Rodriguez-Fernandez et al.
An important procedure performed in parallel to parameter estimation is to study the uniqueness and level of confidence of the variables that are going to be computed.
For that, performing identifiability analysis, local or global, is essential to evaluate the goodness of experimental data to determine model parameters. However, certain models are not identifiable according to their structure, based on known inputs and measured outputs, which turns parameter estimation meaningless. Structural identifiability analysis helps to know which quantities have to be measured and which are able to be estimated.
Theory and tools available for the study of identifiability have been previously reviewed and discussed, together with related concepts such as sensitivity to parameter perturbations, observability, distinguishability, and optimal experimental design Villaverde and Barreiro, Many algorithms have been developed for this task, one in particular using observability, to know how the internal states of a rational model can be inferred by the nature of its outputs.
Moreover, computational tools have been exploited to analyze the structural identifiability of a very general class of nonlinear models by extending previous methods, and also showing how to modify unidentifiable models to make them identifiable Villaverde et al.
Besides these tools, methods to analyze global structural identifiability for arbitrary model parameterization have been developed Ljung and Glad, , as well as to assess local structural identifiability for a general non-linear state-space model Stigter and Molenaar, Furthermore, to evaluate the accuracy of estimated parameters, it is common to analyze standard parameter confidence intervals, defined as a quadratic approximation of the logarithmic likelihood around the optimal value Raue et al.
The general parameter estimation procedure is described in Figure 3 , considering the main types of approaches. Before estimating parameters, a study of the dynamic data has to be performed using structural identifiability. This process helps to qualitatively assess if the available data is useful to make suitable predictions. After this, the search for optimal values that follows depends on the type of approach chosen: Description of the overall parameter estimation procedure.
First, quality of experimental data is studied to determine suitable parameters via structural identifiability analysis. Then, parameter estimation is performed, locally or globally, according to the type of problem formulation. The equivalence between the geometrical and statistical formulations is noted. This type of study allows to identify how a model varies its behavior, such as changes in fluxes and metabolite concentrations, in response to a perturbation around some points in the parameter space.
This analysis can be done through genetic modifications affecting enzyme concentrations, which will allow to identify reasonable ME targets that affect positively the behavior of a cell factory. This kind of sensitivity analysis for dynamic models can be performed through methods such as MCA Fell, MCA quantifies, through two different dimensionless indices, how the control of a flux in equilibrium state is distributed among the enzyme reactions in a particular pathway, namely elasticity coefficients ECs and flux control coefficients FCCs.
ECs are defined using metabolite concentrations and reaction rates catalyzed by enzymes with particular concentrations. FCCs for any flux in steady-state show the degree of control of enzymes on the pathway of that specific flux. ECs and FCCs help to connect properties of the system and components, using the fact that for each metabolite k , the sum of the product of ECs and FCCs is zero with respect to that metabolite.
Larger values of FCCs indicate that the corresponding reactions are primarily controlling the flux, leading to target those enzymes for a successful ME of the corresponding pathways; details on MCA method are further discussed in Fell Moreover, MCA can be applied to steady-state fluxes and metabolite concentrations, and combined with parameter sampling approaches to analyze parameter uncertainties Wang et al.
Some extensions of MCA have been developed for more significant modifications Nikolaev, , since a model with good predictive power is required to simulate larger changes in structure or parameters of the model, which means robustness with respect to different operating references. For instance, candidate targets, after MCA increases the production of some compound, can suggest large changes in two or more enzyme concentrations by firstly simulating a deletion strategy.
Then, by overexpressing an enzyme and analyzing how pathways were affected, this results in a combination of two modifications that can improve a certain flux in the desired direction, compared to the results of the wild-type strain Hoefnagel et al.
The development of dynamic models to quantitatively describe the systemic behavior of essential microbial functions is crucial for the rational design of ME applications. The study of the central carbon metabolism of species, which redirects carbon fluxes to the formation of carbon products, has become of great importance for systems biology approaches. The use and improvement of different mathematical techniques to describe the kinetics of the central carbon metabolism of E.
This can be seen as a case study to discuss the insights of this review regarding the use and evolution of dynamic models of E. One of the first remarkable attempts was made by Chassagnole and coworkers, who presented the design and experimental validation of a dynamic model that deals with the lack of kinetic information on the dynamics of the metabolic reactions.
This model uses experimental observations of intracellular metabolite and co-metabolite concentrations to validate the model structure and to estimate kinetic parameters Chassagnole et al. Some years later, a similar approach was developed, with the integration of pathways for the tricarboxylic acid cycle and anaplerotic reactions, and including analysis of metabolic changes inside the cell in response to specific pathway gene knockouts Kadir et al.
Although these kinetic models study and evaluate in detail many biochemical pathways, reactions and cycles, they share drawbacks, and use simplifications of complex enzymatic activities. Also, they disregard system regulatory properties, such as metabolic regulation networks, and a weak evaluation of the models by insufficient or limited types of experimental data. Peskov and collaborators proposed a more extensive and detailed model to solve these problems.
They use several stages according to Cleland's classification to develop and evaluate their model Cleland, , which allowed to use in vitro and in vivo experimental data, based on fluxomics and metabolomics, to avoid the ambiguity shown in previous models caused by comparing the coincidence between predicted and experimental data Peskov et al. To model the kinetic behavior of the system, the reaction rate complexities depend on the catalytic mechanism, the regulatory properties allowed and the amount of experimental data available for evaluation of the predictions.
Therefore, they use four levels of detail: This model is capable to suggest better hypotheses about system regulatory and functional properties, since it uses analyses of different types of experimental data. However, it takes into account a small number of reactions compared with the thousands present in a genome-scale model. A more detailed descriptions of the formalisms used can be found in Peskov et al. Later on, Khodayari and his team made a good effort to facilitate the construction of a larger-scale kinetic model of E.
The model integrates all the reactions used in previous discussed models Chassagnole et al. Their method consists in decomposing metabolic reactions into elementary reaction steps and incorporating phenotypic observations including genetic perturbations in a parameterization scheme.
The model satisfies steady-state experimental fluxomics and metabolomics data, and minimizes discrepancies between model predictions and experimental measurements. The estimation of parameters problem is solved using genetic algorithms taking into account wild-type and mutant flux data. A Michaelis-Menten equivalent formalism of the model, shows that the predicted fluxes and metabolite concentrations are within acceptable uncertainty ranges Khodayari et al.
Nevertheless, this kind of study requires the availability of additional experimental flux measurements for mutant strains having perturbations in different parts of the metabolism, in order to perform more robust parameterization of a genome-scale kinetic model.
Moreover, its application is restricted to the steady state with a constant cell growth rate. The extension and integration of new insights to previous dynamic models is a trend to improve the power of predictions. Jahan and coworkers proposed a kinetic model that uses detailed kinetic equations, with gene regulations to reproduce the dynamics of wild-type and multiple genetically modified mutants under aerobic conditions in a batch culture.
At the same time, the model estimates a specific cell growth rate that is linear to the total production of adenosine triphosphate, which reflects the reconstituted metabolic pathways caused by genetic changes and avoiding the use of Monod equation as in previous models. The values of parameters are estimated using a constrained evolutionary search method to be able to predict allosteric effectors and gene expressions.
The values estimated are fixed for all the mutant cases, an improvement with respect to previous models that use different parameter values for each mutant. The dynamic model uses the structure of the batch or continuous culture based on mass balance equations in a system of ODEs, and a cell growth rate estimation connected to the flux of production of adenosine triphosphate Jahan et al.
Another effort to include new knowledge and to improve capabilities of a dynamic model of E. They developed and validated a model that links metabolism to environment and cell proliferation through intracellular metabolite levels. Also, the study explores the fact of metabolic regulation producing robust properties and a control widely distributed across the network, from a molecular level to the overall cellular physiology level.
The model is based on the ones published by Kadir and Peskov, but it increases the number of pathways and the level of mechanistic detail, and also includes exchange reactions and a single reaction to model growth coupled to glucose uptake. MCA was used to validate control properties and impact of a small change in the rate of each reaction, flux and metabolite concentration Millard et al.
This study shows that deeper analyses have to be performed to ensure the validity of proposed structures. We have seen that different mathematical models describing the same organism, do not ensure full capabilities individually. Different studies and considerations at local and global levels have to be evaluated for a dynamic model to be competitive for genome-scale applications. There has been an interest in supporting existing predictive models using approximate kinetic rate expressions and well-known structures.
One example is using the lin-log approach in the kinetic model developed by Chassagnole and coworkers. This work was performed by Visser and collaborators, who compares the validity of a mechanistic model and a lin-log model derived from the mechanistic one.
The study demonstrated the value of lin-log approaches as MCA extensions, since it allows to build kinetic models, based on MCA parameters, that can be used for constrained optimization problems, being valid for large changes of metabolite and enzyme levels Visser et al.
CSO is usually seen as a bi-level framework, because its tasks are commonly divided into two stages or layers: The objective is to find the optimal set of genetic modifications applied to an organism to achieve a desirable goal Burgard et al. Possible solutions need to fulfill feasibility specifications, and thus the optimization algorithm also deals with the definition of solution spaces that can be implemented in vivo. These CSOMs, can be based on purely constraint-based or kinetics-based modeling, or the combination of both.
These tasks can also be used in combination with each other to find more complex ME strategies. The solutions are translated into modifications in the network applied to the flux constraints or kinetic parameters, for constraint-based and kinetics-based models, respectively. A complete description and discussion regarding application of CSOMs for constraint-based models, with a special classification as exact bilevel mixed-integer, metaheuristic and elementary-mode analysis-based programming methods, is provided in Maia et al.
The use of metaheuristic approaches in CSOMs brings some important advantages with respect to exact methods, such as providing a framework that can easily scale well for bigger models and larger numbers of modifications, which is computationally less costly and possibly faster for finding sufficiently good solutions.
Another remarkable feature is to gain a level of flexibility to implement complex frameworks, with an independent implementation of phenotype prediction and strain optimization layers, which can allow, for example, to implement nonlinear, or even discontinuous, objective functions in the optimization layer to define more significant problems. Common approaches combine the assumption that microorganisms naturally maximize their growth, with simulated annealing or EAs to select genetic manipulations that will result in a desirable high productivity goal Rocha et al.
For industrial biotechnology purposes, a mathematical model must be able to simulate, predict and examine a variety of scenarios where a biological system is operating under certain assumptions and environmental conditions. It is possible to design CSOMs based on dynamic models through the study of the transient and equilibrium states of the system. The main goal of these CSOMs is to find forms of reaction rates, i.
In silico ME strategies are designs that represent a way of improving the performance of an organism toward a specified goal. They can include the use of dynamic models that predict the behavior of the system under the influence of perturbations, such as gene deletions, enzyme modulations or changes in the medium conditions.
The selection of a dynamic model with high power of prediction benefits the design of newly engineered microbial strains. CSO using dynamic models aims at identifying proper gene deletions or levels of enzymatic activity applied to microbial processes. The methods can be based on two types of formulations, exact or stochastic. On the side of exact formulations, a basic approach involves linear programming problems with linear objectives and constraints defined in a convex space.
However, most of the optimization problems applied to biological systems introduce non-linear programming problems over continuous or discrete variables.
This means that the search process can take place in non-convex spaces, resulting in the possible existence of multimodality, i. This type of problems belongs to the class of non-deterministic polynomial-time hard problems, which are computationally more complex and less efficient to solve than polynomial-time ones Erickson, Exact methods are always able to yield the optimal solutions, but their computational time increases exponentially with the size of networks and of the solutions, thus, demanding the development of approximate and faster algorithms.
These include exact Mixed-Integer Linear Programming formulations, that can be combined with approximation methods such as generalized linearization of kinetic models Vital-Lopez et al. Further, global optimization of non-linear dynamic models has been explored by recasting the system into an equivalent generalized mass action model, which facilitates the numerical computation for the optimization task to identify genetic modifications Pozo et al.
On the other hand, stochastic global optimization can be used to locate solutions near to the global optimum, including EAs which have shown acceptable performance for applications in biological systems Banga, ; Rocha et al. Examples of stochastic optimization include: In addition, they found mediating effects for both social support and income inequality, in that when added together in a model, the main effects for both diminished.
Finally, the moderating effect of income inequality and social support suggested that the presence of high levels of social support reduced the effect of economic inequality on rates of homicide. Maume and Lee examined institutional anomie theory and used disaggregated homicide rates e. They suggested that non-economic institutions will not only moderate the influence of the economy on crime rates, but will also mediate the influence of the economy on crime rates.
Over all, they found limited support for the commonly used moderating hypothesis; that is, they only found one significant interaction effect that suggested the effect of economic pressure on homicide rates was significantly weaker in counties with low levels of welfare disbursements.
More support was found for the mediating hypothesis in that, across all three models, the measure of economy, the Gini coefficient of family income inequality, was reduced when predictors representing the non-economic institutions were added into the model.
In support of institutional anomie theory, Maume and Lee conclude that non-economic institutions i. Piquero and Piquero examined institutional anomie theory in relation to property and violent crime rates using data from 50 US states and Washington, D.
Using census data for measures of the independent variables, Piquero and Piquero examined the education component of institutional anomie theory. For the most part, additive effects were significant and in the expected direction for both property and violent crime models.
More importantly, the interactive effects revealed that higher percentages of individuals enrolled full-time in college reduced the effect of poverty on both crime types, while the polity-economy interaction was only statistically significant for violent crime rates.
These studies, taken as a whole, suggest partial support for institutional anomie theory. Across a variety of different outcomes as well as aggregated units, and utilizing various measures representing non-economic institutions, there appears to be some support for the presumption of the importance of the economy in explaining instrumental crimes. The specific strains discussed in the theory include the failure to achieve positively valued goals e.
While many specific types of strain may fall into these categories, Agnew has attempted to specify the conditions under which strain may lead to crime. Strains that are 1 seen as unjust, 2 high in magnitude 3 associated with low social control, and 4 create some incentive to engage in criminal coping are most likely to lead to violence and delinquency.
According to general strain theory, individuals experiencing strain may develop negative emotions, including anger, when they see adversity as imposed by others, resentment when they perceive unjust treatment by others, and depression or anxiety when they blame themselves for the stressful consequence.
These negative emotions, in turn, necessitate coping responses as a way to relieve internal pressure. Responses to strain may be behavioural, cognitive, or emotional, and not all responses are delinquent. General strain theory, however, is particularly interested in delinquent adaptations. General strain theory identifies various types of delinquent adaptations, including escapist e. Coping via illegal behaviour and violence may be especially true for adolescents because of their limited legitimate coping resources, greater influence from peers, and inability to escape many stressful and frustrating environments.
Of the various types of negative emotions, anger has been identified as playing the key role in mediating the effect of strain on delinquency and violence. Some studies of the mediating model in general strain theory have focused on anger as the sole intervening factor in the relationship between strain and delinquency. Using data from the Youth in Transition survey, Agnew conducted the first study of this kind among tenth-grade boys.
He found that the strain these boys experienced in school and at home had both a direct effect and an indirect effect via anger on property offences, violent offences, and status offences. Agnew also found that anger had the strongest effect on violent offences. Mazerolle and Piquero focused on how anger mediated the impact of strain on violent responses among college students. In contrast to the above, Mazerolle et al. Agnew notes that survey research typically measures trait anger or the disposition of anger, whereas general strain theory argues that strain produces situation-specific or short-term anger, which in turn may lead to crime.
Researchers who measure trait anger may find that it does not mediate between strain and crime. Other studies examined the role of other negative emotions, such as depression and anxiety, but found no mediating effects on delinquent outcomes violent or non-violent.
Piquero and Sealock conducted a study among incarcerated youths on the effects of anger and depression in mediating the impact of strain on both violent and property crime. The results showed that depression failed to predict both types of crime, whereas anger predicted violence but not property crime. There have, however, been a few studies that indicate that the role of other mediating variables, apart from anger, should not be too quickly dismissed. Simons, Yi-Fu, Stewart and Brody , for example, found that strain increased depression, which in turn contributed to crime.
Agnew notes that researchers should attempt to investigate other variables that may mediate between strain and crime. For instance, strain may increase attitudes favourable to aggression, which in turn may lead to crime. General strain theory has attempted to specify the factors which increase the likelihood that individuals will cope with strain by committing crime. Agnew contends that crime becomes a likely outcome when individuals have a low tolerance for strain, when they have poor coping skills and resources, when they have few conventional social supports, when they perceive that the costs of committing crime is low, and when they are disposed to committing crime because of factors such as low self-control, negative emotionality, or their learning history.
Empirical research has offered some support for the above. Such individuals are impulsive, overly active and quick to lose their tempers. Well-being may be estimated based on a number of dimensions, including wealth, income, power and prestige. The research of Agnew et al. Relative deprivation researchers explicitly recognize that people evaluate themselves relative to comparison others, and not all persons may choose the same comparison other.
There are four formal theories of relative deprivation. Davis argues that people will experience relative deprivation when they lack X, perceive that similar others have X, want X, and feel entitled to have X.
Runciman adds that the individual must think it feasible to obtain X, while Crosby asserts that individuals must also lack a sense of responsibility for failure to possess X.
Runciman distinguishes between egoistic and fraternal relative deprivation. The former occurs at the individual level and the latter when individuals compare their group with other reference groups. Value expectations refer to goods and opportunities that the individual wants and feels entitled to, estimated based on comparisons with others. Value capabilities are the goods and opportunities that individuals already possess.
Based on this typology, Gurr distinguishes between aspirational, decremental, and progressive relative deprivation. Relative deprivation results in feelings of despair, frustration, grievance, and anger, and may be a powerful motivator of crime. A number of researchers have employed relative deprivation as a predictor of crime. These authors include drug use as a dependent measure and suggest that relative deprivation may result in various responses other than hostility displaced on others.
Relative deprivation was specified with respect to three reference groups: Accordingly, relative deprivation was assessed by asking respondents to compare their total family income to these three groups. They therefore hypothesize that the relative deprivation—crime relationship is mediated by negative self-feeling. Survey data from 6, adults are employed in their study. Using nine logistic regression models, Stiles et al. Poverty and four controls are employed in each model. Relative deprivation friends significantly predicted violent and property crime; relative deprivation neighbours predicted property crime and drug use; and relative deprivation national norms predicted all three crimes.
To examine the mediating effect of negative self-feeling, Stiles et al. A significant decrease in the predictive ability of relative deprivation indicates a mediated effect Baron and Kenny, In seven of the nine models, the explanatory power of relative deprivation decreased significantly with the addition of negative self-feeling.
Poverty was a significant predictor of property crime in all nine models, both without and with the inclusion of negative self-feeling. Baron examines the effects of strain on property crime, violent crime and drug use. In so doing, he includes two operationalizations of relative deprivation as predictors. This assessment implies a comparison with others and represents a measure of relative deprivation that relates specifically to economic status.
The second measure was broader in nature and asked respondents to give an overall ranking of themselves relative to others in Canadian society. Crime was measured using self report. The sample was homeless youths from Vancouver. Regression analysis indicated that the monetary dissatisfaction operationalization of relative deprivation predicted property crime, while the second more inclusive operationalization predicted property and violent crime. Baron uses the same data set as Baron and has almost identical findings.
The findings of Baron are consistent with Agnew et al. Such dependent measures acknowledge that the responses to relative deprivation may be cognitive, affective or behavioural. The respondents were students who were organized into groups for the experiment. These students were selected by pre-test designed to select only those students who believed that women should be given more encouragement to apply for high-status jobs. In each experimental session, two groups were placed in adjoining rooms to facilitate the belief that an inter-group interaction was taking place.
The two groups never met directly; all communication was through a confederate. Just after this manipulation, collective relative deprivation was measured to verify if the manipulation was successful.
The dependent variables were also measured. Group differences in the dependent measures for the relatively deprived and the non-deprived groups were compared. There were significant differences in all four dependent measures supporting the influence of relative deprivation. Rosenfeld is one of the few studies which explores the relationship of relative deprivation to crime using structural rather than individual measures.
The dependent variables are property and violent crimes. Controls include population size and per cent black. Relative deprivation is defined as the product of the intensity of deprivation, the scope of deprivation, and the level of economic aspirations among poor families in the Standard Metropolitan Statistical Area.
Intensity refers to the degree of discrepancy or difference between economic capabilities and expectations, and is operationalized as the difference between the mean income of families below the poverty level and the mean income of all families in the SMSA.
The scope of deprivation refers to the proportion of the population sharing some specified level of deprivation, and is operationalized as the percentage of families with incomes below the federal poverty level. Aspirations are measured by the ratio of median years of school completed by heads of poor families to median years of school completed by all family heads.
This measure assumes that the economic aspirations of low-income people will vary directly with their educational attainment. This measure of relative deprivation has merit, since it assesses economic inequality within the context of level of economic aspirations. This measure successfully predicted homicide, rape, assault, burglary, larceny and auto theft, but not robbery. Aging flood control facilities right map: Water for agriculture, energy production, and use in homes and buildings is expected to decline across most of the Southeast away from the coasts comparing decadal trends between and , relative to The western part of the Southeast region is expected to see the largest water reductions in water availability.
The hatched areas indicate where the predicted decrease in water availability associated with the range of climate scenarios is most certain.
Modified from USGCRP  In general, the Southeast has had water resources capable of supporting local populations, ecosystems, agriculture, and energy production. However, parts of the region have experienced droughts, and anticipated population growth and changing land-use are likely to add further strain to the water supply.
Ground-level ozone is expected to increase across most of the Southeast as temperatures rise. Ground-level ozone, an air pollutant, is a component of smog that is harmful to human health and may increase the likelihood of death. This map shows projected changes in average yearly ground level ozone concentration in as compared to in a scenario where greenhouse gas emissions are gradually reduced beginning around mid-century.
High temperatures also contribute to poor air quality, including the formation of ground-level ozone, which poses a risk to people with asthma and other respiratory illnesses. Ground-level ozone is projected to increase in the 19 largest urban areas of the Southeast, likely increasing hospital admissions due to respiratory illnesses, emergency room visits for asthma, and missed school days by children. Warmer waters have been linked to the spread of some bacteria.
As temperatures increase, the frequency of other climate-sensitive disease outbreaks are also expected to increase. More algal blooms could increase rates of ciguatera fish poisoning, an illness caused by eating fish carrying toxins produced by the algae. An increase in wildfires driven by drought conditions can affect human health through poor air quality and direct injury. Increased flooding and hurricane intensity could also present extreme public health and emergency management challenges.
Warmer air and water temperatures, hurricanes, increased storm surges, and sea level rise are expected to alter the Southeast's local ecosystems and agricultural productivity. Warmer temperatures could increase the number and intensity of wildfires, as well as outbreaks of damaging forest pests, including the hemlock woolly adelgid.
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