Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology

PubMed Central

Koutinas, Michalis; Kiparissides, Alexandros; Pistikopoulos, Efstratios N.; Mantalaris, Athanasios

2013-01-01

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals. PMID:24688682

Mechanisms of Myofascial Pain

PubMed Central

Jafri, M. Saleet

2014-01-01

Myofascial pain syndrome is an important health problem. It affects a majority of the general population, impairs mobility, causes pain, and reduces the overall sense of well-being. Underlying this syndrome is the existence of painful taut bands of muscle that contain discrete, hypersensitive foci called myofascial trigger points. In spite of the significant impact on public health, a clear mechanistic understanding of the disorder does not exist. This is likely due to the complex nature of the disorder which involves the integration of cellular signaling, excitation-contraction coupling, neuromuscular inputs, local circulation, and energy metabolism. The difficulties are further exacerbated by the lack of an animal model for myofascial pain to test mechanistic hypothesis. In this review, current theories for myofascial pain are presented and their relative strengths and weaknesses are discussed. Based on new findings linking mechanoactivation of reactive oxygen species signaling to destabilized calcium signaling, we put forth a novel mechanistic hypothesis for the initiation and maintenance of myofascial trigger points. It is hoped that this lays a new foundation for understanding myofascial pain syndrome and how current therapies work, and gives key insights that will lead to the improvement of therapies for its treatment. PMID:25574501

Comparing mechanistic and empirical approaches to modeling the thermal niche of almond

NASA Astrophysics Data System (ADS)

Parker, Lauren E.; Abatzoglou, John T.

2017-09-01

Delineating locations that are thermally viable for cultivating high-value crops can help to guide land use planning, agronomics, and water management. Three modeling approaches were used to identify the potential distribution and key thermal constraints on on almond cultivation across the southwestern United States (US), including two empirical species distribution models (SDMs)—one using commonly used bioclimatic variables (traditional SDM) and the other using more physiologically relevant climate variables (nontraditional SDM)—and a mechanistic model (MM) developed using published thermal limitations from field studies. While models showed comparable results over the majority of the domain, including over existing croplands with high almond density, the MM suggested the greatest potential for the geographic expansion of almond cultivation, with frost susceptibility and insufficient heat accumulation being the primary thermal constraints in the southwestern US. The traditional SDM over-predicted almond suitability in locations shown by the MM to be limited by frost, whereas the nontraditional SDM showed greater agreement with the MM in these locations, indicating that incorporating physiologically relevant variables in SDMs can improve predictions. Finally, opportunities for geographic expansion of almond cultivation under current climatic conditions in the region may be limited, suggesting that increasing production may rely on agronomical advances and densifying current almond plantations in existing locations.

ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics

PubMed Central

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J.

2014-01-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity. PMID:24992156

Modeling the voltage loss mechanisms in lithium-sulfur cells: the importance of electrolyte resistance and precipitation kinetics.

PubMed

Zhang, Teng; Marinescu, Monica; O'Neill, Laura; Wild, Mark; Offer, Gregory

2015-09-21

Understanding of the complex electrochemical, transport, and phase-change phenomena in Li-S cells requires experimental characterization in tandem with mechanistic modeling. However, existing Li-S models currently contradict some key features of experimental findings, particularly the evolution of cell resistance during discharge. We demonstrate that, by introducing a concentration-dependent electrolyte conductivity, the correct trends in voltage drop due to electrolyte resistance and activation overpotentials are retrieved. In addition, we reveal the existence of an often overlooked potential drop mechanism in the low voltage-plateau which originates from the limited rate of Li2S precipitation.

Corrosion fatigue crack propagation in metals

NASA Technical Reports Server (NTRS)

Gangloff, Richard P.

1990-01-01

This review assesses fracture mechanics data and mechanistic models for corrosion fatigue crack propagation in structural alloys exposed to ambient temperature gases and electrolytes. Extensive stress intensity-crack growth rate data exist for ferrous, aluminum and nickel based alloys in a variety of environments. Interactive variables (viz., stress intensity range, mean stress, alloy composition and microstructure, loading frequency, temperature, gas pressure and electrode potential) strongly affect crack growth kinetics and complicate fatigue control. Mechanistic models to predict crack growth rates were formulated by coupling crack tip mechanics with occluded crack chemistry, and from both the hydrogen embrittlement and anodic dissolution/film rupture perspectives. Research is required to better define: (1) environmental effects near threshold and on crack closure; (2) damage tolerant life prediction codes and the validity of similitude; (3) the behavior of microcrack; (4) probes and improved models of crack tip damage; and (5) the cracking performance of advanced alloys and composites.

Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, C. W.; Hood, Raleigh R.; Long, Wen

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat modelsmore » of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.« less

PiTS-1: Carbon Partitioning in Loblolly Pine after 13C Labeling and Shade Treatments

DOE Data Explorer

Warren, J. M.; Iversen, C. M.; Garten, Jr., C. T.; Norby, R. J.; Childs, J.; Brice, D.; Evans, R. M.; Gu, L.; Thornton, P.; Weston, D. J.

2013-01-01

The PiTS task was established with the objective of improving the C partitioning routines in existing ecosystem models by exploring mechanistic model representations of partitioning tested against field observations. We used short-term field manipulations of C flow, through 13CO2 labeling, canopy shading and stem girdling, to dramatically alter C partitioning, and resultant data are being used to test model representation of C partitioning processes in the Community Land Model (CLM4 or CLM4.5).

Pharmacometric Models for Characterizing the Pharmacokinetics of Orally Inhaled Drugs.

PubMed

Borghardt, Jens Markus; Weber, Benjamin; Staab, Alexander; Kloft, Charlotte

2015-07-01

During the last decades, the importance of modeling and simulation in clinical drug development, with the goal to qualitatively and quantitatively assess and understand mechanisms of pharmacokinetic processes, has strongly increased. However, this increase could not equally be observed for orally inhaled drugs. The objectives of this review are to understand the reasons for this gap and to demonstrate the opportunities that mathematical modeling of pharmacokinetics of orally inhaled drugs offers. To achieve these objectives, this review (i) discusses pulmonary physiological processes and their impact on the pharmacokinetics after drug inhalation, (ii) provides a comprehensive overview of published pharmacokinetic models, (iii) categorizes these models into physiologically based pharmacokinetic (PBPK) and (clinical data-derived) empirical models, (iv) explores both their (mechanistic) plausibility, and (v) addresses critical aspects of different pharmacometric approaches pertinent for drug inhalation. In summary, pulmonary deposition, dissolution, and absorption are highly complex processes and may represent the major challenge for modeling and simulation of PK after oral drug inhalation. Challenges in relating systemic pharmacokinetics with pulmonary efficacy may be another factor contributing to the limited number of existing pharmacokinetic models for orally inhaled drugs. Investigations comprising in vitro experiments, clinical studies, and more sophisticated mathematical approaches are considered to be necessary for elucidating these highly complex pulmonary processes. With this additional knowledge, the PBPK approach might gain additional attractiveness. Currently, (semi-)mechanistic modeling offers an alternative to generate and investigate hypotheses and to more mechanistically understand the pulmonary and systemic pharmacokinetics after oral drug inhalation including the impact of pulmonary diseases.

A climate-driven mechanistic population model of Aedes albopictus with diapause.

PubMed

Jia, Pengfei; Lu, Liang; Chen, Xiang; Chen, Jin; Guo, Li; Yu, Xiao; Liu, Qiyong

2016-03-24

The mosquito Aedes albopitus is a competent vector for the transmission of many blood-borne pathogens. An important factor that affects the mosquitoes' development and spreading is climate, such as temperature, precipitation and photoperiod. Existing climate-driven mechanistic models overlook the seasonal pattern of diapause, referred to as the survival strategy of mosquito eggs being dormant and unable to hatch under extreme weather. With respect to diapause, several issues remain unaddressed, including identifying the time when diapause eggs are laid and hatched under different climatic conditions, demarcating the thresholds of diapause and non-diapause periods, and considering the mortality rate of diapause eggs. Here we propose a generic climate-driven mechanistic population model of Ae. albopitus applicable to most Ae. albopictus-colonized areas. The new model is an improvement over the previous work by incorporating the diapause behaviors with many modifications to the stage-specific mechanism of the mosquitoes' life-cycle. monthly Container Index (CI) of Ae. albopitus collected in two Chinese cities, Guangzhou and Shanghai is used for model validation. The simulation results by the proposed model is validated with entomological field data by the Pearson correlation coefficient r (2) in Guangzhou (r (2) = 0.84) and in Shanghai (r (2) = 0.90). In addition, by consolidating the effect of diapause-related adjustments and temperature-related parameters in the model, the improvement is significant over the basic model. The model highlights the importance of considering diapause in simulating Ae. albopitus population. It also corroborates that temperature and photoperiod are significant in affecting the population dynamics of the mosquito. By refining the relationship between Ae. albopitus population and climatic factors, the model serves to establish a mechanistic relation to the growth and decline of the species. Understanding this relationship in a better way will benefit studying the transmission and the spatiotemporal distribution of mosquito-borne epidemics and eventually facilitating the early warning and control of the diseases.

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak

PubMed Central

McClelland, Amanda; Zelner, Jon; Streftaris, George; Funk, Sebastian; Metcalf, Jessica; Dalziel, Benjamin D.; Grenfell, Bryan T.

2017-01-01

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging. PMID:29084216

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.

PubMed

Lau, Max S Y; Gibson, Gavin J; Adrakey, Hola; McClelland, Amanda; Riley, Steven; Zelner, Jon; Streftaris, George; Funk, Sebastian; Metcalf, Jessica; Dalziel, Benjamin D; Grenfell, Bryan T

2017-10-01

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.

Mechanistic-empirical design concepts for continuously reinforced concrete pavements in Illinois.

DOT National Transportation Integrated Search

2009-04-01

The Illinois Department of Transportation (IDOT) currently has an existing jointed plain concrete pavement : (JPCP) design based on mechanistic-empirical (M-E) principles. However, their continuously reinforced concrete : pavement (CRCP) design proce...

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

PubMed

Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

2010-06-01

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

Mechanistic modeling of reactive soil nitrogen emissions across agricultural management practices

NASA Astrophysics Data System (ADS)

Rasool, Q. Z.; Miller, D. J.; Bash, J. O.; Venterea, R. T.; Cooter, E. J.; Hastings, M. G.; Cohan, D. S.

2017-12-01

The global reactive nitrogen (N) budget has increased by a factor of 2-3 from pre-industrial levels. This increase is especially pronounced in highly N fertilized agricultural regions in summer. The reactive N emissions from soil to atmosphere can be in reduced (NH3) or oxidized (NO, HONO, N2O) forms, depending on complex biogeochemical transformations of soil N reservoirs. Air quality models like CMAQ typically neglect soil emissions of HONO and N2O. Previously, soil NO emissions estimated by models like CMAQ remained parametric and inconsistent with soil NH3 emissions. Thus, there is a need to more mechanistically and consistently represent the soil N processes that lead to reactive N emissions to the atmosphere. Our updated approach estimates soil NO, HONO and N2O emissions by incorporating detailed agricultural fertilizer inputs from EPIC, and CMAQ-modeled N deposition, into the soil N pool. EPIC addresses the nitrification, denitrification and volatilization rates along with soil N pools for agricultural soils. Suitable updates to account for factors like nitrite (NO2-) accumulation not addressed in EPIC, will also be made. The NO and N2O emissions from nitrification and denitrification are computed mechanistically using the N sub-model of DAYCENT. These mechanistic definitions use soil water content, temperature, NH4+ and NO3- concentrations, gas diffusivity and labile C availability as dependent parameters at various soil layers. Soil HONO emissions found to be most probable under high NO2- availability will be based on observed ratios of HONO to NO emissions under different soil moistures, pH and soil types. The updated scheme will utilize field-specific soil properties and N inputs across differing manure management practices such as tillage. Comparison of the modeled soil NO emission rates from the new mechanistic and existing schemes against field measurements will be discussed. Our updated framework will help to predict the diurnal and daily variability of different reactive N emissions (NO, HONO, N2O) with soil temperature, moisture and N inputs.

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

Preparatory steps for a robust dynamic model for organically bound tritium dynamics in agricultural crops

DOE Office of Scientific and Technical Information (OSTI.GOV)

Melintescu, A.; Galeriu, D.; Diabate, S.

2015-03-15

The processes involved in tritium transfer in crops are complex and regulated by many feedback mechanisms. A full mechanistic model is difficult to develop due to the complexity of the processes involved in tritium transfer and environmental conditions. First, a review of existing models (ORYZA2000, CROPTRIT and WOFOST) presenting their features and limits, is made. Secondly, the preparatory steps for a robust model are discussed, considering the role of dry matter and photosynthesis contribution to the OBT (Organically Bound Tritium) dynamics in crops.

Defence mechanisms: the role of physiology in current and future environmental protection paradigms

PubMed Central

Glover, Chris N

2018-01-01

Abstract Ecological risk assessments principally rely on simplified metrics of organismal sensitivity that do not consider mechanism or biological traits. As such, they are unable to adequately extrapolate from standard laboratory tests to real-world settings, and largely fail to account for the diversity of organisms and environmental variables that occur in natural environments. However, an understanding of how stressors influence organism health can compensate for these limitations. Mechanistic knowledge can be used to account for species differences in basal biological function and variability in environmental factors, including spatial and temporal changes in the chemical, physical and biological milieu. Consequently, physiological understanding of biological function, and how this is altered by stressor exposure, can facilitate proactive, predictive risk assessment. In this perspective article, existing frameworks that utilize physiological knowledge (e.g. biotic ligand models, adverse outcomes pathways and mechanistic effect models), are outlined, and specific examples of how mechanistic understanding has been used to predict risk are highlighted. Future research approaches and data needs for extending the incorporation of physiological information into ecological risk assessments are discussed. Although the review focuses on chemical toxicants in aquatic systems, physical and biological stressors and terrestrial environments are also briefly considered. PMID:29564135

Pattern formation in mass conserving reaction-diffusion systems

NASA Astrophysics Data System (ADS)

Brauns, Fridtjof; Halatek, Jacob; Frey, Erwin

We present a rigorous theoretical framework able to generalize and unify pattern formation for quantitative mass conserving reaction-diffusion models. Mass redistribution controls chemical equilibria locally. Separation of diffusive mass redistribution on the level of conserved species provides a general mathematical procedure to decompose complex reaction-diffusion systems into effectively independent functional units, and to reveal the general underlying bifurcation scenarios. We apply this framework to Min protein pattern formation and identify the mechanistic roles of both involved protein species. MinD generates polarity through phase separation, whereas MinE takes the role of a control variable regulating the existence of MinD phases. Hence, polarization and not oscillations is the generic core dynamics of Min proteins in vivo. This establishes an intrinsic mechanistic link between the Min system and a broad class of intracellular pattern forming systems based on bistability and phase separation (wave-pinning). Oscillations are facilitated by MinE redistribution and can be understood mechanistically as relaxation oscillations of the polarization direction.

Descriptive vs. mechanistic network models in plant development in the post-genomic era.

PubMed

Davila-Velderrain, J; Martinez-Garcia, J C; Alvarez-Buylla, E R

2015-01-01

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

Indirect Effects of Environmental Change in Resource Competition Models.

PubMed

Kleinhesselink, Andrew R; Adler, Peter B

2015-12-01

Anthropogenic environmental change can affect species directly by altering physiological rates or indirectly by changing competitive outcomes. The unknown strength of competition-mediated indirect effects makes it difficult to predict species abundances in the face of ongoing environmental change. Theory developed with phenomenological competition models shows that indirect effects are weak when coexistence is strongly stabilized, but these models lack a mechanistic link between environmental change and species performance. To extend existing theory, we examined the relationship between coexistence and indirect effects in mechanistic resource competition models. We defined environmental change as a change in resource supply points and quantified the resulting competition-mediated indirect effects on species abundances. We found that the magnitude of indirect effects increases in proportion to niche overlap. However, indirect effects also depend on differences in how competitors respond to the change in resource supply, an insight hidden in nonmechanistic models. Our analysis demonstrates the value of using niche overlap to predict the strength of indirect effects and clarifies the types of indirect effects that global change can have on competing species.

Predicting ectotherm disease vector spread—benefits from multidisciplinary approaches and directions forward

NASA Astrophysics Data System (ADS)

Thomas, Stephanie Margarete; Beierkuhnlein, Carl

2013-05-01

The occurrence of ectotherm disease vectors outside of their previous distribution area and the emergence of vector-borne diseases can be increasingly observed at a global scale and are accompanied by a growing number of studies which investigate the vast range of determining factors and their causal links. Consequently, a broad span of scientific disciplines is involved in tackling these complex phenomena. First, we evaluate the citation behaviour of relevant scientific literature in order to clarify the question "do scientists consider results of other disciplines to extend their expertise?" We then highlight emerging tools and concepts useful for risk assessment. Correlative models (regression-based, machine-learning and profile techniques), mechanistic models (basic reproduction number R 0) and methods of spatial regression, interaction and interpolation are described. We discuss further steps towards multidisciplinary approaches regarding new tools and emerging concepts to combine existing approaches such as Bayesian geostatistical modelling, mechanistic models which avoid the need for parameter fitting, joined correlative and mechanistic models, multi-criteria decision analysis and geographic profiling. We take the quality of both occurrence data for vector, host and disease cases, and data of the predictor variables into consideration as both determine the accuracy of risk area identification. Finally, we underline the importance of multidisciplinary research approaches. Even if the establishment of communication networks between scientific disciplines and the share of specific methods is time consuming, it promises new insights for the surveillance and control of vector-borne diseases worldwide.

Reduced modeling of signal transduction – a modular approach

PubMed Central

Koschorreck, Markus; Conzelmann, Holger; Ebert, Sybille; Ederer, Michael; Gilles, Ernst Dieter

2007-01-01

Background Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. Results We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. Conclusion The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties. PMID:17854494

Is Juvenile Hormone a potential mechanism that underlay the "branched Y-model"?

PubMed

Márquez-García, Armando; Canales-Lazcano, Jorge; Rantala, Markus J; Contreras-Garduño, Jorge

2016-05-01

Trade-offs are a central tenet in the life-history evolution and the simplest model to understand it is the "Y" model: the investment of one arm will affect the investment of the other arm. However, this model is by far more complex, and a "branched Y-model" is proposed: trade-offs could exist within each arm of the Y, but the mechanistic link is unknown. Here we used Tenebrio molitor to test if Juvenile Hormone (JH) could be a mechanistic link behind the "branched Y-model". Larvae were assigned to one of the following experimental groups: (1) low, (2) medium and (3) high doses of methoprene (a Juvenile Hormone analogue, JHa), (4) acetone (methoprene diluents; control one) or (5) näive (handled in the same way as other groups; control two). The JHa lengthened the time of development from larvae to pupae and larvae to adults, resulting in adults with a larger size. Males with medium and long JHa treatment doses were favored with female choice, but had smaller testes and fewer viable sperm. There were no differences between groups in regard to the number of spermatozoa of males, or the number of ovarioles or eggs of females. This results suggest that JH: (i) is a mechanistic link of insects "branched Y model", (ii) is a double ended-sword because it may not only provide benefits on reproduction but could also impose costs, and (iii) has a differential effect on each sex, being males more affected than females. Copyright © 2016 Elsevier Inc. All rights reserved.

Mechanistic species distribution modelling as a link between physiology and conservation.

PubMed

Evans, Tyler G; Diamond, Sarah E; Kelly, Morgan W

2015-01-01

Climate change conservation planning relies heavily on correlative species distribution models that estimate future areas of occupancy based on environmental conditions encountered in present-day ranges. The approach benefits from rapid assessment of vulnerability over a large number of organisms, but can have poor predictive power when transposed to novel environments and reveals little in the way of causal mechanisms that define changes in species distribution or abundance. Having conservation planning rely largely on this single approach also increases the risk of policy failure. Mechanistic models that are parameterized with physiological information are expected to be more robust when extrapolating distributions to future environmental conditions and can identify physiological processes that set range boundaries. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance, and because this information is currently restricted to a comparatively small number of well-studied organisms, use of mechanistic modelling in the context of climate change conservation is limited. In this review, we propose that the need to develop mechanistic models that incorporate physiological data presents an opportunity for physiologists to contribute more directly to climate change conservation and advance the field of conservation physiology. We begin by describing the prevalence of species distribution modelling in climate change conservation, highlighting the benefits and drawbacks of both mechanistic and correlative approaches. Next, we emphasize the need to expand mechanistic models and discuss potential metrics of physiological performance suitable for integration into mechanistic models. We conclude by summarizing other factors, such as the need to consider demography, limiting broader application of mechanistic models in climate change conservation. Ideally, modellers, physiologists and conservation practitioners would work collaboratively to build models, interpret results and consider conservation management options, and articulating this need here may help to stimulate collaboration.

Learning to predict chemical reactions.

PubMed

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

Learning to Predict Chemical Reactions

PubMed Central

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section. PMID:21819139

Determination of the in-place hot-mix asphalt layer modulus for rehabilitation projects using a mechanistic-empirical procedure.

DOT National Transportation Integrated Search

2006-01-01

This project evaluated the procedures proposed by the Mechanistic-Empirical Pavement Design Guide (MEPDG) to characterize existing hot-mix asphalt (HMA) layers for rehabilitation purposes. Thirty-three cores were extracted from nine sites in Virginia...

Nonspecific immunomodulators for recurrent respiratory tract infections, wheezing and asthma in children: a systematic review of mechanistic and clinical evidence.

PubMed

Esposito, Susanna; Soto-Martinez, Manuel E; Feleszko, Wojciech; Jones, Marcus H; Shen, Kun-Ling; Schaad, Urs B

2018-06-01

To provide an overview of the mechanistic and clinical evidence for the use of nonspecific immunomodulators in paediatric respiratory tract infection (RTI) and wheezing/asthma prophylaxis. Nonspecific immunomodulators have a long history of empirical use for the prevention of RTIs in vulnerable populations, such as children. The past decade has seen an increase in both the number and quality of studies providing mechanistic and clinical evidence for the prophylactic potential of nonspecific immunomodulators against both respiratory infections and wheezing/asthma in the paediatric population. Orally administered immunomodulators result in the mounting of innate and adaptive immune responses to infection in the respiratory mucosa and anti-inflammatory effects in proinflammatory environments. Clinical data reflect these mechanistic effects in reductions in the recurrence of respiratory infections and wheezing events in high-risk paediatric populations. A new generation of clinical studies is currently underway with the power to position the nonspecific bacterial lysate immunomodulator OM-85 as a potential antiasthma prophylactic. An established mechanistic and clinical role for prophylaxis against paediatric respiratory infections by nonspecific immunomodulators exists. Clinical trials underway promise to provide high-quality data to establish whether a similar role exists in wheezing/asthma prevention.

Volatilization modeling of two herbicides from soil in a wind tunnel experiment under varying humidity conditions.

PubMed

Schneider, Martina; Goss, Kai-Uwe

2012-11-20

Volatilization of pesticides from the bare soil surface is drastically reduced when the soil is under dry conditions (i.e., water content lower than the permanent wilting point). This effect is caused by the hydrated mineral surfaces that become available as additional sorption sites under dry conditions. However, established volatilization models do not explicitly consider the hydrated mineral surfaces as an independent sorption compartment and cannot correctly cover the moisture effect on volatilization. Here we integrated the existing mechanistic understanding of sorption of organic compounds to mineral surfaces and its dependence on the hydration status into a simple volatilization model. The resulting model was tested with reported experimental data for two herbicides from a wind tunnel experiment under various well-defined humidity conditions. The required equilibrium sorption coefficients of triallate and trifluralin to the mineral surfaces, K(min/air), at 60% relative humidity were fitted to experimental data and extrapolated to other humidity conditions. The model captures the general trend of the volatilization in different humidity scenarios. The results reveal that it is essential to have high quality input data for K(min/air), the available specific surface area (SSA), the penetration depth of the applied pesticide solution, and the humidity conditions in the soil. The model approach presented here in combination with an improved description of the humidity conditions under dry conditions can be integrated into existing volatilization models that already work well for humid conditions but still lack the mechanistically based description of the volatilization process under dry conditions.

Toxicokinetic and Dosimetry Modeling Tools for Exposure ...

EPA Pesticide Factsheets

New technologies and in vitro testing approaches have been valuable additions to risk assessments that have historically relied solely on in vivo test results. Compared to in vivo methods, in vitro high throughput screening (HTS) assays are less expensive, faster and can provide mechanistic insights on chemical action. However, extrapolating from in vitro chemical concentrations to target tissue or blood concentrations in vivo is fraught with uncertainties, and modeling is dependent upon pharmacokinetic variables not measured in in vitro assays. To address this need, new tools have been created for characterizing, simulating, and evaluating chemical toxicokinetics. Physiologically-based pharmacokinetic (PBPK) models provide estimates of chemical exposures that produce potentially hazardous tissue concentrations, while tissue microdosimetry PK models relate whole-body chemical exposures to cell-scale concentrations. These tools rely on high-throughput in vitro measurements, and successful methods exist for pharmaceutical compounds that determine PK from limited in vitro measurements and chemical structure-derived property predictions. These high throughput (HT) methods provide a more rapid and less resource–intensive alternative to traditional PK model development. We have augmented these in vitro data with chemical structure-based descriptors and mechanistic tissue partitioning models to construct HTPBPK models for over three hundred environmental and pharmace

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

PubMed

Jones, Matt; Love, Bradley C

2011-08-01

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.

Equilibriumizing all food chain chaos through reproductive efficiency.

PubMed

Deng, Bo

2006-12-01

The intraspecific interference of a top-predator is incorporated into a classical mathematical model for three-trophic food chains. All chaos types known to the classical model are shown to exist for this comprehensive model. It is further demonstrated that if the top-predator reproduces at high efficiency, then all chaotic dynamics will change to a stable coexisting equilibrium, a novel property not found in the classical model. This finding gives a mechanistic explanation to the question of why food chain chaos is rare in the field. It also suggests that high reproductive efficiency of top-predators tends to stabilize food chains.

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-10-22

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of ReactionPredictor are available via the chemoinformatics portal http://cdb.ics.uci.edu/.

Putting the psychology back into psychological models: mechanistic versus rational approaches.

PubMed

Sakamoto, Yasuaki; Jones, Mattr; Love, Bradley C

2008-09-01

Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each category. The model yields a prediction, which we verify, regarding the effects of order manipulations that the rational approach does not anticipate. Although both rational and mechanistic models can successfully postdict known findings, we suggest that psychological advances are driven primarily by consideration of process and representation and that rational accounts trail these breakthroughs.

Assessing uncertainty in mechanistic models

Treesearch

Edwin J. Green; David W. MacFarlane; Harry T. Valentine

2000-01-01

Concern over potential global change has led to increased interest in the use of mechanistic models for predicting forest growth. The rationale for this interest is that empirical models may be of limited usefulness if environmental conditions change. Intuitively, we expect that mechanistic models, grounded as far as possible in an understanding of the biology of tree...

P. falciparum Infection Durations and Infectiousness Are Shaped by Antigenic Variation and Innate and Adaptive Host Immunity in a Mathematical Model

PubMed Central

Eckhoff, Philip

2012-01-01

Many questions remain about P. falciparum within-host dynamics, immunity, and transmission–issues that may affect public health campaign planning. These gaps in knowledge concern the distribution of durations of malaria infections, determination of peak parasitemia during acute infection, the relationships among gametocytes and immune responses and infectiousness to mosquitoes, and the effect of antigenic structure on reinfection outcomes. The present model of intra-host dynamics of P. falciparum implements detailed representations of parasite and immune dynamics, with structures based on minimal extrapolations from first-principles biology in its foundations. The model is designed to quickly and readily accommodate gains in mechanistic understanding and to evaluate effects of alternative biological hypothesis through in silico experiments. Simulations follow the parasite from the liver-stage through the detailed asexual cycle to clearance while tracking gametocyte populations. The modeled immune system includes innate inflammatory and specific antibody responses to a repertoire of antigens. The mechanistic focus provides clear explanations for the structure of the distribution of infection durations through the interaction of antigenic variation and innate and adaptive immunity. Infectiousness to mosquitoes appears to be determined not only by the density of gametocytes but also by the level of inflammatory cytokines, which harmonizes an extensive series of study results. Finally, pre-existing immunity can either decrease or increase the duration of infections upon reinfection, depending on the degree of overlap in antigenic repertoires and the strength of the pre-existing immunity. PMID:23028698

DOUBLE SHELL TANK (DST) HYDROXIDE DEPLETION MODEL FOR CARBON DIOXIDE ABSORPTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

OGDEN DM; KIRCH NW

2007-10-31

This document generates a supernatant hydroxide ion depletion model based on mechanistic principles. The carbon dioxide absorption mechanistic model is developed in this report. The report also benchmarks the model against historical tank supernatant hydroxide data and vapor space carbon dioxide data. A comparison of the newly generated mechanistic model with previously applied empirical hydroxide depletion equations is also performed.

Physiologically induced color-pattern changes in butterfly wings: mechanistic and evolutionary implications.

PubMed

Otaki, Joji M

2008-07-01

A mechanistic understanding of the butterfly wing color-pattern determination can be facilitated by experimental pattern changes. Here I review physiologically induced color-pattern changes in nymphalid butterflies and their mechanistic and evolutionary implications. A type of color-pattern change can be elicited by elemental changes in size and position throughout the wing, as suggested by the nymphalid groundplan. These changes of pattern elements are bi-directional and bi-sided dislocation toward or away from eyespot foci and in both proximal and distal sides of the foci. The peripheral elements are dislocated even in the eyespot-less compartments. Anterior spots are more severely modified, suggesting the existence of an anterior-posterior gradient. In one species, eyespots are transformed into white spots with remnant-like orange scales, and such patterns emerge even at the eyespot-less "imaginary" foci. A series of these color-pattern modifications probably reveal "snap-shots" of a dynamic morphogenic signal due to heterochronic uncoupling between the signaling and reception steps. The conventional gradient model can be revised to account for these observed color-pattern changes.

Four decades of modeling methane cycling in terrestrial ecosystems: Where we are heading?

NASA Astrophysics Data System (ADS)

Xu, X.; Yuan, F.; Hanson, P. J.; Wullschleger, S. D.; Thornton, P. E.; Tian, H.; Riley, W. J.; Song, X.; Graham, D. E.; Song, C.

2015-12-01

A modeling approach to methane (CH4) is widely used to quantify the budget, investigate spatial and temporal variabilities, and understand the mechanistic processes and environmental controls on CH4 fluxes across spatial and temporal scales. Moreover, CH4 models are an important tool for integrating CH4 data from multiple sources, such as laboratory-based incubation and molecular analysis, field observational experiments, remote sensing, and aircraft-based measurements across a variety of terrestrial ecosystems. We reviewed 39 terrestrial CH4 models to characterize their strengths and weaknesses and to design a roadmap for future model improvement and application. We found that: (1) the focus of CH4 models have been shifted from theoretical to site- to regional-level application over the past four decades, expressed as dramatic increases in CH4 model development on regional budget quantification; (2) large discrepancies exist among models in terms of representing CH4 processes and their environmental controls; (3) significant data-model and model-model mismatches are partially attributed to different representations of wetland characterization and inundation dynamics. Three efforts should be paid special attention for future improvements and applications of fully mechanistic CH4 models: (1) CH4 models should be improved to represent the mechanisms underlying land-atmosphere CH4 exchange, with emphasis on improving and validating individual CH4 processes over depth and horizontal space; (2) models should be developed that are capable of simulating CH4 fluxes across space and time (particularly hot moments and hot spots); (3) efforts should be invested to develop model benchmarking frameworks that can easily be used for model improvement, evaluation, and integration with data from molecular to global scales. A newly developed microbial functional group-based CH4 model (CLM-Microbe) was further used to demonstrate the features of mechanistic representation and integration with multiple source of observational datasets.

Toward a Mechanistic Understanding of Anaerobic Nitrate-Dependent Iron Oxidation: Balancing Electron Uptake and Detoxification

PubMed Central

Carlson, Hans K.; Clark, Iain C.; Melnyk, Ryan A.; Coates, John D.

2011-01-01

The anaerobic oxidation of Fe(II) by subsurface microorganisms is an important part of biogeochemical cycling in the environment, but the biochemical mechanisms used to couple iron oxidation to nitrate respiration are not well understood. Based on our own work and the evidence available in the literature, we propose a mechanistic model for anaerobic nitrate-dependent iron oxidation. We suggest that anaerobic iron-oxidizing microorganisms likely exist along a continuum including: (1) bacteria that inadvertently oxidize Fe(II) by abiotic or biotic reactions with enzymes or chemical intermediates in their metabolic pathways (e.g., denitrification) and suffer from toxicity or energetic penalty, (2) Fe(II) tolerant bacteria that gain little or no growth benefit from iron oxidation but can manage the toxic reactions, and (3) bacteria that efficiently accept electrons from Fe(II) to gain a growth advantage while preventing or mitigating the toxic reactions. Predictions of the proposed model are highlighted and experimental approaches are discussed. PMID:22363331

Knowledge-based vision and simple visual machines.

PubMed Central

Cliff, D; Noble, J

1997-01-01

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong. PMID:9304684

Sulfation in lead-acid batteries

NASA Astrophysics Data System (ADS)

Catherino, Henry A.; Feres, Fred F.; Trinidad, Francisco

Virtually, all military land vehicle systems use a lead-acid battery to initiate an engine start. The maintainability of these batteries and as a consequence, system readiness, has suffered from a lack of understanding of the reasons for battery failure. Often, the term most commonly heard for explaining the performance degradation of lead-acid batteries is the word, sulfation. Sulfation is a residual term that came into existence during the early days of lead-acid battery development. The usage is part of the legend that persists as a means for interpreting and justifying the eventual performance deterioration and failure of lead-acid batteries. The usage of this term is confined to the greater user community and, over time, has encouraged a myriad of remedies for solving sulfation problems. One can avoid the connotations associated with the all-inclusive word, sulfation by visualizing the general "sulfation" effect in terms of specific mechanistic models. Also, the mechanistic models are essential for properly understanding the operation and making proper use this battery system. It is evident that the better the model, the better the level of understanding.

Realistic neurons can compute the operations needed by quantum probability theory and other vector symbolic architectures.

PubMed

Stewart, Terrence C; Eliasmith, Chris

2013-06-01

Quantum probability (QP) theory can be seen as a type of vector symbolic architecture (VSA): mental states are vectors storing structured information and manipulated using algebraic operations. Furthermore, the operations needed by QP match those in other VSAs. This allows existing biologically realistic neural models to be adapted to provide a mechanistic explanation of the cognitive phenomena described in the target article by Pothos & Busemeyer (P&B).

Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation: Report of an FDA Public Workshop

PubMed Central

Duan, J; Kesisoglou, F; Novakovic, J; Amidon, GL; Jamei, M; Lukacova, V; Eissing, T; Tsakalozou, E; Zhao, L; Lionberger, R

2017-01-01

On May 19, 2016, the US Food and Drug Administration (FDA) hosted a public workshop, entitled “Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation.”1 The topic of mechanistic oral absorption modeling, which is one of the major applications of physiologically based pharmacokinetic (PBPK) modeling and simulation, focuses on predicting oral absorption by mechanistically integrating gastrointestinal transit, dissolution, and permeation processes, incorporating systems, active pharmaceutical ingredient (API), and the drug product information, into a systemic mathematical whole‐body framework.2 PMID:28571121

Fluid mechanics in dentinal microtubules provides mechanistic insights into the difference between hot and cold dental pain.

PubMed

Lin, Min; Luo, Zheng Yuan; Bai, Bo Feng; Xu, Feng; Lu, Tian Jian

2011-03-23

Dental thermal pain is a significant health problem in daily life and dentistry. There is a long-standing question regarding the phenomenon that cold stimulation evokes sharper and more shooting pain sensations than hot stimulation. This phenomenon, however, outlives the well-known hydrodynamic theory used to explain dental thermal pain mechanism. Here, we present a mathematical model based on the hypothesis that hot or cold stimulation-induced different directions of dentinal fluid flow and the corresponding odontoblast movements in dentinal microtubules contribute to different dental pain responses. We coupled a computational fluid dynamics model, describing the fluid mechanics in dentinal microtubules, with a modified Hodgkin-Huxley model, describing the discharge behavior of intradental neuron. The simulated results agreed well with existing experimental measurements. We thence demonstrated theoretically that intradental mechano-sensitive nociceptors are not "equally sensitive" to inward (into the pulp) and outward (away from the pulp) fluid flows, providing mechanistic insights into the difference between hot and cold dental pain. The model developed here could enable better diagnosis in endodontics which requires an understanding of pulpal histology, neurology and physiology, as well as their dynamic response to the thermal stimulation used in dental practices.

Fluid Mechanics in Dentinal Microtubules Provides Mechanistic Insights into the Difference between Hot and Cold Dental Pain

PubMed Central

Lin, Min; Luo, Zheng Yuan; Bai, Bo Feng; Xu, Feng; Lu, Tian Jian

2011-01-01

Dental thermal pain is a significant health problem in daily life and dentistry. There is a long-standing question regarding the phenomenon that cold stimulation evokes sharper and more shooting pain sensations than hot stimulation. This phenomenon, however, outlives the well-known hydrodynamic theory used to explain dental thermal pain mechanism. Here, we present a mathematical model based on the hypothesis that hot or cold stimulation-induced different directions of dentinal fluid flow and the corresponding odontoblast movements in dentinal microtubules contribute to different dental pain responses. We coupled a computational fluid dynamics model, describing the fluid mechanics in dentinal microtubules, with a modified Hodgkin-Huxley model, describing the discharge behavior of intradental neuron. The simulated results agreed well with existing experimental measurements. We thence demonstrated theoretically that intradental mechano-sensitive nociceptors are not “equally sensitive” to inward (into the pulp) and outward (away from the pulp) fluid flows, providing mechanistic insights into the difference between hot and cold dental pain. The model developed here could enable better diagnosis in endodontics which requires an understanding of pulpal histology, neurology and physiology, as well as their dynamic response to the thermal stimulation used in dental practices. PMID:21448459

Flood forecasting using non-stationarity in a river with tidal influence - a feasibility study

NASA Astrophysics Data System (ADS)

Killick, Rebecca; Kretzschmar, Ann; Ilic, Suzi; Tych, Wlodek

2017-04-01

Flooding is the most common natural hazard causing damage, disruption and loss of life worldwide. Despite improvements in modelling and forecasting of water levels and flood inundation (Kretzschmar et al., 2014; Hoitink and Jay, 2016), there are still large discrepancies between predictions and observations particularly during storm events when accurate predictions are most important. Many models exist for forecasting river levels (Smith et al., 2013; Leedal et al., 2013) however they commonly assume that the errors in the data are independent, stationary and normally distributed. This is generally not the case especially during storm events suggesting that existing models are not describing the drivers of river level in an appropriate fashion. Further challenges exist in the lower sections of a river influenced by both river and tidal flows and their interaction and there is scope for improvement in prediction. This paper investigates the use of a powerful statistical technique to adaptively forecast river levels by modelling the process as locally stationary. The proposed methodology takes information on both upstream and downstream river levels and incorporates meteorological information (rainfall forecasts) and tidal levels when required to forecast river levels at a specified location. Using this approach, a single model will be capable of predicting water levels in both tidal and non-tidal river reaches. In this pilot project, the methodology of Smith et al. (2013) using harmonic tidal analysis and data based mechanistic modelling is compared with the methodology developed by Killick et al. (2016) utilising data-driven wavelet decomposition to account for the information contained in the upstream and downstream river data to forecast a non-stationary time-series. Preliminary modelling has been carried out using the tidal stretch of the River Lune in North-west England and initial results are presented here. Future work includes expanding the methodology to forecast river levels at a network of locations simultaneously. References Hoitink, A. J. F., and D. A. Jay (2016), Tidal river dynamics: Implications for deltas, Rev. Geophys., 54, 240-272 Killick, R., Knight, M., Nason, G.P., Eckley, I.A. (2016) The Local Partial Autocorrelation Function and its Application to the Forecasting of Locally Stationary Time Series. Submitted Kretzschmar, Ann and Tych, Wlodek and Chappell, Nick A (2014) Reversing hydrology: estimation of sub-hourly rainfall time-series from streamflow. Env. Modell Softw., 60. pp. 290-301 D. Leedal, A. H. Weerts, P. J. Smith, & K. J. Beven. (2013). Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales). HESS, 17(1), 177-185. Smith, P., Beven, K., Horsburgh, K., Hardaker, P., & Collier, C. (2013). Data-based mechanistic modelling of tidally affected river reaches for flood warning purposes: An example on the River Dee, UK. , Q.J.R. Meteorol. Soc. 139(671), 340-349.

A white-box model of S-shaped and double S-shaped single-species population growth

PubMed Central

Kalmykov, Lev V.

2015-01-01

Complex systems may be mechanistically modelled by white-box modeling with using logical deterministic individual-based cellular automata. Mathematical models of complex systems are of three types: black-box (phenomenological), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models). Most basic ecological models are of black-box type, including Malthusian, Verhulst, Lotka–Volterra models. In black-box models, the individual-based (mechanistic) mechanisms of population dynamics remain hidden. Here we mechanistically model the S-shaped and double S-shaped population growth of vegetatively propagated rhizomatous lawn grasses. Using purely logical deterministic individual-based cellular automata we create a white-box model. From a general physical standpoint, the vegetative propagation of plants is an analogue of excitation propagation in excitable media. Using the Monte Carlo method, we investigate a role of different initial positioning of an individual in the habitat. We have investigated mechanisms of the single-species population growth limited by habitat size, intraspecific competition, regeneration time and fecundity of individuals in two types of boundary conditions and at two types of fecundity. Besides that, we have compared the S-shaped and J-shaped population growth. We consider this white-box modeling approach as a method of artificial intelligence which works as automatic hyper-logical inference from the first principles of the studied subject. This approach is perspective for direct mechanistic insights into nature of any complex systems. PMID:26038717

Biochar: from laboratory mechanisms through the greenhouse to field trials

NASA Astrophysics Data System (ADS)

Masiello, C. A.; Gao, X.; Dugan, B.; Silberg, J. J.; Zygourakis, K.; Alvarez, P. J. J.

2014-12-01

The biochar community is excellent at pointing to individual cases where biochar amendment has changed soil properties, with some studies showing significant improvements in crop yields, reduction in nutrient export, and remediation of pollutants. However, many studies exist which do not show improvements, and in some cases, studies clearly show detrimental outcomes. The next, crucial step in biochar science and engineering research will be to develop a process-based understanding of how biochar acts to improve soil properties. In particular, we need a better mechanistic understanding of how biochar sorbs and desorbs contaminants, how it interacts with soil water, and how it interacts with the soil microbial community. These mechanistic studies need to encompass processes that range from the nanometer to the kilometer scale. At the nanometer scale, we need a predictive model of how biochar will sorb and desorb hydrocarbons, nutrients, and toxic metals. At the micrometer scale we need models that explain biochar's effects on soil water, especially the plant-available fraction of soil water. The micrometer scale is also where mechanistic information is neeed about microbial processes. At the macroscale we need physical models to describe the landscape mobility of biochar, because biochar that washes away from fields can no longer provide crop benefits. To be most informative, biochar research should occur along a lab-greenhouse-field trial trajectory. Laboratory experiments should aim determine what mechanisms may act to control biochar-soil processes, and then greenhouse experiments can be used to test the significance of lab-derived mechanisms in short, highly replicated, controlled experiments. Once evidence of effect is determined from greenhouse experiments, field trials are merited. Field trials are the gold standard needed prior to full deployment, but results from field trials cannot be extrapolated to other field sites without the mechanistic backup provided by greenhouse and lab trials.

Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation: Report of an FDA Public Workshop.

PubMed

Zhang, X; Duan, J; Kesisoglou, F; Novakovic, J; Amidon, G L; Jamei, M; Lukacova, V; Eissing, T; Tsakalozou, E; Zhao, L; Lionberger, R

2017-08-01

On May 19, 2016, the US Food and Drug Administration (FDA) hosted a public workshop, entitled "Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation." The topic of mechanistic oral absorption modeling, which is one of the major applications of physiologically based pharmacokinetic (PBPK) modeling and simulation, focuses on predicting oral absorption by mechanistically integrating gastrointestinal transit, dissolution, and permeation processes, incorporating systems, active pharmaceutical ingredient (API), and the drug product information, into a systemic mathematical whole-body framework. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Mechanistic investigation of the formation of H2 from HCOOH with a dinuclear Ru model complex for formate hydrogen lyase.

PubMed

Tokunaga, Taisuke; Yatabe, Takeshi; Matsumoto, Takahiro; Ando, Tatsuya; Yoon, Ki-Seok; Ogo, Seiji

2017-01-01

We report the mechanistic investigation of catalytic H 2 evolution from formic acid in water using a formate-bridged dinuclear Ru complex as a formate hydrogen lyase model. The mechanistic study is based on isotope-labeling experiments involving hydrogen isotope exchange reaction.

Native State Volume Fluctuations in Proteins as a Mechanism for Dynamic Allostery.

PubMed

Law, Anthony B; Sapienza, Paul J; Zhang, Jun; Zuo, Xiaobing; Petit, Chad M

2017-03-15

Allostery enables tight regulation of protein function in the cellular environment. Although existing models of allostery are firmly rooted in the current structure-function paradigm, the mechanistic basis for allostery in the absence of structural change remains unclear. In this study, we show that a typical globular protein is able to undergo significant changes in volume under native conditions while exhibiting no additional changes in protein structure. These native state volume fluctuations were found to correlate with changes in internal motions that were previously recognized as a source of allosteric entropy. This finding offers a novel mechanistic basis for allostery in the absence of canonical structural change. The unexpected observation that function can be derived from expanded, low density protein states has broad implications for our understanding of allostery and suggests that the general concept of the native state be expanded to allow for more variable physical dimensions with looser packing.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Law, Anthony B.; Sapienza, Paul J.; Zhang, Jun

Allostery enables tight regulation of protein function in the cellular environment. While existing models of allostery are firmly rooted in the current structure-function paradigm, the mechanistic basis for allostery in the absence of structural change remains unclear. In this study, we show that a typical globular protein is able to undergo significant changes in volume under native conditions while exhibiting no additional changes in protein structure. These native state volume fluctuations were found to correlate with changes in internal motions that were previously recognized as a source of allosteric entropy. This finding offers a novel mechanistic basis for allostery inmore » the absence of canonical structural change. As a result, the unexpected observation that function can be derived from expanded, low density protein states has broad implications for our understanding of allostery and suggests that the general concept of the native state be expanded to allow for more variable physical dimensions with looser packing.« less

Mechanistic modeling of insecticide risks to breeding birds in ...

EPA Pesticide Factsheets

Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. At the present time, current USEPA risk assessments do not include population-level endpoints. In this paper, we present a new mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to use agricultural fields during their breeding season. The new model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model has been applied to assess the relative risk of 12 insecticides used to control corn pests on a suite of 31 avian species known to use cornfields in midwestern agroecosystems. The 12 insecticides that were assessed in this study are all used to treat major pests of corn (corn root worm borer, cutworm, and armyworm). After running the integrated TIM/MCnest model, we found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and ë-cyhalothrin (

Development of sustainable precision farming systems for swine: estimating real-time individual amino acid requirements in growing-finishing pigs.

PubMed

Hauschild, L; Lovatto, P A; Pomar, J; Pomar, C

2012-07-01

The objective of this study was to develop and evaluate a mathematical model used to estimate the daily amino acid requirements of individual growing-finishing pigs. The model includes empirical and mechanistic model components. The empirical component estimates daily feed intake (DFI), BW, and daily gain (DG) based on individual pig information collected in real time. Based on DFI, BW, and DG estimates, the mechanistic component uses classic factorial equations to estimate the optimal concentration of amino acids that must be offered to each pig to meet its requirements. The model was evaluated with data from a study that investigated the effect of feeding pigs with a 3-phase or daily multiphase system. The DFI and BW values measured in this study were compared with those estimated by the empirical component of the model. The coherence of the values estimated by the mechanistic component was evaluated by analyzing if it followed a normal pattern of requirements. Lastly, the proposed model was evaluated by comparing its estimates with those generated by the existing growth model (InraPorc). The precision of the proposed model and InraPorc in estimating DFI and BW was evaluated through the mean absolute error. The empirical component results indicated that the DFI and BW trajectories of individual pigs fed ad libitum could be predicted 1 d (DFI) or 7 d (BW) ahead with the average mean absolute error of 12.45 and 1.85%, respectively. The average mean absolute error obtained with the InraPorc for the average individual of the population was 14.72% for DFI and 5.38% for BW. Major differences were observed when estimates from InraPorc were compared with individual observations. The proposed model, however, was effective in tracking the change in DFI and BW for each individual pig. The mechanistic model component estimated the optimal standardized ileal digestible Lys to NE ratio with reasonable between animal (average CV = 7%) and overtime (average CV = 14%) variation. Thus, the amino acid requirements estimated by model are animal- and time-dependent and follow, in real time, the individual DFI and BW growth patterns. The proposed model can follow the average feed intake and feed weight trajectory of each individual pig in real time with good accuracy. Based on these trajectories and using classical factorial equations, the model makes it possible to estimate dynamically the AA requirements of each animal, taking into account the intake and growth changes of the animal.

Mechanistic modeling of destratification in cryogenic storage tanks using ultrasonics.

PubMed

Jagannathan, T K; Mohanan, Srijith; Nagarajan, R

2014-01-01

Stratification is one of the main causes for vaporization of cryogens and increase of tank pressure during cryogenic storage. This leads subsequent problems such as cavitation in cryo-pumps, reduced length of storage time. Hence, it is vital to prevent stratification to improve the cost efficiency of storage systems. If stratified layers exist inside the tank, they have to be removed by suitable methods without venting the vapor. Sonication is one such method capable of keeping fluid layers mixed. In the present work, a mechanistic model for ultrasonic destratification is proposed and validated with destratification experiments done in water. Then, the same model is used to predict the destratification characteristics of cryogenic liquids such as liquid nitrogen (LN₂), liquid hydrogen (LH₂) and liquid ammonia (LNH₃). The destratification parameters are analysed for different frequencies of ultrasound and storage pressures by considering continuous and pulsed modes of ultrasonic operation. From the results, it is determined that use of high frequency ultrasound (low-power/continuous; high-power/pulsing) or low frequency ultrasound (continuous operation with moderate power) can both be effective in removing stratification. Copyright © 2013 Elsevier B.V. All rights reserved.

Synthesis and Photolysis of Model Compounds for Mechanistic Studies of 1,3,5-Trinitro-1,3,5-Triazacyclohexane (RDX) Decomposition.

DTIC Science & Technology

1978-06-01

However, once irradiation was stopped the radical disappeared within a half hour . When the temperature was Increased to 40 °C the radical existed...and Russat proposed that the mechanism is bimolecular with the products OK - . ‘ being the nitrone and hydroxy l amine. Experimenta l support for this...process was provided by K. U. Ingold 15 . Kaminsky and Lamchen 16 and ethers have shown that the photolysis of cyclic nitrones gives oxi- ziridines

Testing mechanistic models of growth in insects.

PubMed

Maino, James L; Kearney, Michael R

2015-11-22

Insects are typified by their small size, large numbers, impressive reproductive output and rapid growth. However, insect growth is not simply rapid; rather, insects follow a qualitatively distinct trajectory to many other animals. Here we present a mechanistic growth model for insects and show that increasing specific assimilation during the growth phase can explain the near-exponential growth trajectory of insects. The presented model is tested against growth data on 50 insects, and compared against other mechanistic growth models. Unlike the other mechanistic models, our growth model predicts energy reserves per biomass to increase with age, which implies a higher production efficiency and energy density of biomass in later instars. These predictions are tested against data compiled from the literature whereby it is confirmed that insects increase their production efficiency (by 24 percentage points) and energy density (by 4 J mg(-1)) between hatching and the attainment of full size. The model suggests that insects achieve greater production efficiencies and enhanced growth rates by increasing specific assimilation and increasing energy reserves per biomass, which are less costly to maintain than structural biomass. Our findings illustrate how the explanatory and predictive power of mechanistic growth models comes from their grounding in underlying biological processes. © 2015 The Author(s).

Multiscale Constitutive Modeling of Asphalt Concrete

NASA Astrophysics Data System (ADS)

Underwood, Benjamin Shane

Multiscale modeling of asphalt concrete has become a popular technique for gaining improved insight into the physical mechanisms that affect the material's behavior and ultimately its performance. This type of modeling considers asphalt concrete, not as a homogeneous mass, but rather as an assemblage of materials at different characteristic length scales. For proper modeling these characteristic scales should be functionally definable and should have known properties. Thus far, research in this area has not focused significant attention on functionally defining what the characteristic scales within asphalt concrete should be. Instead, many have made assumptions on the characteristic scales and even the characteristic behaviors of these scales with little to no support. This research addresses these shortcomings by directly evaluating the microstructure of the material and uses these results to create materials of different characteristic length scales as they exist within the asphalt concrete mixture. The objectives of this work are to; 1) develop mechanistic models for the linear viscoelastic (LVE) and damage behaviors in asphalt concrete at different length scales and 2) develop a mechanistic, mechanistic/empirical, or phenomenological formulation to link the different length scales into a model capable of predicting the effects of microstructural changes on the linear viscoelastic behaviors of asphalt concrete mixture, e.g., a microstructure association model for asphalt concrete mixture. Through the microstructural study it is found that asphalt concrete mixture can be considered as a build-up of three different phases; asphalt mastic, fine aggregate matrix (FAM), and finally the coarse aggregate particles. The asphalt mastic is found to exist as a homogenous material throughout the mixture and FAM, and the filler content within this material is consistent with the volumetric averaged concentration, which can be calculated from the job mix formula. It is also found that the maximum aggregate size of the FAM is mixture dependent, but consistent with a gradation parameter from the Baily Method of mixture design. Mechanistic modeling of these different length scales reveals that although many consider asphalt concrete to be a LVE material, it is in fact only quasi-LVE because it shows some tendencies that are inconsistent with LVE theory. Asphalt FAM and asphalt mastic show similar nonlinear tendencies although the exact magnitude of the effect differs. These tendencies can be ignored for damage modeling in the mixture and FAM scales as long as the effects are consistently ignored, but it is found that they must be accounted for in mastic and binder damage modeling. The viscoelastic continuum damage (VECD) model is used for damage modeling in this research. To aid in characterization and application of the VECD model for cyclic testing, a simplified version (S-VECD) is rigorously derived and verified. Through the modeling efforts at each scale, various factors affecting the fundamental and engineering properties at each scale are observed and documented. A microstructure association model that accounts for particle interaction through physico-chemical processes and the effects of aggregate structuralization is developed to links the moduli at each scale. This model is shown to be capable of upscaling the mixture modulus from either the experimentally determined mastic modulus or FAM modulus. Finally, an initial attempt at upscaling the damage and nonlinearity phenomenon is shown.

The Perfect Storm: Preterm Birth, Neurodevelopmental Mechanisms, and Autism Causation.

PubMed

Erdei, Carmina; Dammann, Olaf

2014-01-01

A unifying model of autism causation remains elusive, and thus well-designed explanatory models are needed to develop appropriate therapeutic and preventive interventions. This essay argues that autism is not a static disorder, but rather an ongoing process. We discuss the link between preterm birth and autism and briefly review the evidence supporting the link between immune system characteristics and both prematurity and autism. We then propose a causation process model of autism etiology and pathogenesis, in which both neurodevelopment and ongoing/prolonged neuroinflammation are necessary pathogenetic component mechanisms. We suggest that an existing model of sufficient cause and component causes can be interpreted as a mechanistic view of etiology and pathogenesis and can serve as an explanatory model for autism causal pathways.

A mechanistic investigation of the oxygen fixation hypothesis and oxygen enhancement ratio.

PubMed

Grimes, David Robert; Partridge, Mike

2015-12-04

The presence of oxygen in tumours has substantial impact on treatment outcome; relative to anoxic regions, well-oxygenated cells respond better to radiotherapy by a factor 2.5-3. This increased radio-response is known as the oxygen enhancement ratio. The oxygen effect is most commonly explained by the oxygen fixation hypothesis, which postulates that radical-induced DNA damage can be permanently 'fixed' by molecular oxygen, rendering DNA damage irreparable. While this oxygen effect is important in both existing therapy and for future modalities such a radiation dose-painting, the majority of existing mathematical models for oxygen enhancement are empirical rather than based on the underlying physics and radiochemistry. Here we propose a model of oxygen-enhanced damage from physical first principles, investigating factors that might influence the cell kill. This is fitted to a range of experimental oxygen curves from literature and shown to describe them well, yielding a single robust term for oxygen interaction obtained. The model also reveals a small thermal dependency exists but that this is unlikely to be exploitable.

Bridging paradigms: hybrid mechanistic-discriminative predictive models.

PubMed

Doyle, Orla M; Tsaneva-Atansaova, Krasimira; Harte, James; Tiffin, Paul A; Tino, Peter; Díaz-Zuccarini, Vanessa

2013-03-01

Many disease processes are extremely complex and characterized by multiple stochastic processes interacting simultaneously. Current analytical approaches have included mechanistic models and machine learning (ML), which are often treated as orthogonal viewpoints. However, to facilitate truly personalized medicine, new perspectives may be required. This paper reviews the use of both mechanistic models and ML in healthcare as well as emerging hybrid methods, which are an exciting and promising approach for biologically based, yet data-driven advanced intelligent systems.

Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

PubMed Central

Ye, Hao; Beamish, Richard J.; Glaser, Sarah M.; Grant, Sue C. H.; Hsieh, Chih-hao; Richards, Laura J.; Schnute, Jon T.; Sugihara, George

2015-01-01

It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts. PMID:25733874

Computational modeling of neurostimulation in brain diseases.

PubMed

Wang, Yujiang; Hutchings, Frances; Kaiser, Marcus

2015-01-01

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation. © 2015 Elsevier B.V. All rights reserved.

MECHANISTIC DOSIMETRY MODELS OF NANOMATERIAL DEPOSITION IN THE RESPIRATORY TRACT

EPA Science Inventory

Accurate health risk assessments of inhalation exposure to nanomaterials will require dosimetry models that account for interspecies differences in dose delivered to the respiratory tract. Mechanistic models offer the advantage to interspecies extrapolation that physicochemica...

Combining correlative and mechanistic habitat suitability models to improve ecological compensation.

PubMed

Meineri, Eric; Deville, Anne-Sophie; Grémillet, David; Gauthier-Clerc, Michel; Béchet, Arnaud

2015-02-01

Only a few studies have shown positive impacts of ecological compensation on species dynamics affected by human activities. We argue that this is due to inappropriate methods used to forecast required compensation in environmental impact assessments. These assessments are mostly descriptive and only valid at limited spatial and temporal scales. However, habitat suitability models developed to predict the impacts of environmental changes on potential species' distributions should provide rigorous science-based tools for compensation planning. Here we describe the two main classes of predictive models: correlative models and individual-based mechanistic models. We show how these models can be used alone or synoptically to improve compensation planning. While correlative models are easier to implement, they tend to ignore underlying ecological processes and lack accuracy. On the contrary, individual-based mechanistic models can integrate biological interactions, dispersal ability and adaptation. Moreover, among mechanistic models, those considering animal energy balance are particularly efficient at predicting the impact of foraging habitat loss. However, mechanistic models require more field data compared to correlative models. Hence we present two approaches which combine both methods for compensation planning, especially in relation to the spatial scale considered. We show how the availability of biological databases and software enabling fast and accurate population projections could be advantageously used to assess ecological compensation requirement efficiently in environmental impact assessments. © 2014 The Authors. Biological Reviews © 2014 Cambridge Philosophical Society.

Effects of septum and pericardium on heart-lung interactions in a cardiopulmonary simulation model.

PubMed

Karamolegkos, Nikolaos; Albanese, Antonio; Chbat, Nicolas W

2017-07-01

Mechanical heart-lung interactions are often overlooked in clinical settings. However, their impact on cardiac function can be quite significant. Mechanistic physiology-based models can provide invaluable insights into such cardiorespiratory interactions, which occur not only under external mechanical ventilatory support but in normal physiology as well. In this work, we focus on the cardiac component of a previously developed mathematical model of the human cardiopulmonary system, aiming to improve the model's response to the intrathoracic pressure variations that are associated with the respiratory cycle. Interventricular septum and pericardial membrane are integrated into the existing model. Their effect on the overall cardiac response is explained by means of comparison against simulation results from the original model as well as experimental data from literature.

MECHANISTIC-BASED DISINFECTION AND DISINFECTION BYPRODUCT MODELS

EPA Science Inventory

We propose developing a mechanistic-based numerical model for chlorine decay and regulated DBP (THM and HAA) formation derived from (free) chlorination; the model framework will allow future modifications for other DBPs and chloramination. Predicted chlorine residual and DBP r...

Systems, methods and computer-readable media for modeling cell performance fade of rechargeable electrochemical devices

DOEpatents

Gering, Kevin L

2013-08-27

A system includes an electrochemical cell, monitoring hardware, and a computing system. The monitoring hardware periodically samples performance characteristics of the electrochemical cell. The computing system determines cell information from the performance characteristics of the electrochemical cell. The computing system also develops a mechanistic level model of the electrochemical cell to determine performance fade characteristics of the electrochemical cell and analyzing the mechanistic level model to estimate performance fade characteristics over aging of a similar electrochemical cell. The mechanistic level model uses first constant-current pulses applied to the electrochemical cell at a first aging period and at three or more current values bracketing a first exchange current density. The mechanistic level model also is based on second constant-current pulses applied to the electrochemical cell at a second aging period and at three or more current values bracketing the second exchange current density.

The use of mechanistic descriptions of algal growth and zooplankton grazing in an estuarine eutrophication model

NASA Astrophysics Data System (ADS)

Baird, M. E.; Walker, S. J.; Wallace, B. B.; Webster, I. T.; Parslow, J. S.

2003-03-01

A simple model of estuarine eutrophication is built on biomechanical (or mechanistic) descriptions of a number of the key ecological processes in estuaries. Mechanistically described processes include the nutrient uptake and light capture of planktonic and benthic autotrophs, and the encounter rates of planktonic predators and prey. Other more complex processes, such as sediment biogeochemistry, detrital processes and phosphate dynamics, are modelled using empirical descriptions from the Port Phillip Bay Environmental Study (PPBES) ecological model. A comparison is made between the mechanistically determined rates of ecological processes and the analogous empirically determined rates in the PPBES ecological model. The rates generally agree, with a few significant exceptions. Model simulations were run at a range of estuarine depths and nutrient loads, with outputs presented as the annually averaged biomass of autotrophs. The simulations followed a simple conceptual model of eutrophication, suggesting a simple biomechanical understanding of estuarine processes can provide a predictive tool for ecological processes in a wide range of estuarine ecosystems.

A comprehensive mechanistic model for upward two-phase flow in wellbores

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sylvester, N.D.; Sarica, C.; Shoham, O.

1994-05-01

A comprehensive model is formulated to predict the flow behavior for upward two-phase flow. This model is composed of a model for flow-pattern prediction and a set of independent mechanistic models for predicting such flow characteristics as holdup and pressure drop in bubble, slug, and annular flow. The comprehensive model is evaluated by using a well data bank made up of 1,712 well cases covering a wide variety of field data. Model performance is also compared with six commonly used empirical correlations and the Hasan-Kabir mechanistic model. Overall model performance is in good agreement with the data. In comparison withmore » other methods, the comprehensive model performed the best.« less

Mechanism for multiplicity of steady states with distinct cell concentration in continuous culture of mammalian cells.

PubMed

Yongky, Andrew; Lee, Jongchan; Le, Tung; Mulukutla, Bhanu Chandra; Daoutidis, Prodromos; Hu, Wei-Shou

2015-07-01

Continuous culture for the production of biopharmaceutical proteins offers the possibility of steady state operations and thus more consistent product quality and increased productivity. Under some conditions, multiplicity of steady states has been observed in continuous cultures of mammalian cells, wherein with the same dilution rate and feed nutrient composition, steady states with very different cell and product concentrations may be reached. At those different steady states, cells may exhibit a high glycolysis flux with high lactate production and low cell concentration, or a low glycolysis flux with low lactate and high cell concentration. These different steady states, with different cell concentration, also have different productivity. Developing a mechanistic understanding of the occurrence of steady state multiplicity and devising a strategy to steer the culture toward the desired steady state is critical. We establish a multi-scale kinetic model that integrates a mechanistic intracellular metabolic model and cell growth model in a continuous bioreactor. We show that steady state multiplicity exists in a range of dilution rate in continuous culture as a result of the bistable behavior in glycolysis. The insights from the model were used to devise strategies to guide the culture to the desired steady state in the multiple steady state region. The model provides a guideline principle in the design of continuous culture processes of mammalian cells. © 2015 Wiley Periodicals, Inc.

Critical review of membrane bioreactor models--part 1: biokinetic and filtration models.

PubMed

Naessens, W; Maere, T; Nopens, I

2012-10-01

Membrane bioreactor technology exists for a couple of decades, but has not yet overwhelmed the market due to some serious drawbacks of which operational cost due to fouling is the major contributor. Knowledge buildup and optimisation for such complex systems can significantly benefit from mathematical modelling. In this paper, the vast literature on modelling MBR biokinetics and filtration is critically reviewed. It was found that models cover the wide range of empirical to detailed mechanistic descriptions and have mainly been used for knowledge development and to a lesser extent for system optimisation/control. Moreover, studies are still predominantly performed at lab or pilot scale. Trends are discussed, knowledge gaps identified and interesting routes for further research suggested. Copyright © 2012 Elsevier Ltd. All rights reserved.

Enhanced Vapor-Phase Diffusion in Porous Media - LDRD Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ho, C.K.; Webb, S.W.

1999-01-01

As part of the Laboratory-Directed Research and Development (LDRD) Program at Sandia National Laboratories, an investigation into the existence of enhanced vapor-phase diffusion (EVD) in porous media has been conducted. A thorough literature review was initially performed across multiple disciplines (soil science and engineering), and based on this review, the existence of EVD was found to be questionable. As a result, modeling and experiments were initiated to investigate the existence of EVD. In this LDRD, the first mechanistic model of EVD was developed which demonstrated the mechanisms responsible for EVD. The first direct measurements of EVD have also been conductedmore » at multiple scales. Measurements have been made at the pore scale, in a two- dimensional network as represented by a fracture aperture, and in a porous medium. Significant enhancement of vapor-phase transport relative to Fickian diffusion was measured in all cases. The modeling and experimental results provide additional mechanisms for EVD beyond those presented by the generally accepted model of Philip and deVries (1957), which required a thermal gradient for EVD to exist. Modeling and experimental results show significant enhancement under isothermal conditions. Application of EVD to vapor transport in the near-surface vadose zone show a significant variation between no enhancement, the model of Philip and deVries, and the present results. Based on this information, the model of Philip and deVries may need to be modified, and additional studies are recommended.« less

Exploring Organic Mechanistic Puzzles with Molecular Modeling

ERIC Educational Resources Information Center

Horowitz, Gail; Schwartz, Gary

2004-01-01

The molecular modeling was used to reinforce more general skills such as deducing and drawing reaction mechanisms, analyzing reaction kinetics and thermodynamics and drawing reaction coordinate energy diagrams. This modeling was done through the design of mechanistic puzzles, involving reactions not familiar to the students.

Modeling the risk of water pollution by pesticides from imbalanced data.

PubMed

Trajanov, Aneta; Kuzmanovski, Vladimir; Real, Benoit; Perreau, Jonathan Marks; Džeroski, Sašo; Debeljak, Marko

2018-04-30

The pollution of ground and surface waters with pesticides is a serious ecological issue that requires adequate treatment. Most of the existing water pollution models are mechanistic mathematical models. While they have made a significant contribution to understanding the transfer processes, they face the problem of validation because of their complexity, the user subjectivity in their parameterization, and the lack of empirical data for validation. In addition, the data describing water pollution with pesticides are, in most cases, very imbalanced. This is due to strict regulations for pesticide applications, which lead to only a few pollution events. In this study, we propose the use of data mining to build models for assessing the risk of water pollution by pesticides in field-drained outflow water. Unlike the mechanistic models, the models generated by data mining are based on easily obtainable empirical data, while the parameterization of the models is not influenced by the subjectivity of ecological modelers. We used empirical data from field trials at the La Jaillière experimental site in France and applied the random forests algorithm to build predictive models that predict "risky" and "not-risky" pesticide application events. To address the problems of the imbalanced classes in the data, cost-sensitive learning and different measures of predictive performance were used. Despite the high imbalance between risky and not-risky application events, we managed to build predictive models that make reliable predictions. The proposed modeling approach can be easily applied to other ecological modeling problems where we encounter empirical data with highly imbalanced classes.

Mechanistic model for catalytic recombination during aerobraking maneuvers

NASA Technical Reports Server (NTRS)

Willey, Ronald J.

1989-01-01

Several mechanistic models are developed to predict recombination coefficients for use in heat shield design for reusable surface insulation (RSI) on aerobraking vehicles such as space shuttles. The models are applied over a temperature range of 300 to 1800 K and a stagnation pressure range of 0 to 3,000 Pa. A four parameter model in temperature was found to work best; however, several models (including those with atom concentrations at the surface) were also investigated. Mechanistic models developed with atom concentration terms may be applicable when sufficient data becomes available. The requirement is shown for recombination experiments in the 300 to 1000 K and 1500 to 1850 K temperature range, with deliberate concentration variations.

A Solution to the Cosmic Conundrum including Cosmological Constant and Dark Energy Problems

NASA Astrophysics Data System (ADS)

Singh, A.

2009-12-01

A comprehensive solution to the cosmic conundrum is presented that also resolves key paradoxes of quantum mechanics and relativity. A simple mathematical model, the Gravity Nullification model (GNM), is proposed that integrates the missing physics of the spontaneous relativistic conversion of mass to energy into the existing physics theories, specifically a simplified general theory of relativity. Mechanistic mathematical expressions are derived for a relativistic universe expansion, which predict both the observed linear Hubble expansion in the nearby universe and the accelerating expansion exhibited by the supernova observations. The integrated model addresses the key questions haunting physics and Big Bang cosmology. It also provides a fresh perspective on the misconceived birth and evolution of the universe, especially the creation and dissolution of matter. The proposed model eliminates singularities from existing models and the need for the incredible and unverifiable assumptions including the superluminous inflation scenario, multiple universes, multiple dimensions, Anthropic principle, and quantum gravity. GNM predicts the observed features of the universe without any explicit consideration of time as a governing parameter.

Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan

USGS Publications Warehouse

Safaie, Ammar; Wendzel, Aaron; Ge, Zhongfu; Nevers, Meredith; Whitman, Richard L.; Corsi, Steven R.; Phanikumar, Mantha S.

2016-01-01

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration

PubMed Central

Lobo, Daniel; Feldman, Erica B.; Shah, Michelle; Malone, Taylor J.

2014-01-01

Abstract Amphibians and molting arthropods have the remarkable capacity to regenerate amputated limbs, as described by an extensive literature of experimental cuts, amputations, grafts, and molecular techniques. Despite a rich history of experimental effort, no comprehensive mechanistic model exists that can account for the pattern regulation observed in these experiments. While bioinformatics algorithms have revolutionized the study of signaling pathways, no such tools have heretofore been available to assist scientists in formulating testable models of large‐scale morphogenesis that match published data in the limb regeneration field. Major barriers to preventing an algorithmic approach are the lack of formal descriptions for experimental regenerative information and a repository to centralize storage and mining of functional data on limb regeneration. Establishing a new bioinformatics of shape would significantly accelerate the discovery of key insights into the mechanisms that implement complex regeneration. Here, we describe a novel mathematical ontology for limb regeneration to unambiguously encode phenotype, manipulation, and experiment data. Based on this formalism, we present the first centralized formal database of published limb regeneration experiments together with a user‐friendly expert system tool to facilitate its access and mining. These resources are freely available for the community and will assist both human biologists and artificial intelligence systems to discover testable, mechanistic models of limb regeneration. PMID:25729585

A MECHANISTIC MODEL FOR MERCURY CAPTURE WITH IN-SITU GENERATED TITANIA PARTICLES: ROLE OF WATER VAPOR

EPA Science Inventory

A mechanistic model to predict the capture of gas phase mercury species using in-situ generated titania nanosize particles activated by UV irradiation is developed. The model is an extension of a recently reported model1 for photochemical reactions that accounts for the rates of...

Recent advances in mathematical modeling of developmental abnormalities using mechanistic information.

PubMed

Kavlock, R J

1997-01-01

During the last several years, significant changes in the risk assessment process for developmental toxicity of environmental contaminants have begun to emerge. The first of these changes is the development and beginning use of statistically based dose-response models [the benchmark dose (BMD) approach] that better utilize data derived from existing testing approaches. Accompanying this change is the greater emphasis placed on understanding and using mechanistic information to yield more accurate, reliable, and less uncertain risk assessments. The next stage in the evolution of risk assessment will be the use of biologically based dose-response (BBDR) models that begin to build into the statistically based models factors related to the underlying kinetic, biochemical, and/or physiologic processes perturbed by a toxicant. Such models are now emerging from several research laboratories. The introduction of quantitative models and the incorporation of biologic information into them has pointed to the need for even more sophisticated modifications for which we offer the term embryologically based dose-response (EBDR) models. Because these models would be based upon the understanding of normal morphogenesis, they represent a quantum leap in our thinking, but their complexity presents daunting challenges both to the developmental biologist and the developmental toxicologist. Implementation of these models will require extensive communication between developmental toxicologists, molecular embryologists, and biomathematicians. The remarkable progress in the understanding of mammalian embryonic development at the molecular level that has occurred over the last decade combined with advances in computing power and computational models should eventually enable these as yet hypothetical models to be brought into use.

A model of strength

USGS Publications Warehouse

Johnson, Douglas H.; Cook, R.D.

2013-01-01

In her AAAS News & Notes piece "Can the Southwest manage its thirst?" (26 July, p. 362), K. Wren quotes Ajay Kalra, who advocates a particular method for predicting Colorado River streamflow "because it eschews complex physical climate models for a statistical data-driven modeling approach." A preference for data-driven models may be appropriate in this individual situation, but it is not so generally, Data-driven models often come with a warning against extrapolating beyond the range of the data used to develop the models. When the future is like the past, data-driven models can work well for prediction, but it is easy to over-model local or transient phenomena, often leading to predictive inaccuracy (1). Mechanistic models are built on established knowledge of the process that connects the response variables with the predictors, using information obtained outside of an extant data set. One may shy away from a mechanistic approach when the underlying process is judged to be too complicated, but good predictive models can be constructed with statistical components that account for ingredients missing in the mechanistic analysis. Models with sound mechanistic components are more generally applicable and robust than data-driven models.

Rational and Mechanistic Perspectives on Reinforcement Learning

ERIC Educational Resources Information Center

Chater, Nick

2009-01-01

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: "mechanistic" and "rational." Reinforcement learning is often viewed in mechanistic terms--as…

Native state volume fluctuations in proteins as a mechanism for dynamic allostery

DOE PAGES

Law, Anthony B.; Sapienza, Paul J.; Zhang, Jun; ...

2017-01-17

Allostery enables tight regulation of protein function in the cellular environment. While existing models of allostery are firmly rooted in the current structure-function paradigm, the mechanistic basis for allostery in the absence of structural change remains unclear. In this study, we show that a typical globular protein is able to undergo significant changes in volume under native conditions while exhibiting no additional changes in protein structure. These native state volume fluctuations were found to correlate with changes in internal motions that were previously recognized as a source of allosteric entropy. This finding offers a novel mechanistic basis for allostery inmore » the absence of canonical structural change. As a result, the unexpected observation that function can be derived from expanded, low density protein states has broad implications for our understanding of allostery and suggests that the general concept of the native state be expanded to allow for more variable physical dimensions with looser packing.« less

Investigation of mechanistic deterioration modeling for bridge design and management.

DOT National Transportation Integrated Search

2017-04-01

The ongoing deterioration of highway bridges in Colorado dictates that an effective method for allocating limited management resources be developed. In order to predict bridge deterioration in advance, mechanistic models that analyze the physical pro...

Environmentally induced epigenetic toxicity: potential public health concerns

PubMed Central

Marczylo, Emma L.; Jacobs, Miriam N.; Gant, Timothy W.

2016-01-01

Abstract Throughout our lives, epigenetic processes shape our development and enable us to adapt to a constantly changing environment. Identifying and understanding environmentally induced epigenetic change(s) that may lead to adverse outcomes is vital for protecting public health. This review, therefore, examines the present understanding of epigenetic mechanisms involved in the mammalian life cycle, evaluates the current evidence for environmentally induced epigenetic toxicity in human cohorts and rodent models and highlights the research considerations and implications of this emerging knowledge for public health and regulatory toxicology. Many hundreds of studies have investigated such toxicity, yet relatively few have demonstrated a mechanistic association among specific environmental exposures, epigenetic changes and adverse health outcomes in human epidemiological cohorts and/or rodent models. While this small body of evidence is largely composed of exploratory in vivo high-dose range studies, it does set a precedent for the existence of environmentally induced epigenetic toxicity. Consequently, there is worldwide recognition of this phenomenon, and discussion on how to both guide further scientific research towards a greater mechanistic understanding of environmentally induced epigenetic toxicity in humans, and translate relevant research outcomes into appropriate regulatory policies for effective public health protection. PMID:27278298

Environmentally induced epigenetic toxicity: potential public health concerns.

PubMed

Marczylo, Emma L; Jacobs, Miriam N; Gant, Timothy W

2016-09-01

Throughout our lives, epigenetic processes shape our development and enable us to adapt to a constantly changing environment. Identifying and understanding environmentally induced epigenetic change(s) that may lead to adverse outcomes is vital for protecting public health. This review, therefore, examines the present understanding of epigenetic mechanisms involved in the mammalian life cycle, evaluates the current evidence for environmentally induced epigenetic toxicity in human cohorts and rodent models and highlights the research considerations and implications of this emerging knowledge for public health and regulatory toxicology. Many hundreds of studies have investigated such toxicity, yet relatively few have demonstrated a mechanistic association among specific environmental exposures, epigenetic changes and adverse health outcomes in human epidemiological cohorts and/or rodent models. While this small body of evidence is largely composed of exploratory in vivo high-dose range studies, it does set a precedent for the existence of environmentally induced epigenetic toxicity. Consequently, there is worldwide recognition of this phenomenon, and discussion on how to both guide further scientific research towards a greater mechanistic understanding of environmentally induced epigenetic toxicity in humans, and translate relevant research outcomes into appropriate regulatory policies for effective public health protection.

From cancer genomes to cancer models: bridging the gaps

PubMed Central

Baudot, Anaïs; Real, Francisco X.; Izarzugaza, José M. G.; Valencia, Alfonso

2009-01-01

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high-throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high-throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer-evolution models will be influenced by the high-throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications. PMID:19305388

Improved theory of time domain reflectometry with variable coaxial cable length for electrical conductivity measurements

USDA-ARS?s Scientific Manuscript database

Although empirical models have been developed previously, a mechanistic model is needed for estimating electrical conductivity (EC) using time domain reflectometry (TDR) with variable lengths of coaxial cable. The goals of this study are to: (1) derive a mechanistic model based on multisection tra...

The Japanese utilities` expectations for subchannel analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toba, Akio; Omoto, Akira

1995-12-01

Boiling water reactor (BWR) utilities in Japan began to consider the development of a mechanistic model to describe the critical heat transfer conditions in the BWR fuel subchannel. Such a mechanistic model will not only decrease the necessity of tests, but will also help by removing some overly conservative safety margins in thermal hydraulics. With the use of a postdryout heat transfer correlation, new acceptance criteria may be applicable to evaluate the fuel integrity. Mechanistic subchannel analysis models will certainly back up this approach. This model will also be applicable to the analysis of large-size fuel bundles and examination ofmore » corrosion behavior.« less

Specialists without spirit: limitations of the mechanistic biomedical model.

PubMed

Hewa, S; Hetherington, R W

1995-06-01

This paper examines the origin and the development of the mechanistic model of the human body and health in terms of Max Weber's theory of rationalization. It is argued that the development of Western scientific medicine is a part of the broad process of rationalization that began in sixteenth century Europe as a result of the Reformation. The development of the mechanistic view of the human body in Western medicine is consistent with the ideas of calculability, predictability, and control-the major tenets of the process of rationalization as described by Weber. In recent years, however, the limitations of the mechanistic model have been the topic of many discussions. George Engel, a leading advocate of general systems theory, is one of the leading proponents of a new medical model which includes the general quality of life, clean environment, and psychological, or spiritual stability of life. The paper concludes with consideration of the potential of Engel's proposed new model in the context of the current state of rationalization in modern industrialized society.

Biomechanics-based in silico medicine: the manifesto of a new science.

PubMed

Viceconti, Marco

2015-01-21

In this perspective article we discuss the role of contemporary biomechanics in the light of recent applications such as the development of the so-called Virtual Physiological Human technologies for physiology-based in silico medicine. In order to build Virtual Physiological Human (VPH) models, computer models that capture and integrate the complex systemic dynamics of living organisms across radically different space-time scales, we need to re-formulate a vast body of existing biology and physiology knowledge so that it is formulated as a quantitative hypothesis, which can be expressed in mathematical terms. Once the predictive accuracy of these models is confirmed against controlled experiments and against clinical observations, we will have VPH model that can reliably predict certain quantitative changes in health status of a given patient, but also, more important, we will have a theory, in the true meaning this word has in the scientific method. In this scenario, biomechanics plays a very important role, biomechanics is one of the few areas of life sciences where we attempt to build full mechanistic explanations based on quantitative observations, in other words, we investigate living organisms like physical systems. This is in our opinion a Copernican revolution, around which the scope of biomechanics should be re-defined. Thus, we propose a new definition for our research domain "Biomechanics is the study of living organisms as mechanistic systems". Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparing two-zone models of dust exposure.

PubMed

Jones, Rachael M; Simmons, Catherine E; Boelter, Fred W

2011-09-01

The selection and application of mathematical models to work tasks is challenging. Previously, we developed and evaluated a semi-empirical two-zone model that predicts time-weighted average (TWA) concentrations (Ctwa) of dust emitted during the sanding of drywall joint compound. Here, we fit the emission rate and random air speed variables of a mechanistic two-zone model to testing event data and apply and evaluate the model using data from two field studies. We found that the fitted random air speed values and emission rate were sensitive to (i) the size of the near-field and (ii) the objective function used for fitting, but this did not substantially impact predicted dust Ctwa. The mechanistic model predictions were lower than the semi-empirical model predictions and measured respirable dust Ctwa at Site A but were within an acceptable range. At Site B, a 10.5 m3 room, the mechanistic model did not capture the observed difference between PBZ and area Ctwa. The model predicted uniform mixing and predicted dust Ctwa up to an order of magnitude greater than was measured. We suggest that applications of the mechanistic model be limited to contexts where the near-field volume is very small relative to the far-field volume.

The chronicles of Porphyromonas gingivalis: the microbium, the human oral epithelium and their interplay

PubMed Central

Yilmaz, Özlem

2009-01-01

The microbiota of the human oral mucosa consists of a myriad of bacterial species that normally exist in commensal harmony with the host. Porphyromonas gingivalis, an aetiological agent in severe forms of periodontitis (a chronic inflammatory disease), is a prominent component of the oral microbiome and a successful colonizer of the oral epithelium. This Gram-negative anaerobe can also exist within the host epithelium without the existence of overt disease. Gingival epithelial cells, the outer lining of the gingival mucosa, which function as an important part of the innate immune system, are among the first host cells colonized by P. gingivalis. This review describes recent studies implicating the co-existence and intracellular adaptation of the organism in these target host cells. Specifically, recent findings on the putative mechanisms of persistence, intercellular dissemination and opportunism are highlighted. These new findings may also represent an original and valuable model for mechanistic characterization of other successful host-adapted, self-limiting, persistent intracellular bacteria in human epithelial tissues. PMID:18832296

The chronicles of Porphyromonas gingivalis: the microbium, the human oral epithelium and their interplay.

PubMed

Yilmaz, Ozlem

2008-10-01

The microbiota of the human oral mucosa consists of a myriad of bacterial species that normally exist in commensal harmony with the host. Porphyromonas gingivalis, an aetiological agent in severe forms of periodontitis (a chronic inflammatory disease), is a prominent component of the oral microbiome and a successful colonizer of the oral epithelium. This Gram-negative anaerobe can also exist within the host epithelium without the existence of overt disease. Gingival epithelial cells, the outer lining of the gingival mucosa, which function as an important part of the innate immune system, are among the first host cells colonized by P. gingivalis. This review describes recent studies implicating the co-existence and intracellular adaptation of the organism in these target host cells. Specifically, recent findings on the putative mechanisms of persistence, intercellular dissemination and opportunism are highlighted. These new findings may also represent an original and valuable model for mechanistic characterization of other successful host-adapted, self-limiting, persistent intracellular bacteria in human epithelial tissues.

Preparation for implementation of the mechanistic-empirical pavement design guide in Michigan, part 3 : local calibration and validation of the pavement-ME performance models.

DOT National Transportation Integrated Search

2014-11-01

The main objective of Part 3 was to locally calibrate and validate the mechanistic-empirical pavement : design guide (Pavement-ME) performance models to Michigan conditions. The local calibration of the : performance models in the Pavement-ME is a ch...

A new mechanistic growth model for simultaneous determination of lag phase duration and exponential growth rate and a new Belehdradek-type model for evaluating the effect of temperature on growth rate

USDA-ARS?s Scientific Manuscript database

A new mechanistic growth model was developed to describe microbial growth under isothermal conditions. The new mathematical model was derived from the basic observation of bacterial growth that may include lag, exponential, and stationary phases. With this model, the lag phase duration and exponen...

DEVELOPMENT AND VALIDATION OF A MECHANISTIC GROUND SPRAYER MODEL

EPA Science Inventory

In the last ten years the Spray Drift Task Force (SDTF), U.S. Environmental Protection Agency (EPA), USDA Agricultural Research Service, and USDA Forest Service cooperated in the refinement and evaluation of a mechanistically-based aerial spray model (contained within AGDISP and ...

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Solving Immunology?

PubMed Central

Vodovotz, Yoram; Xia, Ashley; Read, Elizabeth L.; Bassaganya-Riera, Josep; Hafler, David A.; Sontag, Eduardo; Wang, Jin; Tsang, John S.; Day, Judy D.; Kleinstein, Steven; Butte, Atul J.; Altman, Matthew C; Hammond, Ross; Sealfon, Stuart C.

2016-01-01

Emergent responses of the immune system result from integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the NIAID workshop “Complex Systems Science, Modeling and Immunity” and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies. PMID:27986392

A mechanistic model of heat transfer for gas-liquid flow in vertical wellbore annuli.

PubMed

Yin, Bang-Tang; Li, Xiang-Fang; Liu, Gang

2018-01-01

The most prominent aspect of multiphase flow is the variation in the physical distribution of the phases in the flow conduit known as the flow pattern. Several different flow patterns can exist under different flow conditions which have significant effects on liquid holdup, pressure gradient and heat transfer. Gas-liquid two-phase flow in an annulus can be found in a variety of practical situations. In high rate oil and gas production, it may be beneficial to flow fluids vertically through the annulus configuration between well tubing and casing. The flow patterns in annuli are different from pipe flow. There are both casing and tubing liquid films in slug flow and annular flow in the annulus. Multiphase heat transfer depends on the hydrodynamic behavior of the flow. There are very limited research results that can be found in the open literature for multiphase heat transfer in wellbore annuli. A mechanistic model of multiphase heat transfer is developed for different flow patterns of upward gas-liquid flow in vertical annuli. The required local flow parameters are predicted by use of the hydraulic model of steady-state multiphase flow in wellbore annuli recently developed by Yin et al. The modified heat-transfer model for single gas or liquid flow is verified by comparison with Manabe's experimental results. For different flow patterns, it is compared with modified unified Zhang et al. model based on representative diameters.

Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning.

PubMed

An, Gary C; Faeder, James R

2009-01-01

Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this 'engineering' process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed 'dynamic knowledge representation,' and is intended to be an integrated component of the iterative cycle of scientific discovery. BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000 and 10,000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10,000 and a secondary dose of LPS at 10,000 administered at approximately 27h of simulated time. Simulations of 'knockout' versions of the model allowed further examination of the interactions within the signaling cascade. The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge - a 'tolerance' dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated 'knockout' results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IkappaB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned 'tolerant' behavior. We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of 'conceptual model verification.' This allows for both the 'checking' of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.

ASSESSING POPULATION EXPOSURES TO MULTIPLE AIR POLLUTANTS USING A MECHANISTIC SOURCE-TO-DOSE MODELING FRAMEWORK

EPA Science Inventory

The Modeling Environment for Total Risks studies (MENTOR) system, combined with an extension of the SHEDS (Stochastic Human Exposure and Dose Simulation) methodology, provide a mechanistically consistent framework for conducting source-to-dose exposure assessments of multiple pol...

PROPOSED SUITE OF MODELS FOR ESTIMATING DOSE RESULTING FROM EXPOSURES BY THE DERMAL ROUTE

EPA Science Inventory

Recent risk assessment guidance emphasizes consideration of mechanistic factors for influencing disposition of a toxicant. To incorporate mechanistic information into risk assessment, a suite of models is proposed for use in characterizing and quantifying dosimetry of toxic age...

Toward a Broader Perspective in the Evolutionism-Creationism Debate.

ERIC Educational Resources Information Center

Strahler, Arthur N.

1983-01-01

Examines creationism/evolution debate in context of philosophy using ontological models in which reality is assigned to one or both natural or transnatural (supernatural) realms. The six models (theistic-teleological dualism; deistic-mechanistic dualism; fundamentalist creationism; atheistic monism; theistic monism; mechanistic monism) deal with…

Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity

PubMed Central

Fjodorova, Natalja; Novič, Marjana

2012-01-01

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals. PMID:24688639

Towards predictive models of the human gut microbiome

PubMed Central

2014-01-01

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the current state-of-the-art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model of the intestinal microbiota. PMID:24727124

Towards new approaches in phenological modelling

NASA Astrophysics Data System (ADS)

Chmielewski, Frank-M.; Götz, Klaus-P.; Rawel, Harshard M.; Homann, Thomas

2014-05-01

Modelling of phenological stages is based on temperature sums for many decades, describing both the chilling and the forcing requirement of woody plants until the beginning of leafing or flowering. Parts of this approach go back to Reaumur (1735), who originally proposed the concept of growing degree-days. Now, there is a growing body of opinion that asks for new methods in phenological modelling and more in-depth studies on dormancy release of woody plants. This requirement is easily understandable if we consider the wide application of phenological models, which can even affect the results of climate models. To this day, in phenological models still a number of parameters need to be optimised on observations, although some basic physiological knowledge of the chilling and forcing requirement of plants is already considered in these approaches (semi-mechanistic models). Limiting, for a fundamental improvement of these models, is the lack of knowledge about the course of dormancy in woody plants, which cannot be directly observed and which is also insufficiently described in the literature. Modern metabolomic methods provide a solution for this problem and allow both, the validation of currently used phenological models as well as the development of mechanistic approaches. In order to develop this kind of models, changes of metabolites (concentration, temporal course) must be set in relation to the variability of environmental (steering) parameters (weather, day length, etc.). This necessarily requires multi-year (3-5 yr.) and high-resolution (weekly probes between autumn and spring) data. The feasibility of this approach has already been tested in a 3-year pilot-study on sweet cherries. Our suggested methodology is not only limited to the flowering of fruit trees, it can be also applied to tree species of the natural vegetation, where even greater deficits in phenological modelling exist.

An Emphasis on Perception: Teaching Image Formation Using a Mechanistic Model of Vision.

ERIC Educational Resources Information Center

Allen, Sue; And Others

An effective way to teach the concept of image is to give students a model of human vision which incorporates a simple mechanism of depth perception. In this study two almost identical versions of a curriculum in geometrical optics were created. One used a mechanistic, interpretive eye model, and in the other the eye was modeled as a passive,…

Development of a Mechanistically Based, Basin-Scale Stream Temperature Model: Applications to Cumulative Effects Modeling

Treesearch

Douglas Allen; William Dietrich; Peter Baker; Frank Ligon; Bruce Orr

2007-01-01

We describe a mechanistically-based stream model, BasinTemp, which assumes that direct shortwave radiation moderated by riparian and topographic shading, controls stream temperatures during the hottest part of the year. The model was developed to support a temperature TMDL for the South Fork Eel basin in Northern California and couples a GIS and a 1-D energy balance...

Predicting subsurface uranium transport: Mechanistic modeling constrained by experimental data

NASA Astrophysics Data System (ADS)

Ottman, Michael; Schenkeveld, Walter D. C.; Kraemer, Stephan

2017-04-01

Depleted uranium (DU) munitions and their widespread use throughout conflict zones around the world pose a persistent health threat to the inhabitants of those areas long after the conclusion of active combat. However, little emphasis has been put on developing a comprehensive, quantitative tool for use in remediation and hazard avoidance planning in a wide range of environments. In this context, we report experimental data on U interaction with soils and sediments. Here, we strive to improve existing risk assessment modeling paradigms by incorporating a variety of experimental data into a mechanistic U transport model for subsurface environments. 20 different soils and sediments from a variety of environments were chosen to represent a range of geochemical parameters that are relevant to U transport. The parameters included pH, organic matter content, CaCO3, Fe content and speciation, and clay content. pH ranged from 3 to 10, organic matter content from 6 to 120 g kg-1, CaCO3 from 0 to 700 g kg-1, amorphous Fe content from 0.3 to 6 g kg-1 and clay content from 4 to 580 g kg-1. Sorption experiments were then performed, and linear isotherms were constructed. Sorption experiment results show that among separate sets of sediments and soils, there is an inverse correlation between both soil pH and CaCO¬3 concentration relative to U sorptive affinity. The geological materials with the highest and lowest sorptive affinities for U differed in CaCO3 and organic matter concentrations, as well as clay content and pH. In a further step, we are testing if transport behavior in saturated porous media can be predicted based on adsorption isotherms and generic geochemical parameters, and comparing these modeling predictions with the results from column experiments. The comparison of these two data sets will examine if U transport can be effectively predicted from reactive transport modeling that incorporates the generic geochemical parameters. This work will serve to show whether a more mechanistic approach offers an improvement over statistical regression-based risk assessment models.

Mechanistic, Mathematical Model to Predict the Dynamics of Tissue Genesis in Bone Defects via Mechanical Feedback and Mediation of Biochemical Factors

PubMed Central

Moore, Shannon R.; Saidel, Gerald M.; Knothe, Ulf; Knothe Tate, Melissa L.

2014-01-01

The link between mechanics and biology in the generation and the adaptation of bone has been well studied in context of skeletal development and fracture healing. Yet, the prediction of tissue genesis within - and the spatiotemporal healing of - postnatal defects, necessitates a quantitative evaluation of mechano-biological interactions using experimental and clinical parameters. To address this current gap in knowledge, this study aims to develop a mechanistic mathematical model of tissue genesis using bone morphogenetic protein (BMP) to represent of a class of factors that may coordinate bone healing. Specifically, we developed a mechanistic, mathematical model to predict the dynamics of tissue genesis by periosteal progenitor cells within a long bone defect surrounded by periosteum and stabilized via an intramedullary nail. The emergent material properties and mechanical environment associated with nascent tissue genesis influence the strain stimulus sensed by progenitor cells within the periosteum. Using a mechanical finite element model, periosteal surface strains are predicted as a function of emergent, nascent tissue properties. Strains are then input to a mechanistic mathematical model, where mechanical regulation of BMP-2 production mediates rates of cellular proliferation, differentiation and tissue production, to predict healing outcomes. A parametric approach enables the spatial and temporal prediction of endochondral tissue regeneration, assessed as areas of cartilage and mineralized bone, as functions of radial distance from the periosteum and time. Comparing model results to histological outcomes from two previous studies of periosteum-mediated bone regeneration in a common ovine model, it was shown that mechanistic models incorporating mechanical feedback successfully predict patterns (spatial) and trends (temporal) of bone tissue regeneration. The novel model framework presented here integrates a mechanistic feedback system based on the mechanosensitivity of periosteal progenitor cells, which allows for modeling and prediction of tissue regeneration on multiple length and time scales. Through combination of computational, physical and engineering science approaches, the model platform provides a means to test new hypotheses in silico and to elucidate conditions conducive to endogenous tissue genesis. Next generation models will serve to unravel intrinsic differences in bone genesis by endochondral and intramembranous mechanisms. PMID:24967742

INCORPORATION OF MECHANISTIC INFORMATION IN THE ARSENIC PBPK MODEL DEVELOPMENT PROCESS

EPA Science Inventory

INCORPORATING MECHANISTIC INSIGHTS IN A PBPK MODEL FOR ARSENIC

Elaina M. Kenyon, Michael F. Hughes, Marina V. Evans, David J. Thomas, U.S. EPA; Miroslav Styblo, University of North Carolina; Michael Easterling, Analytical Sciences, Inc.

A physiologically based phar...

Use of Animal Models in Understanding Cancer-induced Bone Pain

PubMed Central

Slosky, Lauren M; Largent-Milnes, Tally M; Vanderah, Todd W

2015-01-01

Many common cancers have a propensity to metastasize to bone. Although malignancies often go undetected in their native tissues, bone metastases produce excruciating pain that severely compromises patient quality of life. Cancer-induced bone pain (CIBP) is poorly managed with existing medications, and its multifaceted etiology remains to be fully elucidated. Novel analgesic targets arise as more is learned about this complex and distinct pain state. Over the past two decades, multiple animal models have been developed to study CIBP’s unique pathology and identify therapeutic targets. Here, we review animal models of CIBP and the mechanistic insights gained as these models evolve. Findings from immunocompromised and immunocompetent host systems are discussed separately to highlight the effect of model choice on outcome. Gaining an understanding of the unique neuromolecular profile of cancer pain through the use of appropriate animal models will aid in the development of more effective therapeutics for CIBP. PMID:26339191

Progress in understanding the neuronal SNARE function and its regulation.

PubMed

Yoon, T-Y; Shin, Y-K

2009-02-01

Vesicle budding and fusion underlies many essential biochemical deliveries in eukaryotic cells, and its core fusion machinery is thought to be built on one protein family named soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE). Recent technical advances based on site-directed fluorescence labelling and nano-scale detection down to the single-molecule level rapidly unveiled the protein and the lipid intermediates along the fusion pathway as well as the molecular actions of fusion effectors. Here we summarize these new exciting findings in context with a new mechanistic model that reconciles two existing fusion models: the proteinaceous pore model and the hemifusion model. Further, we attempt to locate the points of action for the fusion effectors along the fusion pathway and to delineate the energetic interplay between the SNARE complexes and the fusion effectors.

How informative is the mouse for human gut microbiota research?

PubMed Central

Nguyen, Thi Loan Anh; Vieira-Silva, Sara; Liston, Adrian; Raes, Jeroen

2015-01-01

The microbiota of the human gut is gaining broad attention owing to its association with a wide range of diseases, ranging from metabolic disorders (e.g. obesity and type 2 diabetes) to autoimmune diseases (such as inflammatory bowel disease and type 1 diabetes), cancer and even neurodevelopmental disorders (e.g. autism). Having been increasingly used in biomedical research, mice have become the model of choice for most studies in this emerging field. Mouse models allow perturbations in gut microbiota to be studied in a controlled experimental setup, and thus help in assessing causality of the complex host-microbiota interactions and in developing mechanistic hypotheses. However, pitfalls should be considered when translating gut microbiome research results from mouse models to humans. In this Special Article, we discuss the intrinsic similarities and differences that exist between the two systems, and compare the human and murine core gut microbiota based on a meta-analysis of currently available datasets. Finally, we discuss the external factors that influence the capability of mouse models to recapitulate the gut microbiota shifts associated with human diseases, and investigate which alternative model systems exist for gut microbiota research. PMID:25561744

How informative is the mouse for human gut microbiota research?

PubMed

Nguyen, Thi Loan Anh; Vieira-Silva, Sara; Liston, Adrian; Raes, Jeroen

2015-01-01

The microbiota of the human gut is gaining broad attention owing to its association with a wide range of diseases, ranging from metabolic disorders (e.g. obesity and type 2 diabetes) to autoimmune diseases (such as inflammatory bowel disease and type 1 diabetes), cancer and even neurodevelopmental disorders (e.g. autism). Having been increasingly used in biomedical research, mice have become the model of choice for most studies in this emerging field. Mouse models allow perturbations in gut microbiota to be studied in a controlled experimental setup, and thus help in assessing causality of the complex host-microbiota interactions and in developing mechanistic hypotheses. However, pitfalls should be considered when translating gut microbiome research results from mouse models to humans. In this Special Article, we discuss the intrinsic similarities and differences that exist between the two systems, and compare the human and murine core gut microbiota based on a meta-analysis of currently available datasets. Finally, we discuss the external factors that influence the capability of mouse models to recapitulate the gut microbiota shifts associated with human diseases, and investigate which alternative model systems exist for gut microbiota research. © 2015. Published by The Company of Biologists Ltd.

Functionally relevant climate variables for arid lands: Aclimatic water deficit approach for modelling desert shrub distributions

Treesearch

Thomas E. Dilts; Peter J. Weisberg; Camie M. Dencker; Jeanne C. Chambers

2015-01-01

We have three goals. (1) To develop a suite of functionally relevant climate variables for modelling vegetation distribution on arid and semi-arid landscapes of the Great Basin, USA. (2) To compare the predictive power of vegetation distribution models based on mechanistically proximate factors (water deficit variables) and factors that are more mechanistically removed...

A Physics-Inspired Mechanistic Model of Migratory Movement Patterns in Birds.

PubMed

Revell, Christopher; Somveille, Marius

2017-08-29

In this paper, we introduce a mechanistic model of migratory movement patterns in birds, inspired by ideas and methods from physics. Previous studies have shed light on the factors influencing bird migration but have mainly relied on statistical correlative analysis of tracking data. Our novel method offers a bottom up explanation of population-level migratory movement patterns. It differs from previous mechanistic models of animal migration and enables predictions of pathways and destinations from a given starting location. We define an environmental potential landscape from environmental data and simulate bird movement within this landscape based on simple decision rules drawn from statistical mechanics. We explore the capacity of the model by qualitatively comparing simulation results to the non-breeding migration patterns of a seabird species, the Black-browed Albatross (Thalassarche melanophris). This minimal, two-parameter model was able to capture remarkably well the previously documented migration patterns of the Black-browed Albatross, with the best combination of parameter values conserved across multiple geographically separate populations. Our physics-inspired mechanistic model could be applied to other bird and highly-mobile species, improving our understanding of the relative importance of various factors driving migration and making predictions that could be useful for conservation.

The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species.

PubMed

Rougier, Thibaud; Lassalle, Géraldine; Drouineau, Hilaire; Dumoulin, Nicolas; Faure, Thierry; Deffuant, Guillaume; Rochard, Eric; Lambert, Patrick

2015-01-01

Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well.

The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species

PubMed Central

Rougier, Thibaud; Lassalle, Géraldine; Drouineau, Hilaire; Dumoulin, Nicolas; Faure, Thierry; Deffuant, Guillaume; Rochard, Eric; Lambert, Patrick

2015-01-01

Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well. PMID:26426280

Mechanistic Understanding of Microbial Plugging for Improved Sweep Efficiency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steven Bryant; Larry Britton

2008-09-30

Microbial plugging has been proposed as an effective low cost method of permeability reduction. Yet there is a dearth of information on the fundamental processes of microbial growth in porous media, and there are no suitable data to model the process of microbial plugging as it relates to sweep efficiency. To optimize the field implementation, better mechanistic and volumetric understanding of biofilm growth within a porous medium is needed. In particular, the engineering design hinges upon a quantitative relationship between amount of nutrient consumption, amount of growth, and degree of permeability reduction. In this project experiments were conducted to obtainmore » new data to elucidate this relationship. Experiments in heterogeneous (layered) beadpacks showed that microbes could grow preferentially in the high permeability layer. Ultimately this caused flow to be equally divided between high and low permeability layers, precisely the behavior needed for MEOR. Remarkably, classical models of microbial nutrient uptake in batch experiments do not explain the nutrient consumption by the same microbes in flow experiments. We propose a simple extension of classical kinetics to account for the self-limiting consumption of nutrient observed in our experiments, and we outline a modeling approach based on architecture and behavior of biofilms. Such a model would account for the changing trend of nutrient consumption by bacteria with the increasing biomass and the onset of biofilm formation. However no existing model can explain the microbial preference for growth in high permeability regions, nor is there any obvious extension of the model for this observation. An attractive conjecture is that quorum sensing is involved in the heterogeneous bead packs.« less

Current limitations and recommendations to improve testing for the environmental assessment of endocrine active substances

USGS Publications Warehouse

Coady, Katherine K.; Biever, Ronald C.; Denslow, Nancy D.; Gross, Melanie; Guiney, Patrick D.; Holbech, Henrik; Karouna-Renier, Natalie K.; Katsiadaki, Ioanna; Krueger, Hank; Levine, Steven L.; Maack, Gerd; Williams, Mike; Wolf, Jeffrey C.; Ankley, Gerald T.

2017-01-01

In the present study, existing regulatory frameworks and test systems for assessing potential endocrine active chemicals are described, and associated challenges are discussed, along with proposed approaches to address these challenges. Regulatory frameworks vary somewhat across geographies, but all basically evaluate whether a chemical possesses endocrine activity and whether this activity can result in adverse outcomes either to humans or to the environment. Current test systems include in silico, in vitro, and in vivo techniques focused on detecting potential endocrine activity, and in vivo tests that collect apical data to detect possible adverse effects. These test systems are currently designed to robustly assess endocrine activity and/or adverse effects in the estrogen, androgen, and thyroid hormone signaling pathways; however, there are some limitations of current test systems for evaluating endocrine hazard and risk. These limitations include a lack of certainty regarding: 1) adequately sensitive species and life stages; 2) mechanistic endpoints that are diagnostic for endocrine pathways of concern; and 3) the linkage between mechanistic responses and apical, adverse outcomes. Furthermore, some existing test methods are resource intensive with regard to time, cost, and use of animals. However, based on recent experiences, there are opportunities to improve approaches to and guidance for existing test methods and to reduce uncertainty. For example, in vitro high-throughput screening could be used to prioritize chemicals for testing and provide insights as to the most appropriate assays for characterizing hazard and risk. Other recommendations include adding endpoints for elucidating connections between mechanistic effects and adverse outcomes, identifying potentially sensitive taxa for which test methods currently do not exist, and addressing key endocrine pathways of possible concern in addition to those associated with estrogen, androgen, and thyroid signaling. 

Model-Based Analysis of Biopharmaceutic Experiments To Improve Mechanistic Oral Absorption Modeling: An Integrated in Vitro in Vivo Extrapolation Perspective Using Ketoconazole as a Model Drug.

PubMed

Pathak, Shriram M; Ruff, Aaron; Kostewicz, Edmund S; Patel, Nikunjkumar; Turner, David B; Jamei, Masoud

2017-12-04

Mechanistic modeling of in vitro data generated from metabolic enzyme systems (viz., liver microsomes, hepatocytes, rCYP enzymes, etc.) facilitates in vitro-in vivo extrapolation (IVIV_E) of metabolic clearance which plays a key role in the successful prediction of clearance in vivo within physiologically-based pharmacokinetic (PBPK) modeling. A similar concept can be applied to solubility and dissolution experiments whereby mechanistic modeling can be used to estimate intrinsic parameters required for mechanistic oral absorption simulation in vivo. However, this approach has not widely been applied within an integrated workflow. We present a stepwise modeling approach where relevant biopharmaceutics parameters for ketoconazole (KTZ) are determined and/or confirmed from the modeling of in vitro experiments before being directly used within a PBPK model. Modeling was applied to various in vitro experiments, namely: (a) aqueous solubility profiles to determine intrinsic solubility, salt limiting solubility factors and to verify pK a ; (b) biorelevant solubility measurements to estimate bile-micelle partition coefficients; (c) fasted state simulated gastric fluid (FaSSGF) dissolution for formulation disintegration profiling; and (d) transfer experiments to estimate supersaturation and precipitation parameters. These parameters were then used within a PBPK model to predict the dissolved and total (i.e., including the precipitated fraction) concentrations of KTZ in the duodenum of a virtual population and compared against observed clinical data. The developed model well characterized the intraluminal dissolution, supersaturation, and precipitation behavior of KTZ. The mean simulated AUC 0-t of the total and dissolved concentrations of KTZ were comparable to (within 2-fold of) the corresponding observed profile. Moreover, the developed PBPK model of KTZ successfully described the impact of supersaturation and precipitation on the systemic plasma concentration profiles of KTZ for 200, 300, and 400 mg doses. These results demonstrate that IVIV_E applied to biopharmaceutical experiments can be used to understand and build confidence in the quality of the input parameters and mechanistic models used for mechanistic oral absorption simulations in vivo, thereby improving the prediction performance of PBPK models. Moreover, this approach can inform the selection and design of in vitro experiments, potentially eliminating redundant experiments and thus helping to reduce the cost and time of drug product development.

Mechanistic insights into the role of mTOR signaling in neuronal differentiation.

PubMed

Bateman, Joseph M

2015-01-01

Temporal control of neuronal differentiation is critical to produce a complete and fully functional nervous system. Loss of the precise temporal control of neuronal cell fate can lead to defects in cognitive development and to disorders such as epilepsy and autism. Mechanistic target of rapamycin (mTOR) is a large serine/threonine kinase that acts as a crucial sensor of cellular homeostasis. mTOR signaling has recently emerged as a key regulator of neurogenesis. However, the mechanism by which mTOR regulates neurogenesis is poorly understood. In constrast to other functions of the pathway, 'neurogenic mTOR pathway factors' have not previously been identified. We have very recently used Drosophila as a model system to identify the gene unkempt as the first component of the mTOR pathway regulating neuronal differentiation. Our study demonstrates that specific adaptor proteins exist that channel mTOR signaling toward the regulation of neuronal cell fate. In this Commentary we discuss the role of mTOR signaling in neurogenesis and the significance of these findings in advancing our understanding of the mechanism by which mTOR signaling controls neuronal differentiation.

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

PubMed

Wang, Gang; Briskot, Till; Hahn, Tobias; Baumann, Pascal; Hubbuch, Jürgen

2017-03-03

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Mechanistic models as a transferable framework for projecting effects of habitat change on production and delivery of ecosystem services

EPA Science Inventory

Drawing a link between habitat change and the production and delivery of ecosystem services is a priority in coastal estuarine ecosystems. Mechanistic modeling tools are highly functional for exploring this link because they allow for the synthesis of multiple ecological and beh...

GENE ARRAYS FOR ELUCIDATING MECHANISTIC DATA FROM MODELS OF MALE INFERTILITY AND CHEMICAL EXPOSURE IN MICE, RATS AND HUMANS

EPA Science Inventory

Gene arrays for elucidating mechanistic data from models of male infertility and chemical exposure in mice, rats and humans
John C. Rockett and David J. Dix
Gamete and Early Embryo Biology Branch, Reproductive Toxicology Division, National Health and Environmental Effects ...

Controls on Filling and Evacuation of Sediment in Waterfall Plunge Pools

NASA Astrophysics Data System (ADS)

Scheingross, J. S.; Lamb, M. P.

2014-12-01

Many waterfalls are characterized by the presence of deep plunge pools that experience periods of sediment fill and evacuation. These cycles of sediment fill are a first order control on the relative magnitude of lateral versus vertical erosion at the base of waterfalls, as vertical incision requires cover-free plunge pools to expose the bedrock floor, while lateral erosion can occur when pools are partially filled and plunge-pool walls are exposed. Currently, there exists no mechanistic model describing sediment transport through waterfall plunge pools, limiting our ability to predict waterfall retreat. To address this knowledge gap, we performed detailed laboratory experiments measuring plunge-pool sediment transport capacity (Qsc_pool) under varying waterfall and plunge-pool geometries, flow hydraulics, and sediment size. Our experimental plunge-pool sediment transport capacity measurements match well with a mechanistic model we developed which combines existing waterfall jet theory with a modified Rouse profile to predict sediment transport capacity as a function of water discharge and suspended sediment concentration at the plunge-pool lip. Comparing the transport capacity of plunge pools to lower gradient portions of rivers (Qsc_river) shows that, for transport limited conditions, plunge pools fill with sediment under modest water discharges when Qsc_river > Qsc_pool, and empty to bedrock under high discharges when Qsc_pool > Qsc_river. These results are consistent with field observations of sand-filled plunge pools with downstream boulder rims, implying filling and excavation of plunge pools over single-storm timescales. Thus, partial filling of waterfall plunge pools may provide a mechanism to promote lateral undercutting and retreat of waterfalls in homogeneous rock in which plunge-pool vertical incision occurs during brief large floods that expose bedrock, whereas lateral erosion may prevail during smaller events.

Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology.

PubMed

MacLeod, Miles; Nersessian, Nancy J

2015-02-01

In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding." Copyright © 2014 Elsevier Ltd. All rights reserved.

Solving Immunology?

PubMed

Vodovotz, Yoram; Xia, Ashley; Read, Elizabeth L; Bassaganya-Riera, Josep; Hafler, David A; Sontag, Eduardo; Wang, Jin; Tsang, John S; Day, Judy D; Kleinstein, Steven H; Butte, Atul J; Altman, Matthew C; Hammond, Ross; Sealfon, Stuart C

2017-02-01

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies. Copyright © 2016 Elsevier Ltd. All rights reserved.

FASTGRASS: A mechanistic model for the prediction of Xe, I, Cs, Te, Ba, and Sr release from nuclear fuel under normal and severe-accident conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rest, J.; Zawadzki, S.A.

The primary physical/chemical models that form the basis of the FASTGRASS mechanistic computer model for calculating fission-product release from nuclear fuel are described. Calculated results are compared with test data and the major mechanisms affecting the transport of fission products during steady-state and accident conditions are identified.

Evolution of supersaturation of amorphous pharmaceuticals: the effect of rate of supersaturation generation.

PubMed

Sun, Dajun D; Lee, Ping I

2013-11-04

The combination of a rapidly dissolving and supersaturating "spring" with a precipitation retarding "parachute" has often been pursued as an effective formulation strategy for amorphous solid dispersions (ASDs) to enhance the rate and extent of oral absorption. However, the interplay between these two rate processes in achieving and maintaining supersaturation remains inadequately understood, and the effect of rate of supersaturation buildup on the overall time evolution of supersaturation during the dissolution of amorphous solids has not been explored. The objective of this study is to investigate the effect of supersaturation generation rate on the resulting kinetic solubility profiles of amorphous pharmaceuticals and to delineate the evolution of supersaturation from a mechanistic viewpoint. Experimental concentration-time curves under varying rates of supersaturation generation and recrystallization for model drugs, indomethacin (IND), naproxen (NAP) and piroxicam (PIR), were generated from infusing dissolved drug (e.g., in ethanol) into the dissolution medium and compared with that predicted from a comprehensive mechanistic model based on the classical nucleation theory taking into account both the particle growth and ripening processes. In the absence of any dissolved polymer to inhibit drug precipitation, both our experimental and predicted results show that the maximum achievable supersaturation (i.e., kinetic solubility) of the amorphous solids increases, the time to reach maximum decreases, and the rate of concentration decline in the de-supersaturation phase increases, with increasing rate of supersaturation generation (i.e., dissolution rate). Our mechanistic model also predicts the existence of an optimal supersaturation rate which maximizes the area under the curve (AUC) of the kinetic solubility concentration-time profile, which agrees well with experimental data. In the presence of a dissolved polymer from ASD dissolution, these observed trends also hold true except the de-supersaturation phase is more extended due to the crystallization inhibition effect. Since the observed kinetic solubility of nonequilibrium amorphous solids depends on the rate of supersaturation generation, our results also highlight the underlying difficulty in determining a reproducible solubility advantage for amorphous solids.

Modelling and observing the role of wind in Anopheles population dynamics around a reservoir.

PubMed

Endo, Noriko; Eltahir, Elfatih A B

2018-01-25

Wind conditions, as well as other environmental conditions, are likely to influence malaria transmission through the behaviours of Anopheles mosquitoes, especially around water-resource reservoirs. Wind-induced waves in a reservoir impose mortality on aquatic-stage mosquitoes. Mosquitoes' host-seeking activity is also influenced by wind through dispersion of [Formula: see text]. However, no malaria transmission model exists to date that simulated those impacts of wind mechanistically. A modelling framework for simulating the three important effects of wind on the behaviours of mosquito is developed: attraction of adult mosquitoes through dispersion of [Formula: see text] ([Formula: see text] attraction), advection of adult mosquitoes (advection), and aquatic-stage mortality due to wind-induced surface waves (waves). The framework was incorporated in a mechanistic malaria transmission simulator, HYDREMATS. The performance of the extended simulator was compared with the observed population dynamics of the Anopheles mosquitoes at a village adjacent to the Koka Reservoir in Ethiopia. The observed population dynamics of the Anopheles mosquitoes were reproduced with some reasonable accuracy in HYDREMATS that includes the representation of the wind effects. HYDREMATS without the wind model failed to do so. Offshore wind explained the increase in Anopheles population that cannot be expected from other environmental conditions alone. Around large water bodies such as reservoirs, the role of wind in the dynamics of Anopheles population, hence in malaria transmission, can be significant. Modelling the impacts of wind on the behaviours of Anopheles mosquitoes aids in reproducing the seasonality of malaria transmission and in estimation of the risk of malaria around reservoirs.

Modeling relations in nature and eco-informatics: a practical application of rosennean complexity.

PubMed

Kineman, John J

2007-10-01

The purpose of eco-informatics is to communicate critical information about organisms and ecosystems. To accomplish this, it must reflect the complexity of natural systems. Present information systems are designed around mechanistic concepts that do not capture complexity. Robert Rosen's relational theory offers a way of representing complexity in terms of information entailments that are part of an ontologically implicit 'modeling relation'. This relation has corresponding epistemological components that can be captured empirically, the components being structure (associated with model encoding) and function (associated with model decoding). Relational complexity, thus, provides a long-awaited theoretical underpinning for these concepts that ecology has found indispensable. Structural information pertains to the material organization of a system, which can be represented by data. Functional information specifies potential change, which can be inferred from experiment and represented as models or descriptions of state transformations. Contextual dependency (of structure or function) implies meaning. Biological functions imply internalized or system-dependent laws. Complexity can be represented epistemologically by relating structure and function in two different ways. One expresses the phenomenal relation that exists in any present or past instance, and the other draws the ontology of a system into the empirical world in terms of multiple potentials subject to natural forms of selection and optimality. These act as system attractors. Implementing these components and their theoretical relations in an informatics system will provide more-complete ecological informatics than is possible from a strictly mechanistic point of view. This approach will enable many new possibilities for supporting science and decision making.

A network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect

PubMed Central

2013-01-01

Background While the majority of studies have focused on the association between sex hormones and dementia, emerging evidence supports the role of other hormone signals in increasing dementia risk. However, due to the lack of an integrated view on mechanistic interactions of hormone signaling pathways associated with dementia, molecular mechanisms through which hormones contribute to the increased risk of dementia has remained unclear and capacity of translating hormone signals to potential therapeutic and diagnostic applications in relation to dementia has been undervalued. Methods Using an integrative knowledge- and data-driven approach, a global hormone interaction network in the context of dementia was constructed, which was further filtered down to a model of convergent hormone signaling pathways. This model was evaluated for its biological and clinical relevance through pathway recovery test, evidence-based analysis, and biomarker-guided analysis. Translational validation of the model was performed using the proposed novel mechanism discovery approach based on ‘serendipitous off-target effects’. Results Our results reveal the existence of a well-connected hormone interaction network underlying dementia. Seven hormone signaling pathways converge at the core of the hormone interaction network, which are shown to be mechanistically linked to the risk of dementia. Amongst these pathways, estrogen signaling pathway takes the major part in the model and insulin signaling pathway is analyzed for its association to learning and memory functions. Validation of the model through serendipitous off-target effects suggests that hormone signaling pathways substantially contribute to the pathogenesis of dementia. Conclusions The integrated network model of hormone interactions underlying dementia may serve as an initial translational platform for identifying potential therapeutic targets and candidate biomarkers for dementia-spectrum disorders such as Alzheimer’s disease. PMID:23885764

Mechanistic materials modeling for nuclear fuel performance

DOE PAGES

Tonks, Michael R.; Andersson, David; Phillpot, Simon R.; ...

2017-03-15

Fuel performance codes are critical tools for the design, certification, and safety analysis of nuclear reactors. However, their ability to predict fuel behavior under abnormal conditions is severely limited by their considerable reliance on empirical materials models correlated to burn-up (a measure of the number of fission events that have occurred, but not a unique measure of the history of the material). In this paper, we propose a different paradigm for fuel performance codes to employ mechanistic materials models that are based on the current state of the evolving microstructure rather than burn-up. In this approach, a series of statemore » variables are stored at material points and define the current state of the microstructure. The evolution of these state variables is defined by mechanistic models that are functions of fuel conditions and other state variables. The material properties of the fuel and cladding are determined from microstructure/property relationships that are functions of the state variables and the current fuel conditions. Multiscale modeling and simulation is being used in conjunction with experimental data to inform the development of these models. Finally, this mechanistic, microstructure-based approach has the potential to provide a more predictive fuel performance capability, but will require a team of researchers to complete the required development and to validate the approach.« less

Productivity of "Collisions Generate Heat" for Reconciling an Energy Model with Mechanistic Reasoning: A Case Study

ERIC Educational Resources Information Center

Scherr, Rachel E.; Robertson, Amy D.

2015-01-01

We observe teachers in professional development courses about energy constructing mechanistic accounts of energy transformations. We analyze a case in which teachers investigating adiabatic compression develop a model of the transformation of kinetic energy to thermal energy. Among their ideas is the idea that thermal energy is generated as a…

Fuel Performance Experiments and Modeling: Fission Gas Bubble Nucleation and Growth in Alloy Nuclear Fuels

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDeavitt, Sean; Shao, Lin; Tsvetkov, Pavel

2014-04-07

Advanced fast reactor systems being developed under the DOE's Advanced Fuel Cycle Initiative are designed to destroy TRU isotopes generated in existing and future nuclear energy systems. Over the past 40 years, multiple experiments and demonstrations have been completed using U-Zr, U-Pu-Zr, U-Mo and other metal alloys. As a result, multiple empirical and semi-empirical relationships have been established to develop empirical performance modeling codes. Many mechanistic questions about fission as mobility, bubble coalescience, and gas release have been answered through industrial experience, research, and empirical understanding. The advent of modern computational materials science, however, opens new doors of development suchmore » that physics-based multi-scale models may be developed to enable a new generation of predictive fuel performance codes that are not limited by empiricism.« less

The spatial structure of a nonlinear receptive field.

PubMed

Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred

2012-11-01

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

Recent developments in broadly applicable structure-biodegradability relationships.

PubMed

Jaworska, Joanna S; Boethling, Robert S; Howard, Philip H

2003-08-01

Biodegradation is one of the most important processes influencing concentration of a chemical substance after its release to the environment. It is the main process for removal of many chemicals from the environment and therefore is an important factor in risk assessments. This article reviews available methods and models for predicting biodegradability of organic chemicals from structure. The first section of the article briefly discusses current needs for biodegradability estimation methods related to new and existing chemicals and in the context of multimedia exposure models. Following sections include biodegradation test methods and endpoints used in modeling, with special attention given to the Japanese Ministry of International Trade and Industry test; a primer on modeling, describing the various approaches that have been used in the structure/biodegradability relationship work, and contrasting statistical and mechanistic approaches; and recent developments in structure/biodegradability relationships, divided into group contribution, chemometric, and artificial intelligence approaches.

Adverse outcome pathway (AOP) development II: Best practices

EPA Science Inventory

Organization of existing and emerging toxicological knowledge into adverse outcome pathway (AOP) descriptions can facilitate greater application of mechanistic data, including high throughput in vitro, high content omics and imaging, and biomarkers, in risk-based decision-making....

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

PubMed Central

Tsamandouras, Nikolaos; Rostami-Hodjegan, Amin; Aarons, Leon

2015-01-01

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance. PMID:24033787

Fundamental kinetics and mechanistic pathways for oxidation reactions in supercritical water

NASA Technical Reports Server (NTRS)

Webley, Paul A.; Tester, Jefferson W.

1988-01-01

Oxidation of the products of human metabolism in supercritical water has been shown to be an efficient way to accomplish the on-board water/waste recycling in future long-term space flights. Studies of the oxidation kinetics of methane to carbon dioxide in supercritical water are presented in this paper in order to enhance the fundamental understanding of the oxidation of human waste compounds in supercritical water. It is concluded that, although the elementary reaction models remain the best hope for simulating oxidation in supercritical water, several modifications to existing mechanisms need to be made to account for the role of water in the reaction mechanism.

Mechanistic modelling of cancer: some reflections from software engineering and philosophy of science.

PubMed

Cañete-Valdeón, José M; Wieringa, Roel; Smallbone, Kieran

2012-12-01

There is a growing interest in mathematical mechanistic modelling as a promising strategy for understanding tumour progression. This approach is accompanied by a methodological change of making research, in which models help to actively generate hypotheses instead of waiting for general principles to become apparent once sufficient data are accumulated. This paper applies recent research from philosophy of science to uncover three important problems of mechanistic modelling which may compromise its mainstream application, namely: the dilemma of formal and informal descriptions, the need to express degrees of confidence and the need of an argumentation framework. We report experience and research on similar problems from software engineering and provide evidence that the solutions adopted there can be transferred to the biological domain. We hope this paper can provoke new opportunities for further and profitable interdisciplinary research in the field.

Local calibration of the MEPDG for New Hampshire.

DOT National Transportation Integrated Search

2013-10-01

This report summarizes the UNH results of a study to calibrate the Mechanistic-Empirical Pavement : Design Guide (MEPDG) model for sites and conditions within New Hampshire. : MEPDG adds mechanistic understanding of material properties into methods f...

Draft user's guide for UDOT mechanistic-empirical pavement design.

DOT National Transportation Integrated Search

2009-10-01

Validation of the new AASHTO Mechanistic-Empirical Pavement Design Guides (MEPDG) nationally calibrated pavement distress and smoothness prediction models when applied under Utah conditions, and local calibration of the new hot-mix asphalt (HMA) p...

Novel in vitro and mathematical models for the prediction of chemical toxicity.

PubMed

Williams, Dominic P; Shipley, Rebecca; Ellis, Marianne J; Webb, Steve; Ward, John; Gardner, Iain; Creton, Stuart

2013-01-01

The focus of much scientific and medical research is directed towards understanding the disease process and defining therapeutic intervention strategies. The scientific basis of drug safety is very complex and currently remains poorly understood, despite the fact that adverse drug reactions (ADRs) are a major health concern and a serious impediment to development of new medicines. Toxicity issues account for ∼21% drug attrition during drug development and safety testing strategies require considerable animal use. Mechanistic relationships between drug plasma levels and molecular/cellular events that culminate in whole organ toxicity underpins development of novel safety assessment strategies. Current in vitro test systems are poorly predictive of toxicity of chemicals entering the systemic circulation, particularly to the liver. Such systems fall short because of (1) the physiological gap between cells currently used and human hepatocytes existing in their native state, (2) the lack of physiological integration with other cells/systems within organs, required to amplify the initial toxicological lesion into overt toxicity, (3) the inability to assess how low level cell damage induced by chemicals may develop into overt organ toxicity in a minority of patients, (4) lack of consideration of systemic effects. Reproduction of centrilobular and periportal hepatocyte phenotypes in in vitro culture is crucial for sensitive detection of cellular stress. Hepatocyte metabolism/phenotype is dependent on cell position along the liver lobule, with corresponding differences in exposure to substrate, oxygen and hormone gradients. Application of bioartificial liver (BAL) technology can encompass in vitro predictive toxicity testing with enhanced sensitivity and improved mechanistic understanding. Combining this technology with mechanistic mathematical models describing intracellular metabolism, fluid-flow, substrate, hormone and nutrient distribution provides the opportunity to design the BAL specifically to mimic the in vivo scenario. Such mathematical models enable theoretical hypothesis testing, will inform the design of in vitro experiments, and will enable both refinement and reduction of in vivo animal trials. In this way, development of novel mathematical modelling tools will help to focus and direct in vitro and in vivo research, and can be used as a framework for other areas of drug safety science.

Novel in vitro and mathematical models for the prediction of chemical toxicity

PubMed Central

Shipley, Rebecca; Ellis, Marianne J.; Webb, Steve; Ward, John; Gardner, Iain; Creton, Stuart

2013-01-01

The focus of much scientific and medical research is directed towards understanding the disease process and defining therapeutic intervention strategies. The scientific basis of drug safety is very complex and currently remains poorly understood, despite the fact that adverse drug reactions (ADRs) are a major health concern and a serious impediment to development of new medicines. Toxicity issues account for ∼21% drug attrition during drug development and safety testing strategies require considerable animal use. Mechanistic relationships between drug plasma levels and molecular/cellular events that culminate in whole organ toxicity underpins development of novel safety assessment strategies. Current in vitro test systems are poorly predictive of toxicity of chemicals entering the systemic circulation, particularly to the liver. Such systems fall short because of (1) the physiological gap between cells currently used and human hepatocytes existing in their native state, (2) the lack of physiological integration with other cells/systems within organs, required to amplify the initial toxicological lesion into overt toxicity, (3) the inability to assess how low level cell damage induced by chemicals may develop into overt organ toxicity in a minority of patients, (4) lack of consideration of systemic effects. Reproduction of centrilobular and periportal hepatocyte phenotypes in in vitro culture is crucial for sensitive detection of cellular stress. Hepatocyte metabolism/phenotype is dependent on cell position along the liver lobule, with corresponding differences in exposure to substrate, oxygen and hormone gradients. Application of bioartificial liver (BAL) technology can encompass in vitro predictive toxicity testing with enhanced sensitivity and improved mechanistic understanding. Combining this technology with mechanistic mathematical models describing intracellular metabolism, fluid-flow, substrate, hormone and nutrient distribution provides the opportunity to design the BAL specifically to mimic the in vivo scenario. Such mathematical models enable theoretical hypothesis testing, will inform the design of in vitro experiments, and will enable both refinement and reduction of in vivo animal trials. In this way, development of novel mathematical modelling tools will help to focus and direct in vitro and in vivo research, and can be used as a framework for other areas of drug safety science. PMID:26966512

The physicochemical process of bacterial attachment to abiotic surfaces: Challenges for mechanistic studies, predictability and the development of control strategies.

PubMed

Wang, Yi; Lee, Sui Mae; Dykes, Gary

2015-01-01

Bacterial attachment to abiotic surfaces can be explained as a physicochemical process. Mechanisms of the process have been widely studied but are not yet well understood due to their complexity. Physicochemical processes can be influenced by various interactions and factors in attachment systems, including, but not limited to, hydrophobic interactions, electrostatic interactions and substratum surface roughness. Mechanistic models and control strategies for bacterial attachment to abiotic surfaces have been established based on the current understanding of the attachment process and the interactions involved. Due to a lack of process control and standardization in the methodologies used to study the mechanisms of bacterial attachment, however, various challenges are apparent in the development of models and control strategies. In this review, the physicochemical mechanisms, interactions and factors affecting the process of bacterial attachment to abiotic surfaces are described. Mechanistic models established based on these parameters are discussed in terms of their limitations. Currently employed methods to study these parameters and bacterial attachment are critically compared. The roles of these parameters in the development of control strategies for bacterial attachment are reviewed, and the challenges that arise in developing mechanistic models and control strategies are assessed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jardine, Kolby

In conjunction with the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility GoAmazon campaign, the Terrestrial Ecosystem Science (TES)-funded Green Ocean Amazon (GoAmazon 2014/15) terrestrial ecosystem project (Geco) was designed to: • evaluate the strengths and weaknesses of leaf-level algorithms for biogenic volatile organic compounds (BVOCs) emissions in Amazon forests near Manaus, Brazil, and • conduct mechanistic field studies to characterize biochemical and physiological processes governing leaf- and landscape-scale tropical forest BVOC emissions, and the influence of environmental drivers that are expected to change with a warming climate. Through a close interaction between modeling and observationalmore » activities, including the training of MS and PhD graduate students, post-doctoral students, and technicians at the National Institute for Amazon Research (INPA), the study aimed at improving the representation of BVOC-mediated biosphere-atmosphere interactions and feedbacks under a warming climate. BVOCs can form cloud condensation nuclei (CCN) that influence precipitation dynamics and modify the quality of down welling radiation for photosynthesis. However, our ability to represent these coupled biosphere-atmosphere processes in Earth system models suffers from poor understanding of the functions, identities, quantities, and seasonal patterns of BVOC emissions from tropical forests as well as their biological and environmental controls. The Model of Emissions of Gases and Aerosols from Nature (MEGAN), the current BVOC sub-model of the Community Earth System Model (CESM), was evaluated to explore mechanistic controls over BVOC emissions. Based on that analysis, a combination of observations and experiments were studied in forests near Manaus, Brazil, to test existing parameterizations and algorithm structures in MEGAN. The model was actively modified as needed to improve tropical BVOC emission simulations on a regional scale.« less

Mechanistic Links Between PARP, NAD, and Brain Inflammation After TBI

DTIC Science & Technology

2015-10-01

1 AWARD NUMBER: W81XWH-13-2-0091 TITLE: Mechanistic Links Between PARP, NAD , and Brain Inflammation After TBI PRINCIPAL INVESTIGATOR...COVERED 25 Sep 2014 - 24 Sep 2015 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Mechanistic Links Between PARP, NAD , and Brain Inflammation After TBI 5b. GRANT...efficacy of veliparib and NAD as agents for suppressing inflammation and improving outcomes after traumatic brain injury. The animal models include

Worldview and health promoting behavior: a causal model.

PubMed

Kagee, A; Dixon, D N

2000-04-01

The present study investigated the manner in which Pepper's (1942) worldview theory relates to health promoting behavior. A sample of 259 subjects completed a battery of inventories measuring worldview, health promoting behavior (HPB), social class, and sex. The data were analyzed by means of structural equation modeling using the statistical program for the social sciences (SPSS) and the analysis of moment and structure (AMOS) computer programs. The results support the idea that a modest relationship exists between worldview and HPB, with organismic thinkers more likely than mechanistic thinkers to engage in HPB. There was also a slight indirect effect of sex on worldview and HPB, with women more likely to endorse an organismic worldview and therefore more likely to engage in HPB than men. No relationship was found between socioeconomic status and HPB.

Theories of Memory and Aging: A Look at the Past and a Glimpse of the Future

PubMed Central

Festini, Sara B.

2017-01-01

The present article reviews theories of memory and aging over the past 50 years. Particularly notable is a progression from early single-mechanism perspectives to complex multifactorial models proposed to account for commonly observed age deficits in memory function. The seminal mechanistic theories of processing speed, limited resources, and inhibitory deficits are discussed and viewed as especially important theories for understanding age-related memory decline. Additionally, advances in multivariate techniques including structural equation modeling provided new tools that led to the development of more complex multifactorial theories than existed earlier. The important role of neuroimaging is considered, along with the current prevalence of intervention studies. We close with predictions about new directions that future research on memory and aging will take. PMID:27257229

Mechanistic equivalent circuit modelling of a commercial polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Giner-Sanz, J. J.; Ortega, E. M.; Pérez-Herranz, V.

2018-03-01

Electrochemical impedance spectroscopy (EIS) has been widely used in the fuel cell field since it allows deconvolving the different physic-chemical processes that affect the fuel cell performance. Typically, EIS spectra are modelled using electric equivalent circuits. In this work, EIS spectra of an individual cell of a commercial PEM fuel cell stack were obtained experimentally. The goal was to obtain a mechanistic electric equivalent circuit in order to model the experimental EIS spectra. A mechanistic electric equivalent circuit is a semiempirical modelling technique which is based on obtaining an equivalent circuit that does not only correctly fit the experimental spectra, but which elements have a mechanistic physical meaning. In order to obtain the aforementioned electric equivalent circuit, 12 different models with defined physical meanings were proposed. These equivalent circuits were fitted to the obtained EIS spectra. A 2 step selection process was performed. In the first step, a group of 4 circuits were preselected out of the initial list of 12, based on general fitting indicators as the determination coefficient and the fitted parameter uncertainty. In the second step, one of the 4 preselected circuits was selected on account of the consistency of the fitted parameter values with the physical meaning of each parameter.

A mechanistic model (BCC-PSSICO) to predict changes in the hydraulic properties for bio-amended variably saturated soils

NASA Astrophysics Data System (ADS)

Carles Brangarí, Albert; Sanchez-Vila, Xavier; Freixa, Anna; M. Romaní, Anna; Rubol, Simonetta; Fernà ndez-Garcia, Daniel

2017-01-01

The accumulation of biofilms in porous media is likely to influence the overall hydraulic properties and, consequently, a sound understanding of the process is required for the proper design and management of many technological applications. In order to bring some light into this phenomenon we present a mechanistic model to study the variably saturated hydraulic properties of bio-amended soils. Special emphasis is laid on the distribution of phases at pore-scale and the mechanisms to retain and let water flow through, providing valuable insights into phenomena behind bioclogging. Our approach consists in modeling the porous media as an ensemble of capillary tubes, obtained from the biofilm-free water retention curve. This methodology is extended by the incorporation of a biofilm composed of bacterial cells and extracellular polymeric substances (EPS). Moreover, such a microbial consortium displays a channeled geometry that shrinks/swells with suction. Analytical equations for the volumetric water content and the relative permeability can then be derived by assuming that biomass reshapes the pore space following specific geometrical patterns. The model is discussed by using data from laboratory studies and other approaches already existing in the literature. It can reproduce (i) displacements of the retention curve toward higher saturations and (ii) permeability reductions of distinct orders of magnitude. Our findings also illustrate how even very small amounts of biofilm may lead to significant changes in the hydraulic properties. We, therefore, state the importance of accounting for the hydraulic characteristics of biofilms and for a complex/more realistic geometry of colonies at the pore-scale.

Sediment transport through self-adjusting, bedrock-walled waterfall plunge pools

NASA Astrophysics Data System (ADS)

Scheingross, Joel S.; Lamb, Michael P.

2016-05-01

Many waterfalls have deep plunge pools that are often partially or fully filled with sediment. Sediment fill may control plunge-pool bedrock erosion rates, partially determine habitat availability for aquatic organisms, and affect sediment routing and debris flow initiation. Currently, there exists no mechanistic model to describe sediment transport through waterfall plunge pools. Here we develop an analytical model to predict steady-state plunge-pool depth and sediment-transport capacity by combining existing jet theory with sediment transport mechanics. Our model predicts plunge-pool sediment-transport capacity increases with increasing river discharge, flow velocity, and waterfall drop height and decreases with increasing plunge-pool depth, radius, and grain size. We tested the model using flume experiments under varying waterfall and plunge-pool geometries, flow hydraulics, and sediment size. The model and experiments show that through morphodynamic feedbacks, plunge pools aggrade to reach shallower equilibrium pool depths in response to increases in imposed sediment supply. Our theory for steady-state pool depth matches the experiments with an R2 value of 0.8, with discrepancies likely due to model simplifications of the hydraulics and sediment transport. Analysis of 75 waterfalls suggests that the water depths in natural plunge pools are strongly influenced by upstream sediment supply, and our model provides a mass-conserving framework to predict sediment and water storage in waterfall plunge pools for sediment routing, habitat assessment, and bedrock erosion modeling.

Computational Modeling of Cobalt-Based Water Oxidation: Current Status and Future Challenges

PubMed Central

Schilling, Mauro; Luber, Sandra

2018-01-01

A lot of effort is nowadays put into the development of novel water oxidation catalysts. In this context, mechanistic studies are crucial in order to elucidate the reaction mechanisms governing this complex process, new design paradigms and strategies how to improve the stability and efficiency of those catalysts. This review is focused on recent theoretical mechanistic studies in the field of homogeneous cobalt-based water oxidation catalysts. In the first part, computational methodologies and protocols are summarized and evaluated on the basis of their applicability toward real catalytic or smaller model systems, whereby special emphasis is laid on the choice of an appropriate model system. In the second part, an overview of mechanistic studies is presented, from which conceptual guidelines are drawn on how to approach novel studies of catalysts and how to further develop the field of computational modeling of water oxidation reactions. PMID:29721491

Computational Modeling of Cobalt-based Water Oxidation: Current Status and Future Challenges

NASA Astrophysics Data System (ADS)

Schilling, Mauro; Luber, Sandra

2018-04-01

A lot of effort is nowadays put into the development of novel water oxidation catalysts. In this context mechanistic studies are crucial in order to elucidate the reaction mechanisms governing this complex process, new design paradigms and strategies how to improve the stability and efficiency of those catalysis. This review is focused on recent theoretical mechanistic studies in the field of homogeneous cobalt-based water oxidation catalysts. In the first part, computational methodologies and protocols are summarized and evaluated on the basis of their applicability towards real catalytic or smaller model systems, whereby special emphasis is laid on the choice of an appropriate model system. In the second part, an overview of mechanistic studies is presented, from which conceptual guidelines are drawn on how to approach novel studies of catalysts and how to further develop the field of computational modeling of water oxidation reactions.

BIOB: a mathematical model for the biodegradation of low solubility hydrocarbons.

PubMed

Geng, Xiaolong; Boufadel, Michel C; Personna, Yves R; Lee, Ken; Tsao, David; Demicco, Erik D

2014-06-15

Modeling oil biodegradation is an important step in predicting the long term fate of oil on beaches. Unfortunately, existing models do not account mechanistically for environmental factors, such as pore water nutrient concentration, affecting oil biodegradation, rather in an empirical way. We present herein a numerical model, BIOB, to simulate the biodegradation of insoluble attached hydrocarbon. The model was used to simulate an experimental oil spill on a sand beach. The biodegradation kinetic parameters were estimated by fitting the model to the experimental data of alkanes and aromatics. It was found that parameter values are comparable to their counterparts for the biodegradation of dissolved organic matter. The biodegradation of aromatics was highly affected by the decay of aromatic biomass, probably due to its low growth rate. Numerical simulations revealed that the biodegradation rate increases by 3-4 folds when the nutrient concentration is increased from 0.2 to 2.0 mg N/L. Published by Elsevier Ltd.

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story.

PubMed

Emami Riedmaier, Arian; Lindley, David J; Hall, Jeffrey A; Castleberry, Steven; Slade, Russell T; Stuart, Patricia; Carr, Robert A; Borchardt, Thomas B; Bow, Daniel A J; Nijsen, Marjoleen

2018-01-01

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Validation of pavement performance curves for the mechanistic-empirical pavement design guide.

DOT National Transportation Integrated Search

2009-02-01

The objective of this research is to determine whether the nationally calibrated performance models used in the Mechanistic-Empirical : Pavement Design Guide (MEPDG) provide a reasonable prediction of actual field performance, and if the desired accu...

Acquisition of a high-quality temperature chamber

DOT National Transportation Integrated Search

2008-05-01

With the Missouri Department of Transportation (MoDOT) beginning to implement the new Mechanistic-Empirical (M-E) Design Guide for New and Rehabilitated Pavements, the need exists for various types of testing of hot-mix asphalt (HMA) mixes used by Mo...

Mechanistic-empirical asphalt overlay thickness design and analysis system.

DOT National Transportation Integrated Search

2009-10-01

The placement of an asphalt overlay is the most common method used by the Texas Department of Transportation (TxDOT) to rehabilitate : existing asphalt and concrete pavements. The type of overlay and its required thickness are important decisions tha...

When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world

Treesearch

Eric J. Gustafson

2013-01-01

Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical)...

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

DTIC Science & Technology

2015-03-03

whether we could simultaneously estimate kinetic parameters and regulatory connectivity, in the absence of specific mechanistic knowledge , from synthetic...that manage metabolism. Of course, these issues are not independent; any description of enzyme activity regulation will be a function of system state...the absence of specific mechanistic knowledge , from synthetic experimental data. Toward these questions, we explored five hypothetical cell-free

A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions.

PubMed

Zhu, Qing; Riley, William J; Tang, Jinyun

2017-04-01

Terrestrial plants assimilate anthropogenic CO 2 through photosynthesis and synthesizing new tissues. However, sustaining these processes requires plants to compete with microbes for soil nutrients, which therefore calls for an appropriate understanding and modeling of nutrient competition mechanisms in Earth System Models (ESMs). Here, we survey existing plant-microbe competition theories and their implementations in ESMs. We found no consensus regarding the representation of nutrient competition and that observational and theoretical support for current implementations are weak. To reconcile this situation, we applied the Equilibrium Chemistry Approximation (ECA) theory to plant-microbe nitrogen competition in a detailed grassland 15 N tracer study and found that competition theories in current ESMs fail to capture observed patterns and the ECA prediction simplifies the complex nature of nutrient competition and quantitatively matches the 15 N observations. Since plant carbon dynamics are strongly modulated by soil nutrient acquisition, we conclude that (1) predicted nutrient limitation effects on terrestrial carbon accumulation by existing ESMs may be biased and (2) our ECA-based approach may improve predictions by mechanistically representing plant-microbe nutrient competition. © 2016 by the Ecological Society of America.

Rational and mechanistic perspectives on reinforcement learning.

PubMed

Chater, Nick

2009-12-01

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: mechanistic and rational. Reinforcement learning is often viewed in mechanistic terms--as describing the operation of aspects of an agent's cognitive and neural machinery. Yet it can also be viewed as a rational level of description, specifically, as describing a class of methods for learning from experience, using minimal background knowledge. This paper considers how rational and mechanistic perspectives differ, and what types of evidence distinguish between them. Reinforcement learning research in the cognitive and brain sciences is often implicitly committed to the mechanistic interpretation. Here the opposite view is put forward: that accounts of reinforcement learning should apply at the rational level, unless there is strong evidence for a mechanistic interpretation. Implications of this viewpoint for reinforcement-based theories in the cognitive and brain sciences are discussed.

Application of synthetic scenarios to address water resource concerns: A management-guided case study from the Upper Colorado River Basin

USGS Publications Warehouse

McAfee, Stephanie A.; Pederson, Gregory T.; Woodhouse, Connie A.; McCabe, Gregory

2017-01-01

Water managers are increasingly interested in better understanding and planning for projected resource impacts from climate change. In this management-guided study, we use a very large suite of synthetic climate scenarios in a statistical modeling framework to simultaneously evaluate how (1) average temperature and precipitation changes, (2) initial basin conditions, and (3) temporal characteristics of the input climate data influence water-year flow in the Upper Colorado River. The results here suggest that existing studies may underestimate the degree of uncertainty in future streamflow, particularly under moderate temperature and precipitation changes. However, we also find that the relative severity of future flow projections within a given climate scenario can be estimated with simple metrics that characterize the input climate data and basin conditions. These results suggest that simple testing, like the analyses presented in this paper, may be helpful in understanding differences between existing studies or in identifying specific conditions for physically based mechanistic modeling. Both options could reduce overall cost and improve the efficiency of conducting climate change impacts studies.

Modelling the economic impact of three lameness causing diseases using herd and cow level evidence.

PubMed

Ettema, Jehan; Østergaard, Søren; Kristensen, Anders Ringgaard

2010-06-01

Diseases to the cow's hoof, interdigital skin and legs are highly prevalent and of large economic impact in modern dairy farming. In order to support farmer's decisions on preventing and treating lameness and its underlying causes, decision support models can be used to predict the economic profitability of such actions. An existing approach of modelling lameness as one health disorder in a dynamic, stochastic and mechanistic simulation model has been improved in two ways. First of all, three underlying diseases causing lameness were modelled: digital dermatitis, interdigital hyperplasia and claw horn diseases. Secondly, the existing simulation model was set-up in way that it uses hyper-distributions describing diseases risk of the three lameness causing diseases. By combining information on herd level risk factors with prevalence of lameness or prevalence of underlying diseases among cows, marginal posterior probability distributions for disease prevalence in the specific herd are created in a Bayesian network. Random draws from these distributions are used by the simulation model to describe disease risk. Hereby field data on prevalence is used systematically and uncertainty around herd specific risk is represented. Besides the fact that estimated profitability of halving disease risk depended on the hyper-distributions used, the estimates differed for herds with different levels of diseases risk and reproductive efficiency. (c) 2010 Elsevier B.V. All rights reserved.

Assessing the Role of Climate Variability on Liver Fluke Risk in the UK Through Mechanistic Hydro-Epidemiological Modelling

NASA Astrophysics Data System (ADS)

Beltrame, L.; Dunne, T.; Rose, H.; Walker, J.; Morgan, E.; Vickerman, P.; Wagener, T.

2016-12-01

Liver fluke is a flatworm parasite infecting grazing animals worldwide. In the UK, it causes considerable production losses to cattle and sheep industries and costs farmers millions of pounds each year due to reduced growth rates and lower milk yields. Large part of the parasite life-cycle takes place outside of the host, with its survival and development strongly controlled by climatic and hydrologic conditions. Evidence of climate-driven changes in the distribution and seasonality of fluke disease already exists, as the infection is increasingly expanding to new areas and becoming a year-round problem. Therefore, it is crucial to assess current and potential future impacts of climate variability on the disease to guide interventions at the farm scale and mitigate risk. Climate-based fluke risk models have been available since the 1950s, however, they are based on empirical relationships derived between historical climate and incidence data, and thus are unlikely to be robust for simulating risk under changing conditions. Moreover, they are not dynamic, but estimate risk over large regions in the UK based on monthly average climate conditions, so they do not allow investigating the effects of climate variability for supporting farmers' decisions. In this study, we introduce a mechanistic model for fluke, which represents habitat suitability for disease development at 25m resolution with a daily time step, explicitly linking the parasite life-cycle to key hydro-climate conditions. The model is used on a case study in the UK and sensitivity analysis is performed to better understand the role of climate variability on the space-time dynamics of the disease, while explicitly accounting for uncertainties. Comparisons are presented with experts' knowledge and a widely used empirical model.

Evaluating the environmental fate of short-chain chlorinated paraffins (SCCPs) in the Nordic environment using a dynamic multimedia model.

PubMed

Krogseth, Ingjerd S; Breivik, Knut; Arnot, Jon A; Wania, Frank; Borgen, Anders R; Schlabach, Martin

2013-12-01

Short chain chlorinated paraffins (SCCPs) raise concerns due to their potential for persistence, bioaccumulation, long-range transport and adverse effects. An understanding of their environmental fate remains limited, partly due to the complexity of the mixture. The purpose of this study was to evaluate whether a mechanistic, integrated, dynamic environmental fate and bioaccumulation multimedia model (CoZMoMAN) can reconcile what is known about environmental emissions and human exposure of SCCPs in the Nordic environment. Realistic SCCP emission scenarios, resolved by formula group, were estimated and used to predict the composition and concentrations of SCCPs in the environment and the human food chain. Emissions at the upper end of the estimated range resulted in predicted total concentrations that were often within a factor of 6 of observations. Similar model performance for a complex group of organic contaminants as for the well-known polychlorinated biphenyls strengthens the confidence in the CoZMoMAN model and implies a relatively good mechanistic understanding of the environmental fate of SCCPs. However, the degree of chlorination predicted for SCCPs in sediments, fish, and humans was higher than observed and poorly established environmental half-lives and biotransformation rate constants contributed to the uncertainties in the predicted composition and ∑SCCP concentrations. Improving prediction of the SCCP composition will also require better constrained estimates of the composition of SCCP emissions. There is, however, also large uncertainty and lack of coherence in the existing observations, and better model-measurement agreement will require improved analytical methods and more strategic sampling. More measurements of SCCP levels and compositions in samples from background regions are particularly important.

Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations.

PubMed

Hu, Eric Y; Bouteiller, Jean-Marie C; Song, Dong; Baudry, Michel; Berger, Theodore W

2015-01-01

Chemical synapses are comprised of a wide collection of intricate signaling pathways involving complex dynamics. These mechanisms are often reduced to simple spikes or exponential representations in order to enable computer simulations at higher spatial levels of complexity. However, these representations cannot capture important nonlinear dynamics found in synaptic transmission. Here, we propose an input-output (IO) synapse model capable of generating complex nonlinear dynamics while maintaining low computational complexity. This IO synapse model is an extension of a detailed mechanistic glutamatergic synapse model capable of capturing the input-output relationships of the mechanistic model using the Volterra functional power series. We demonstrate that the IO synapse model is able to successfully track the nonlinear dynamics of the synapse up to the third order with high accuracy. We also evaluate the accuracy of the IO synapse model at different input frequencies and compared its performance with that of kinetic models in compartmental neuron models. Our results demonstrate that the IO synapse model is capable of efficiently replicating complex nonlinear dynamics that were represented in the original mechanistic model and provide a method to replicate complex and diverse synaptic transmission within neuron network simulations.

Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations

PubMed Central

Hu, Eric Y.; Bouteiller, Jean-Marie C.; Song, Dong; Baudry, Michel; Berger, Theodore W.

2015-01-01

Chemical synapses are comprised of a wide collection of intricate signaling pathways involving complex dynamics. These mechanisms are often reduced to simple spikes or exponential representations in order to enable computer simulations at higher spatial levels of complexity. However, these representations cannot capture important nonlinear dynamics found in synaptic transmission. Here, we propose an input-output (IO) synapse model capable of generating complex nonlinear dynamics while maintaining low computational complexity. This IO synapse model is an extension of a detailed mechanistic glutamatergic synapse model capable of capturing the input-output relationships of the mechanistic model using the Volterra functional power series. We demonstrate that the IO synapse model is able to successfully track the nonlinear dynamics of the synapse up to the third order with high accuracy. We also evaluate the accuracy of the IO synapse model at different input frequencies and compared its performance with that of kinetic models in compartmental neuron models. Our results demonstrate that the IO synapse model is capable of efficiently replicating complex nonlinear dynamics that were represented in the original mechanistic model and provide a method to replicate complex and diverse synaptic transmission within neuron network simulations. PMID:26441622

The attention schema theory: a mechanistic account of subjective awareness

PubMed Central

Graziano, Michael S. A.; Webb, Taylor W.

2015-01-01

We recently proposed the attention schema theory, a novel way to explain the brain basis of subjective awareness in a mechanistic and scientifically testable manner. The theory begins with attention, the process by which signals compete for the brain’s limited computing resources. This internal signal competition is partly under a bottom–up influence and partly under top–down control. We propose that the top–down control of attention is improved when the brain has access to a simplified model of attention itself. The brain therefore constructs a schematic model of the process of attention, the ‘attention schema,’ in much the same way that it constructs a schematic model of the body, the ‘body schema.’ The content of this internal model leads a brain to conclude that it has a subjective experience. One advantage of this theory is that it explains how awareness and attention can sometimes become dissociated; the brain’s internal models are never perfect, and sometimes a model becomes dissociated from the object being modeled. A second advantage of this theory is that it explains how we can be aware of both internal and external events. The brain can apply attention to many types of information including external sensory information and internal information about emotions and cognitive states. If awareness is a model of attention, then this model should pertain to the same domains of information to which attention pertains. A third advantage of this theory is that it provides testable predictions. If awareness is the internal model of attention, used to help control attention, then without awareness, attention should still be possible but should suffer deficits in control. In this article, we review the existing literature on the relationship between attention and awareness, and suggest that at least some of the predictions of the theory are borne out by the evidence. PMID:25954242

Modeling of Mn/Road test sections with the CRREL mechanistic pavement design procedure

DOT National Transportation Integrated Search

1996-09-01

The U.S. Army Cold Regions Research and Engineering Laboratory is developing a mechanistic pavement design procedure for use in seasonal frost areas. The procedure was used to predict pavement performance of some test sections under construction at t...

Understanding the effect of carbon status on stem diameter variations

PubMed Central

De Swaef, Tom; Driever, Steven M.; Van Meulebroek, Lieven; Vanhaecke, Lynn; Marcelis, Leo F. M.; Steppe, Kathy

2013-01-01

Background Carbon assimilation and leaf-to-fruit sugar transport are, along with plant water status, the driving mechanisms for fruit growth. An integrated comprehension of the plant water and carbon relationships is therefore essential to better understand water and dry matter accumulation. Variations in stem diameter result from an integrated response to plant water and carbon status and are as such a valuable source of information. Methods A mechanistic water flow and storage model was used to relate variations in stem diameter to phloem sugar loading and sugar concentration dynamics in tomato. The simulation results were compared with an independent model, simulating phloem sucrose loading at the leaf level based on photosynthesis and sugar metabolism kinetics and enabled a mechanistic interpretation of the ‘one common assimilate pool’ concept for tomato. Key Results Combining stem diameter variation measurements and mechanistic modelling allowed us to distinguish instantaneous dynamics in the plant water relations and gradual variations in plant carbon status. Additionally, the model combined with stem diameter measurements enabled prediction of dynamic variables which are difficult to measure in a continuous and non-destructive way, such as xylem water potential and phloem hydrostatic potential. Finally, dynamics in phloem sugar loading and sugar concentration were distilled from stem diameter variations. Conclusions Stem diameter variations, when used in mechanistic models, have great potential to continuously monitor and interpret plant water and carbon relations under natural growing conditions. PMID:23186836

Fortune favours the brave: Movement responses shape demographic dynamics in strongly competing populations.

PubMed

Potts, Jonathan R; Petrovskii, Sergei V

2017-05-07

Animal movement is a key mechanism for shaping population dynamics. The effect of interactions between competing animals on a population's survival has been studied for many decades. However, interactions also affect an animal's subsequent movement decisions. Despite this, the indirect effect of these decisions on animal survival is much less well-understood. Here, we incorporate movement responses to foreign animals into a model of two competing populations, where inter-specific competition is greater than intra-specific competition. When movement is diffusive, the travelling wave moves from the stronger population to the weaker. However, by incorporating behaviourally induced directed movement towards the stronger population, the weaker one can slow the travelling wave down, even reversing its direction. Hence movement responses can switch the predictions of traditional mechanistic models. Furthermore, when environmental heterogeneity is combined with aggressive movement strategies, it is possible for spatially segregated co-existence to emerge. In this situation, the spatial patterns of the competing populations have the unusual feature that they are slightly out-of-phase with the environmental patterns. Finally, incorporating dynamic movement responses can also enable stable co-existence in a homogeneous environment, giving a new mechanism for spatially segregated co-existence. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current understanding of the human microbiome.

PubMed

Gilbert, Jack A; Blaser, Martin J; Caporaso, J Gregory; Jansson, Janet K; Lynch, Susan V; Knight, Rob

2018-04-10

Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities that are associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes and by mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this review, we focus on studies in humans to describe these challenges and propose strategies that leverage existing knowledge to move rapidly from correlation to causation and ultimately to translation into therapies.

How electrostatic networks modulate specificity and stability of collagen.

PubMed

Zheng, Hongning; Lu, Cheng; Lan, Jun; Fan, Shilong; Nanda, Vikas; Xu, Fei

2018-06-12

One-quarter of the 28 types of natural collagen exist as heterotrimers. The oligomerization state of collagen affects the structure and mechanics of the extracellular matrix, providing essential cues to modulate biological and pathological processes. A lack of high-resolution structural information limits our mechanistic understanding of collagen heterospecific self-assembly. Here, the 1.77-Å resolution structure of a synthetic heterotrimer demonstrates the balance of intermolecular electrostatics and hydrogen bonding that affects collagen stability and heterospecificity of assembly. Atomistic simulations and mutagenesis based on the solved structure are used to explore the contributions of specific interactions to energetics. A predictive model of collagen stability and specificity is developed for engineering novel collagen structures.

Current understanding of the human microbiome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Jack A.; Blaser, Martin J.; Caporaso, J. Gregory

Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledgemore » to move rapidly from correlation to causation, and ultimately to translation.« less

Self-Organized Criticality, Plasticity and Sensorimotor Coupling. Explorations with a Neurorobotic Model in a Behavioural Preference Task

PubMed Central

Aguilera, Miguel; Barandiaran, Xabier E.; Bedia, Manuel G.; Seron, Francisco

2015-01-01

During the last two decades, analysis of 1/ƒ noise in cognitive science has led to a considerable progress in the way we understand the organization of our mental life. However, there is still a lack of specific models providing explanations of how 1/ƒ noise is generated in coupled brain-body-environment systems, since existing models and experiments typically target either externally observable behaviour or isolated neuronal systems but do not address the interplay between neuronal mechanisms and sensorimotor dynamics. We present a conceptual model of a minimal neurorobotic agent solving a behavioural task that makes it possible to relate mechanistic (neurodynamic) and behavioural levels of description. The model consists of a simulated robot controlled by a network of Kuramoto oscillators with homeostatic plasticity and the ability to develop behavioural preferences mediated by sensorimotor patterns. With only three oscillators, this simple model displays self-organized criticality in the form of robust 1/ƒ noise and a wide multifractal spectrum. We show that the emergence of self-organized criticality and 1/ƒ noise in our model is the result of three simultaneous conditions: a) non-linear interaction dynamics capable of generating stable collective patterns, b) internal plastic mechanisms modulating the sensorimotor flows, and c) strong sensorimotor coupling with the environment that induces transient metastable neurodynamic regimes. We carry out a number of experiments to show that both synaptic plasticity and strong sensorimotor coupling play a necessary role, as constituents of self-organized criticality, in the generation of 1/ƒ noise. The experiments also shown to be useful to test the robustness of 1/ƒ scaling comparing the results of different techniques. We finally discuss the role of conceptual models as mediators between nomothetic and mechanistic models and how they can inform future experimental research where self-organized critically includes sensorimotor coupling among the essential interaction-dominant process giving rise to 1/ƒ noise. PMID:25706744

Nipah virion entry kinetics, composition, and conformational changes determined by enzymatic virus-like particles and new flow virometry tools.

PubMed

Landowski, Matthew; Dabundo, Jeffrey; Liu, Qian; Nicola, Anthony V; Aguilar, Hector C

2014-12-01

Virus-cell membrane fusion is essential for enveloped virus infections. However, mechanistic viral membrane fusion studies have predominantly focused on cell-cell fusion models, largely due to the low availability of technologies capable of characterizing actual virus-cell membrane fusion. Although cell-cell fusion assays are valuable, they do not fully recapitulate all the variables of virus-cell membrane fusion. Drastic differences between viral and cellular membrane lipid and protein compositions and curvatures exist. For biosafety level 4 (BSL4) pathogens such as the deadly Nipah virus (NiV), virus-cell fusion mechanistic studies are notably cumbersome. To circumvent these limitations, we used enzymatic Nipah virus-like-particles (NiVLPs) and developed new flow virometric tools. NiV's attachment (G) and fusion (F) envelope glycoproteins mediate viral binding to the ephrinB2/ephrinB3 cell receptors and virus-cell membrane fusion, respectively. The NiV matrix protein (M) can autonomously induce NiV assembly and budding. Using a β-lactamase (βLa) reporter/NiV-M chimeric protein, we produced NiVLPs expressing NiV-G and wild-type or mutant NiV-F on their surfaces. By preloading target cells with the βLa fluorescent substrate CCF2-AM, we obtained viral entry kinetic curves that correlated with the NiV-F fusogenic phenotypes, validating NiVLPs as suitable viral entry kinetic tools and suggesting overall relatively slower viral entry than cell-cell fusion kinetics. Additionally, the proportions of F and G on individual NiVLPs and the extent of receptor-induced conformational changes in NiV-G were measured via flow virometry, allowing the proper interpretation of the viral entry kinetic phenotypes. The significance of these findings in the viral entry field extends beyond NiV to other paramyxoviruses and enveloped viruses. Virus-cell membrane fusion is essential for enveloped virus infections. However, mechanistic viral membrane fusion studies have predominantly focused on cell-cell fusion models, largely due to the low availability of technologies capable of characterizing actual virus-cell membrane fusion. Although cell-cell fusion assays are valuable, they do not fully recapitulate all the variables of virus-cell membrane fusion. For example, drastic differences between viral and cellular membrane lipid and protein compositions and curvatures exist. For biosafety level 4 (BSL4) pathogens such as the deadly Nipah virus (NiV), virus-cell fusion mechanistic studies are especially cumbersome. To circumvent these limitations, we used enzymatic Nipah virus-like-particles (NiVLPs) and developed new flow virometric tools. Our new tools allowed us the high-throughput measurement of viral entry kinetics, glycoprotein proportions on individual viral particles, and receptor-induced conformational changes in viral glycoproteins on viral surfaces. The significance of these findings extends beyond NiV to other paramyxoviruses and enveloped viruses. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

DOSE-DEPENDENT TRANSITIONS IN MECHANISMS OF TOXICITY: CASE STUDIES

EPA Science Inventory

Experience with dose response and mechanisms of toxicity has shown that multiple mechanisms may exist for a single agent along the continuum of the full dose-response curve. It is highly likely that critical, limiting steps in any given mechanistic pathway may become overwhelmed ...

Leatherbacks swimming in silico: modeling and verifying their momentum and heat balance using computational fluid dynamics.

PubMed

Dudley, Peter N; Bonazza, Riccardo; Jones, T Todd; Wyneken, Jeanette; Porter, Warren P

2014-01-01

As global temperatures increase throughout the coming decades, species ranges will shift. New combinations of abiotic conditions will make predicting these range shifts difficult. Biophysical mechanistic niche modeling places bounds on an animal's niche through analyzing the animal's physical interactions with the environment. Biophysical mechanistic niche modeling is flexible enough to accommodate these new combinations of abiotic conditions. However, this approach is difficult to implement for aquatic species because of complex interactions among thrust, metabolic rate and heat transfer. We use contemporary computational fluid dynamic techniques to overcome these difficulties. We model the complex 3D motion of a swimming neonate and juvenile leatherback sea turtle to find power and heat transfer rates during the stroke. We combine the results from these simulations and a numerical model to accurately predict the core temperature of a swimming leatherback. These results are the first steps in developing a highly accurate mechanistic niche model, which can assists paleontologist in understanding biogeographic shifts as well as aid contemporary species managers about potential range shifts over the coming decades.

Modeling of batch sorber system: kinetic, mechanistic, and thermodynamic modeling

NASA Astrophysics Data System (ADS)

Mishra, Vishal

2017-10-01

The present investigation has dealt with the biosorption of copper and zinc ions on the surface of egg-shell particles in the liquid phase. Various rate models were evaluated to elucidate the kinetics of copper and zinc biosorptions, and the results indicated that the pseudo-second-order model was more appropriate than the pseudo-first-order model. The curve of the initial sorption rate versus the initial concentration of copper and zinc ions also complemented the results of the pseudo-second-order model. Models used for the mechanistic modeling were the intra-particle model of pore diffusion and Bangham's model of film diffusion. The results of the mechanistic modeling together with the values of pore and film diffusivities indicated that the preferential mode of the biosorption of copper and zinc ions on the surface of egg-shell particles in the liquid phase was film diffusion. The results of the intra-particle model showed that the biosorption of the copper and zinc ions was not dominated by the pore diffusion, which was due to macro-pores with open-void spaces present on the surface of egg-shell particles. The thermodynamic modeling reproduced the fact that the sorption of copper and zinc was spontaneous, exothermic with the increased order of the randomness at the solid-liquid interface.

Development of Macroscale Models of UO 2 Fuel Sintering and Densification using Multiscale Modeling and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenquist, Ian; Tonks, Michael

2016-10-01

Light water reactor fuel pellets are fabricated using sintering to final densities of 95% or greater. During reactor operation, the porosity remaining in the fuel after fabrication decreases further due to irradiation-assisted densification. While empirical models have been developed to describe this densification process, a mechanistic model is needed as part of the ongoing work by the NEAMS program to develop a more predictive fuel performance code. In this work we will develop a phase field model of sintering of UO 2 in the MARMOT code, and validate it by comparing to published sintering data. We will then add themore » capability to capture irradiation effects into the model, and use it to develop a mechanistic model of densification that will go into the BISON code and add another essential piece to the microstructure-based materials models. The final step will be to add the effects of applied fields, to model field-assisted sintering of UO 2. The results of the phase field model will be validated by comparing to data from field-assisted sintering. Tasks over three years: 1. Develop a sintering model for UO 2 in MARMOT 2. Expand model to account for irradiation effects 3. Develop a mechanistic macroscale model of densification for BISON« less

Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

USGS Publications Warehouse

Hooten, Mevin B.; Leeds, William B.; Fiechter, Jerome; Wikle, Christopher K.

2011-01-01

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches.

PubMed

Jones, Hannah M; Mayawala, Kapil; Poulin, Patrick

2013-04-01

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.

Productivity of "collisions generate heat" for reconciling an energy model with mechanistic reasoning: A case study

NASA Astrophysics Data System (ADS)

Scherr, Rachel E.; Robertson, Amy D.

2015-06-01

We observe teachers in professional development courses about energy constructing mechanistic accounts of energy transformations. We analyze a case in which teachers investigating adiabatic compression develop a model of the transformation of kinetic energy to thermal energy. Among their ideas is the idea that thermal energy is generated as a byproduct of individual particle collisions, which is represented in science education research literature as an obstacle to learning. We demonstrate that in this instructional context, the idea that individual particle collisions generate thermal energy is not an obstacle to learning, but instead is productive: it initiates intellectual progress. Specifically, this idea initiates the reconciliation of the teachers' energy model with mechanistic reasoning about adiabatic compression, and leads to a canonically correct model of the transformation of kinetic energy into thermal energy. We claim that the idea's productivity is influenced by features of our particular instructional context, including the instructional goals of the course, the culture of collaborative sense making, and the use of certain representations of energy.

Review of a causal role of fructose-containing sugars in myocardial susceptibility to ischemia/reperfusion injury.

PubMed

Maarman, Gerald J; Mendham, Amy E; Lamont, Kim; George, Cindy

2017-06-01

In 2012, the World Health Organization Global Status Report on noncommunicable diseases showed that 7.4 million deaths were due to ischemic heart disease. Consequently, cardiovascular disease is a significant health burden, especially when partnered with comorbidities such as obesity, metabolic syndrome, and type 2 diabetes mellitus. Of note, these diseases can all be induced or exacerbated by diet. Carbohydrates, in particular, fructose and glucose, generally form the largest part of the human diet. Accumulating evidence from animal studies suggests that if large amounts of fructose are consumed either in isolation or in combination with glucose (fructose-containing sugars), myocardial susceptibility to ischemia/reperfusion (I/R) injury increases. However, the underlying mechanisms that predisposes the myocardium to I/R injury in the fructose model are not elucidated, and no single mechanistic pathway has been described. Based on all available data on this topic, this review describes previously investigated mechanisms and highlights 3 main mechanistic pathways whereby fructose has shown to increase myocardial susceptibility to I/R injury. These pathways include (1) increased reactive oxygen species, resulting in reduced nitric oxide synthase and coronary flow; (2) elevated plasma fatty acids and insulin, leading to increased cardiac triglyceride content and lipotoxicity; and (3) disrupted myocardial calcium handling/homeostasis. Moreover, we highlight various factors that should be taken into account when the fructose animal model is used, such as rat strain, treatment periods, and doses. We argue that failure to do so would result in erratic inferences drawn from the existing body of evidence on fructose animal models. Copyright © 2017 Elsevier Inc. All rights reserved.

A semi-mechanistic model of dead fine fuel moisture for Temperate and Mediterranean ecosystems

NASA Astrophysics Data System (ADS)

Resco de Dios, Víctor; Fellows, Aaron; Boer, Matthias; Bradstock, Ross; Nolan, Rachel; Goulden, Michel

2014-05-01

Fire is a major disturbance in terrestrial ecosystems globally. It has an enormous economic and social cost, and leads to fatalities in the worst cases. The moisture content of the vegetation (fuel moisture) is one of the main determinants of fire risk. Predicting the moisture content of dead and fine fuel (< 2.5 cm in diameter) is particularly important, as this is often the most important component of the fuel complex for fire propagation. A variety of drought indices, empirical and mechanistic models have been proposed to model fuel moisture. A commonality across these different approaches is that they have been neither validated across large temporal datasets nor validated across broadly different vegetation types. Here, we present the results of a study performed at 6 locations in California, USA (5 sites) and New South Wales, Australia (1 site), where 10-hours fuel moisture content was continuously measured every 30 minutes during one full year at each site. We observed that drought indices did not accurately predict fuel moisture, and that empirical and mechanistic models both needed site-specific calibrations, which hinders their global application as indices of fuel moisture. We developed a novel, single equation and semi-mechanistic model, based on atmospheric vapor-pressure deficit. Across sites and years, mean absolute error (MAE) of predicted fuel moisture was 4.7%. MAE dropped <1% in the critical range of fuel moisture <10%. The high simplicity, accuracy and precision of our model makes it suitable for a wide range of applications: from operational purposes, to global vegetation models.

Model for estimating enteric methane emissions from United States dairy and feedlot cattle.

PubMed

Kebreab, E; Johnson, K A; Archibeque, S L; Pape, D; Wirth, T

2008-10-01

Methane production from enteric fermentation in cattle is one of the major sources of anthropogenic greenhouse gas emission in the United States and worldwide. National estimates of methane emissions rely on mathematical models such as the one recommended by the Intergovernmental Panel for Climate Change (IPCC). Models used for prediction of methane emissions from cattle range from empirical to mechanistic with varying input requirements. Two empirical and 2 mechanistic models (COWPOLL and MOLLY) were evaluated for their prediction ability using individual cattle measurements. Model selection was based on mean square prediction error (MSPE), concordance correlation coefficient, and residuals vs. predicted values analyses. In dairy cattle, COWPOLL had the lowest root MSPE and greatest accuracy and precision of predicting methane emissions (correlation coefficient estimate = 0.75). The model simulated differences in diet more accurately than the other models, and the residuals vs. predicted value analysis showed no mean bias (P = 0.71). In feedlot cattle, MOLLY had the lowest root MSPE with almost all errors from random sources (correlation coefficient estimate = 0.69). The IPCC model also had good agreement with observed values, and no significant mean (P = 0.74) or linear bias (P = 0.11) was detected when residuals were plotted against predicted values. A fixed methane conversion factor (Ym) might be an easier alternative to diet-dependent variable Ym. Based on the results, the 2 mechanistic models were used to simulate methane emissions from representative US diets and were compared with the IPCC model. The average Ym in dairy cows was 5.63% of GE (range 3.78 to 7.43%) compared with 6.5% +/- 1% recommended by IPCC. In feedlot cattle, the average Ym was 3.88% (range 3.36 to 4.56%) compared with 3% +/- 1% recommended by IPCC. Based on our simulations, using IPCC values can result in an overestimate of about 12.5% and underestimate of emissions by about 9.8% for dairy and feedlot cattle, respectively. In addition to providing improved estimates of emissions based on diets, mechanistic models can be used to assess mitigation options such as changing source of carbohydrate or addition of fat to decrease methane, which is not possible with empirical models. We recommend national inventories use diet-specific Ym values predicted by mechanistic models to estimate methane emissions from cattle.

Mechanistic ecohydrological modeling with Tethys-Chloris: an attempt to unravel complexity

NASA Astrophysics Data System (ADS)

Fatichi, S.; Ivanov, V. Y.; Caporali, E.

2010-12-01

The role of vegetation in controlling and mediating hydrological states and fluxes at the level of individual processes has been largely explored, which has lead to the improvement of our understanding of mechanisms and patterns in ecohydrological systems. Nonetheless, relatively few efforts have been directed toward the development of continuous, complex, mechanistic ecohydrological models operating at the watershed-scale. This study presents a novel ecohydrological model Tethys-Chloris (T&C) and aims to discuss current limitations and perspectives of the mechanistic approach in ecohydrology. The model attempts to synthesize the state-of-the-art knowledge on individual processes and mechanisms drawn from various disciplines such as hydrology, plant physiology, ecology, and biogeochemistry. The model reproduces all essential components of hydrological cycle resolving the mass and energy budgets at the hourly scale; it includes energy and mass exchanges in the atmospheric boundary layer; a module of saturated and unsaturated soil water dynamics; two layers of vegetation, and a module of snowpack evolution. The vegetation component parsimoniously parameterizes essential plant life-cycle processes, including photosynthesis, phenology, carbon allocation, tissues turnover, and soil biogeochemistry. Quantitative metrics of model performance are discussed and highlight the capabilities of T&C in reproducing ecohydrological dynamics. The simulated patterns mimic the outcome of hydrological dynamics with high realism, given the uncertainty of imposed boundary conditions and limited data availability. Furthermore, highly satisfactory results are obtained without significant (e.g., automated) calibration efforts despite the large phase-space dimensionality of the model. A significant investment into model design and development leads to such desirable behavior. This suggests that while using the presented tool for high-precision predictions can be still problematic, the mechanistic nature of the model can be extremely valuable for designing virtual experiments, testing hypotheses. and focusing questions of scientific inquiry.

Quantitative prediction of repaglinide-rifampicin complex drug interactions using dynamic and static mechanistic models: delineating differential CYP3A4 induction and OATP1B1 inhibition potential of rifampicin.

PubMed

Varma, Manthena V S; Lin, Jian; Bi, Yi-An; Rotter, Charles J; Fahmi, Odette A; Lam, Justine L; El-Kattan, Ayman F; Goosen, Theunis C; Lai, Yurong

2013-05-01

Repaglinide is mainly metabolized by cytochrome P450 enzymes CYP2C8 and CYP3A4, and it is also a substrate to a hepatic uptake transporter, organic anion transporting polypeptide (OATP)1B1. The purpose of this study is to predict the dosing time-dependent pharmacokinetic interactions of repaglinide with rifampicin, using mechanistic models. In vitro hepatic transport of repaglinide, characterized using sandwich-cultured human hepatocytes, and intrinsic metabolic parameters were used to build a dynamic whole-body physiologically-based pharmacokinetic (PBPK) model. The PBPK model adequately described repaglinide plasma concentration-time profiles and successfully predicted area under the plasma concentration-time curve ratios of repaglinide (within ± 25% error), dosed (staggered 0-24 hours) after rifampicin treatment when primarily considering induction of CYP3A4 and reversible inhibition of OATP1B1 by rifampicin. Further, a static mechanistic "extended net-effect" model incorporating transport and metabolic disposition parameters of repaglinide and interaction potency of rifampicin was devised. Predictions based on the static model are similar to those observed in the clinic (average error ∼19%) and to those based on the PBPK model. Both the models suggested that the combined effect of increased gut extraction and decreased hepatic uptake caused minimal repaglinide systemic exposure change when repaglinide is dosed simultaneously or 1 hour after the rifampicin dose. On the other hand, isolated induction effect as a result of temporal separation of the two drugs translated to an approximate 5-fold reduction in repaglinide systemic exposure. In conclusion, both dynamic and static mechanistic models are instrumental in delineating the quantitative contribution of transport and metabolism in the dosing time-dependent repaglinide-rifampicin interactions.

The mechanism distinguishability problem in biochemical kinetics: the single-enzyme, single-substrate reaction as a case study.

PubMed

Schnell, Santiago; Chappell, Michael J; Evans, Neil D; Roussel, Marc R

2006-01-01

A theoretical analysis of the distinguishability problem of two rival models of the single enzyme-single substrate reaction, the Michaelis-Menten and Henri mechanisms, is presented. We also outline a general approach for analysing the structural indistinguishability between two mechanisms. The approach involves constructing, if possible, a smooth mapping between the two candidate models. Evans et al. [N.D. Evans, M.J. Chappell, M.J. Chapman, K.R. Godfrey, Structural indistinguishability between uncontrolled (autonomous) nonlinear analytic systems, Automatica 40 (2004) 1947-1953] have shown that if, in addition, either of the mechanisms satisfies a particular criterion then such a transformation always exists when the models are indistinguishable from their experimentally observable outputs. The approach is applied to the single enzyme-single substrate reaction mechanism. In principle, mechanisms can be distinguished using this analysis, but we show that our ability to distinguish mechanistic models depends both on the precise measurements made, and on our knowledge of the system prior to performing the kinetics experiments.

Progress toward an explicit mechanistic model for the light-driven pump, bacteriorhodopsin

NASA Technical Reports Server (NTRS)

Lanyi, J. K.

1999-01-01

Recent crystallographic information about the structure of bacteriorhodopsin and some of its photointermediates, together with a large amount of spectroscopic and mutational data, suggest a mechanistic model for how this protein couples light energy to the translocation of protons across the membrane. Now nearing completion, this detailed molecular model will describe the nature of the steric and electrostatic conflicts at the photoisomerized retinal, as well as the means by which it induces proton transfers in the two half-channels leading to the two membrane surfaces, thereby causing unidirectional, uphill transport.

A TAD further: exogenous control of gene activation.

PubMed

Mapp, Anna K; Ansari, Aseem Z

2007-01-23

Designer molecules that can be used to impose exogenous control on gene transcription, artificial transcription factors (ATFs), are highly desirable as mechanistic probes of gene regulation, as potential therapeutic agents, and as components of cell-based devices. Recently, several advances have been made in the design of ATFs that activate gene transcription (activator ATFs), including reports of small-molecule-based systems and ATFs that exhibit potent activity. However, the many open mechanistic questions about transcriptional activators, in particular, the structure and function of the transcriptional activation domain (TAD), have hindered rapid development of synthetic ATFs. A compelling need thus exists for chemical tools and insights toward a more detailed portrait of the dynamic process of gene activation.

A framework for conducting mechanistic based reliability assessments of components operating in complex systems

NASA Astrophysics Data System (ADS)

Wallace, Jon Michael

2003-10-01

Reliability prediction of components operating in complex systems has historically been conducted in a statistically isolated manner. Current physics-based, i.e. mechanistic, component reliability approaches focus more on component-specific attributes and mathematical algorithms and not enough on the influence of the system. The result is that significant error can be introduced into the component reliability assessment process. The objective of this study is the development of a framework that infuses the needs and influence of the system into the process of conducting mechanistic-based component reliability assessments. The formulated framework consists of six primary steps. The first three steps, identification, decomposition, and synthesis, are primarily qualitative in nature and employ system reliability and safety engineering principles to construct an appropriate starting point for the component reliability assessment. The following two steps are the most unique. They involve a step to efficiently characterize and quantify the system-driven local parameter space and a subsequent step using this information to guide the reduction of the component parameter space. The local statistical space quantification step is accomplished using two proposed multivariate probability models: Multi-Response First Order Second Moment and Taylor-Based Inverse Transformation. Where existing joint probability models require preliminary distribution and correlation information of the responses, these models combine statistical information of the input parameters with an efficient sampling of the response analyses to produce the multi-response joint probability distribution. Parameter space reduction is accomplished using Approximate Canonical Correlation Analysis (ACCA) employed as a multi-response screening technique. The novelty of this approach is that each individual local parameter and even subsets of parameters representing entire contributing analyses can now be rank ordered with respect to their contribution to not just one response, but the entire vector of component responses simultaneously. The final step of the framework is the actual probabilistic assessment of the component. Although the same multivariate probability tools employed in the characterization step can be used for the component probability assessment, variations of this final step are given to allow for the utilization of existing probabilistic methods such as response surface Monte Carlo and Fast Probability Integration. The overall framework developed in this study is implemented to assess the finite-element based reliability prediction of a gas turbine airfoil involving several failure responses. Results of this implementation are compared to results generated using the conventional 'isolated' approach as well as a validation approach conducted through large sample Monte Carlo simulations. The framework resulted in a considerable improvement to the accuracy of the part reliability assessment and an improved understanding of the component failure behavior. Considerable statistical complexity in the form of joint non-normal behavior was found and accounted for using the framework. Future applications of the framework elements are discussed.

Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model

PubMed Central

Emmott, Stephen; Hutton, Jon; Lyutsarev, Vassily; Smith, Matthew J.; Scharlemann, Jörn P. W.; Purves, Drew W.

2014-01-01

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures. PMID:24756001

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model.

PubMed

Harfoot, Michael B J; Newbold, Tim; Tittensor, Derek P; Emmott, Stephen; Hutton, Jon; Lyutsarev, Vassily; Smith, Matthew J; Scharlemann, Jörn P W; Purves, Drew W

2014-04-01

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures.

Biomechanics meets the ecological niche: the importance of temporal data resolution.

PubMed

Kearney, Michael R; Matzelle, Allison; Helmuth, Brian

2012-03-15

The emerging field of mechanistic niche modelling aims to link the functional traits of organisms to their environments to predict survival, reproduction, distribution and abundance. This approach has great potential to increase our understanding of the impacts of environmental change on individuals, populations and communities by providing functional connections between physiological and ecological response to increasingly available spatial environmental data. By their nature, such mechanistic models are more data intensive in comparison with the more widely applied correlative approaches but can potentially provide more spatially and temporally explicit predictions, which are often needed by decision makers. A poorly explored issue in this context is the appropriate level of temporal resolution of input data required for these models, and specifically the error in predictions that can be incurred through the use of temporally averaged data. Here, we review how biomechanical principles from heat-transfer and metabolic theory are currently being used as foundations for mechanistic niche models and consider the consequences of different temporal resolutions of environmental data for modelling the niche of a behaviourally thermoregulating terrestrial lizard. We show that fine-scale temporal resolution (daily) data can be crucial for unbiased inference of climatic impacts on survival, growth and reproduction. This is especially so for species with little capacity for behavioural buffering, because of behavioural or habitat constraints, and for detecting temporal trends. However, coarser-resolution data (long-term monthly averages) can be appropriate for mechanistic studies of climatic constraints on distribution and abundance limits in thermoregulating species at broad spatial scales.

Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science.

PubMed

Bechtel, William; Abrahamsen, Adele

2010-09-01

We consider computational modeling in two fields: chronobiology and cognitive science. In circadian rhythm models, variables generally correspond to properties of parts and operations of the responsible mechanism. A computational model of this complex mechanism is grounded in empirical discoveries and contributes a more refined understanding of the dynamics of its behavior. In cognitive science, on the other hand, computational modelers typically advance de novo proposals for mechanisms to account for behavior. They offer indirect evidence that a proposed mechanism is adequate to produce particular behavioral data, but typically there is no direct empirical evidence for the hypothesized parts and operations. Models in these two fields differ in the extent of their empirical grounding, but they share the goal of achieving dynamic mechanistic explanation. That is, they augment a proposed mechanistic explanation with a computational model that enables exploration of the mechanism's dynamics. Using exemplars from circadian rhythm research, we extract six specific contributions provided by computational models. We then examine cognitive science models to determine how well they make the same types of contributions. We suggest that the modeling approach used in circadian research may prove useful in cognitive science as researchers develop procedures for experimentally decomposing cognitive mechanisms into parts and operations and begin to understand their nonlinear interactions.

Experimental and Theoretical Studies of Atmosphereic Inorganic Chlorine Chemistry

NASA Technical Reports Server (NTRS)

Sander, Stanley P.; Friedl, Randall R.

1993-01-01

Over the last five years substantial progress has been made in defining the realm of new chlorine chemistry in the polar stratosphere. Application of existing experimental techniques to potentially important chlorine-containing compounds has yielded quantitative kinetic and spectroscopic data as well as qualitative mechanistic insights into the relevant reactions.

Mechanisms of antimony adsorption onto soybean stover-derived biochar in aqueous solutions

USDA-ARS?s Scientific Manuscript database

Limited mechanistic knowledge is available to understand how biochar interacts with trace elements that exist predominantly as oxoanions, such as antimony (Sb). Soybean stover biochars were produced at 300 degrees C (SBC300) and 700 degrees C (SBC700), and were characterized by BET, Boehm titration,...

Causal-Explanatory Pluralism: How Intentions, Functions, and Mechanisms Influence Causal Ascriptions

ERIC Educational Resources Information Center

Lombrozo, Tania

2010-01-01

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual…

Mechanistic-Empirical Pavement Design Guide Flexible Pavement Performance Prediction Models Volume III Field Guide - Calibration and User's Guide for the Mechanistic-Empirical Pavement Design Guide

DOT National Transportation Integrated Search

2007-08-01

The objective of this research study was to develop performance characteristics or variables (e.g., ride quality, rutting, : fatigue cracking, transverse cracking) of flexible pavements in Montana, and to use these characteristics in the : implementa...

MECHANISTIC ROLES OF SOIL HUMUS AND MINERALS IN THE SORPTION OF NONIONIC ORGANIC COMPOUNDS FROM AQUEOUS AND ORGANIC SOLUTIONS

EPA Science Inventory

Mechanistic roles of soil humus and soil minerals and their contributions to soil sorption of nonionic organic compounds from aqueous and organic solutions are illustrated. Parathion and lindane are used as model solutes on two soils that differ greatly in their humic and mineral...

Effects of exercise on tumor physiology and metabolism.

PubMed

Pedersen, Line; Christensen, Jesper Frank; Hojman, Pernille

2015-01-01

Exercise is a potent regulator of a range of physiological processes in most tissues. Solid epidemiological data show that exercise training can reduce disease risk and mortality for several cancer diagnoses, suggesting that exercise training may directly regulate tumor physiology and metabolism. Here, we review the body of literature describing exercise intervention studies performed in rodent tumor models and elaborate on potential mechanistic effects of exercise on tumor physiology. Exercise has been shown to reduce tumor incidence, tumor multiplicity, and tumor growth across numerous different transplantable, chemically induced or genetic tumor models. We propose 4 emerging mechanistic effects of exercise, including (1) vascularization and blood perfusion, (2) immune function, (3) tumor metabolism, and (4) muscle-to-cancer cross-talk, and discuss these in details. In conclusion, exercise training has the potential to be a beneficial and integrated component of cancer management, but has yet to fully elucidate its potential. Understanding the mechanistic effects of exercise on tumor physiology is warranted. Insight into these mechanistic effects is emerging, but experimental intervention studies are still needed to verify the cause-effect relationship between these mechanisms and the control of tumor growth.

Regulatory Technology Development Plan - Sodium Fast Reactor: Mechanistic Source Term – Trial Calculation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grabaskas, David; Bucknor, Matthew; Jerden, James

2016-10-01

The potential release of radioactive material during a plant incident, referred to as the source term, is a vital design metric and will be a major focus of advanced reactor licensing. The U.S. Nuclear Regulatory Commission has stated an expectation for advanced reactor vendors to present a mechanistic assessment of the potential source term in their license applications. The mechanistic source term presents an opportunity for vendors to realistically assess the radiological consequences of an incident, and may allow reduced emergency planning zones and smaller plant sites. However, the development of a mechanistic source term for advanced reactors is notmore » without challenges, as there are often numerous phenomena impacting the transportation and retention of radionuclides. This project sought to evaluate U.S. capabilities regarding the mechanistic assessment of radionuclide release from core damage incidents at metal fueled, pool-type sodium fast reactors (SFRs). The purpose of the analysis was to identify, and prioritize, any gaps regarding computational tools or data necessary for the modeling of radionuclide transport and retention phenomena. To accomplish this task, a parallel-path analysis approach was utilized. One path, led by Argonne and Sandia National Laboratories, sought to perform a mechanistic source term assessment using available codes, data, and models, with the goal to identify gaps in the current knowledge base. The second path, performed by an independent contractor, performed sensitivity analyses to determine the importance of particular radionuclides and transport phenomena in regards to offsite consequences. The results of the two pathways were combined to prioritize gaps in current capabilities.« less

Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

PubMed

Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

2017-11-15

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among cell types. Despite this extensive lysosomal sequestration in the individual cells types, the maximal change in the overall predicted tissue Kpu was <3-fold for lysosome-rich tissues investigated here. Accounting for the variability in cellular physiological model input parameters, in particular lysosomal pH and fraction of the cellular volume occupied by the lysosomes, only partially explained discrepancies between observed and predicted Kpu data in the lung. Improved understanding of the system properties, e.g., cell/organelle composition is required to support further development of mechanistic equations for the prediction of drug tissue distribution. Application of this revised mechanistic model is recommended for prediction of Kpu in lysosome-rich tissue to facilitate the advancement of physiologically-based prediction of volume of distribution and drug exposure in the tissues. Copyright © 2017 Elsevier B.V. All rights reserved.

In Silico Modelling of Transdermal and Systemic Kinetics of Topically Applied Solutes: Model Development and Initial Validation for Transdermal Nicotine.

PubMed

Chen, Tao; Lian, Guoping; Kattou, Panayiotis

2016-07-01

The purpose was to develop a mechanistic mathematical model for predicting the pharmacokinetics of topically applied solutes penetrating through the skin and into the blood circulation. The model could be used to support the design of transdermal drug delivery systems and skin care products, and risk assessment of occupational or consumer exposure. A recently reported skin penetration model [Pharm Res 32 (2015) 1779] was integrated with the kinetic equations for dermis-to-capillary transport and systemic circulation. All model parameters were determined separately from the molecular, microscopic and physiological bases, without fitting to the in vivo data to be predicted. Published clinical studies of nicotine were used for model demonstration. The predicted plasma kinetics is in good agreement with observed clinical data. The simulated two-dimensional concentration profile in the stratum corneum vividly illustrates the local sub-cellular disposition kinetics, including tortuous lipid pathway for diffusion and the "reservoir" effect of the corneocytes. A mechanistic model for predicting transdermal and systemic kinetics was developed and demonstrated with published clinical data. The integrated mechanistic approach has significantly extended the applicability of a recently reported microscopic skin penetration model by providing prediction of solute concentration in the blood.

Mechanistic Prediction of the Effect of Microstructural Coarsening on Creep Response of SnAgCu Solder Joints

NASA Astrophysics Data System (ADS)

Mukherjee, S.; Chauhan, P.; Osterman, M.; Dasgupta, A.; Pecht, M.

2016-07-01

Mechanistic microstructural models have been developed to capture the effect of isothermal aging on time dependent viscoplastic response of Sn3.0Ag0.5Cu (SAC305) solders. SnAgCu (SAC) solders undergo continuous microstructural coarsening during both storage and service because of their high homologous temperature. The microstructures of these low melting point alloys continuously evolve during service. This results in evolution of creep properties of the joint over time, thereby influencing the long term reliability of microelectronic packages. It is well documented that isothermal aging degrades the creep resistance of SAC solder. SAC305 alloy is aged for (24-1000) h at (25-100)°C (~0.6-0.8 × T melt). Cross-sectioning and image processing techniques were used to periodically quantify the effect of isothermal aging on phase coarsening and evolution. The parameters monitored during isothermal aging include size, area fraction, and inter-particle spacing of nanoscale Ag3Sn intermetallic compounds (IMCs) and the volume fraction of micronscale Cu6Sn5 IMCs, as well as the area fraction of pure tin dendrites. Effects of microstructural evolution on secondary creep constitutive response of SAC305 solder joints were then modeled using a mechanistic multiscale creep model. The mechanistic phenomena modeled include: (1) dispersion strengthening by coarsened nanoscale Ag3Sn IMCs in the eutectic phase; and (2) load sharing between pro-eutectic Sn dendrites and the surrounding coarsened eutectic Sn-Ag phase and microscale Cu6Sn5 IMCs. The coarse-grained polycrystalline Sn microstructure in SAC305 solder was not captured in the above model because isothermal aging does not cause any significant change in the initial grain size and orientation of SAC305 solder joints. The above mechanistic model can successfully capture the drop in creep resistance due to the influence of isothermal aging on SAC305 single crystals. Contribution of grain boundary sliding to the creep strain of coarse grained joints has not been modeled in this study.

Magnetic particle-mediated magnetoreception

PubMed Central

Shaw, Jeremy; Boyd, Alastair; House, Michael; Woodward, Robert; Mathes, Falko; Cowin, Gary; Saunders, Martin; Baer, Boris

2015-01-01

Behavioural studies underpin the weight of experimental evidence for the existence of a magnetic sense in animals. In contrast, studies aimed at understanding the mechanistic basis of magnetoreception by determining the anatomical location, structure and function of sensory cells have been inconclusive. In this review, studies attempting to demonstrate the existence of a magnetoreceptor based on the principles of the magnetite hypothesis are examined. Specific attention is given to the range of techniques, and main animal model systems that have been used in the search for magnetite particulates. Anatomical location/cell rarity and composition are identified as two key obstacles that must be addressed in order to make progress in locating and characterizing a magnetite-based magnetoreceptor cell. Avenues for further study are suggested, including the need for novel experimental, correlative, multimodal and multidisciplinary approaches. The aim of this review is to inspire new efforts towards understanding the cellular basis of magnetoreception in animals, which will in turn inform a new era of behavioural research based on first principles. PMID:26333810

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

NASA Astrophysics Data System (ADS)

Laksari, Kaveh; Kurt, Mehmet; Babaee, Hessam; Kleiven, Svein; Camarillo, David

2018-03-01

Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology.

Fast antibody fragment motion: flexible linkers act as entropic spring

PubMed Central

Stingaciu, Laura R.; Ivanova, Oxana; Ohl, Michael; Biehl, Ralf; Richter, Dieter

2016-01-01

A flexible linker region between three fragments allows antibodies to adjust their binding sites to an antigen or receptor. Using Neutron Spin Echo Spectroscopy we observed fragment motion on a timescale of 7 ns with motional amplitudes of about 1 nm relative to each other. The mechanistic complexity of the linker region can be described by a spring model with Brownian motion of the fragments in a harmonic potential. Displacements, timescale, friction and force constant of the underlying dynamics are accessed. The force constant exhibits a similar strength to an entropic spring, with friction of the fragment matching the unbound state. The observed fast motions are fluctuations in pre-existing equilibrium configurations. The Brownian motion of domains in a harmonic potential is the appropriate model to examine functional hinge motions dependent on the structural topology and highlights the role of internal forces and friction to function. PMID:27020739

Flamelet Formation In Hele-Shaw Flow

NASA Technical Reports Server (NTRS)

Wichman, I. S.; Olson, S. L.

2003-01-01

A Hele-Shaw flow apparatus constructed at Michigan State University (MSU) produces conditions that reduce influences of buoyancy-driven flows. In addition, in the MSU Hele-Shaw apparatus it is possible to adjust the heat losses from the fuel sample (0.001 in. thick cellulose) and the flow speed of the approaching oxidizer flow (air) so that the "flamelet regime of flame spread" is entered. In this regime various features of the flame-to-smolder (and vice versa) transition can be studied. For the relatively wide (approx. 17.5 cm) and long (approx. 20 cm) samples used, approximately ten flamelets existed at all times. The flamelet behavior was studied mechanistically and statistically. A heat transfer analysis of the dominant heat transfer mechanisms was conducted. Results indicate that radiation and conduction processes are important, and that a simple 1-D model using the Broido-Shafizadeh model for cellulose decomposition chemistry can describe aspects of the flamelet spread process. Introduction

Fast antibody fragment motion: flexible linkers act as entropic spring

DOE PAGES

Stingaciu, Laura R.; Ivanova, Oxana; Ohl, Michael; ...

2016-03-29

A flexible linker region between three fragments allows antibodies to adjust their binding sites to an antigen or receptor. Using Neutron Spin Echo Spectroscopy we observed fragment motion on a timescale of 7 ns with motional amplitudes of about 1 nm relative to each other. The mechanistic complexity of the linker region can be described by a spring model with Brownian motion of the fragments in a harmonic potential. Displacements, timescale, friction and force constant of the underlying dynamics are accessed. The force constant exhibits a similar strength to an entropic spring, with friction of the fragment matching the unboundmore » state. The observed fast motions are fluctuations in pre-existing equilibrium configurations. In conclusion, the Brownian motion of domains in a harmonic potential is the appropriate model to examine functional hinge motions dependent on the structural topology and highlights the role of internal forces and friction to function.« less

Free-Energy Landscape of the Dissolution of Gibbsite at High pH.

PubMed

Shen, Zhizhang; Kerisit, Sebastien N; Stack, Andrew G; Rosso, Kevin M

2018-04-05

The individual elementary reactions involved in the dissolution of a solid into solution remain mostly speculative due to a lack of direct experimental probes. In this regard, we have applied atomistic simulations to map the free-energy landscape of the dissolution of gibbsite from a step edge as a model of metal hydroxide dissolution. The overall reaction combines kink formation and kink propagation. Two individual reactions were found to be rate-limiting for kink formation, that is, the displacement of Al from a step site to a ledge adatom site and its detachment from ledge/terrace adatom sites into the solution. As a result, a pool of mobile and labile adsorbed species, or adatoms, exists before the release of Al into solution. Because of the quasi-hexagonal symmetry of gibbsite, kink site propagation can occur in multiple directions. Overall, our results will enable the development of microscopic mechanistic models of metal oxide dissolution.

Fast antibody fragment motion: flexible linkers act as entropic spring.

PubMed

Stingaciu, Laura R; Ivanova, Oxana; Ohl, Michael; Biehl, Ralf; Richter, Dieter

2016-03-29

A flexible linker region between three fragments allows antibodies to adjust their binding sites to an antigen or receptor. Using Neutron Spin Echo Spectroscopy we observed fragment motion on a timescale of 7 ns with motional amplitudes of about 1 nm relative to each other. The mechanistic complexity of the linker region can be described by a spring model with Brownian motion of the fragments in a harmonic potential. Displacements, timescale, friction and force constant of the underlying dynamics are accessed. The force constant exhibits a similar strength to an entropic spring, with friction of the fragment matching the unbound state. The observed fast motions are fluctuations in pre-existing equilibrium configurations. The Brownian motion of domains in a harmonic potential is the appropriate model to examine functional hinge motions dependent on the structural topology and highlights the role of internal forces and friction to function.

Models, theory structure and mechanisms in biochemistry: The case of allosterism.

PubMed

Alleva, Karina; Díez, José; Federico, Lucia

2017-06-01

From the perspective of the new mechanistic philosophy, it has been argued that explanatory causal mechanisms in some special sciences such as biochemistry and neurobiology cannot be captured by any useful notion of theory, or at least by any standard notion. The goal of this paper is to show that a model-theoretic notion of theory, and in particular the structuralist notion of a theory-net already applied to other unified explanatory theories, adequately suits the MWC allosteric mechanism explanatory set-up. We also argue, contra some mechanistic claims questioning the use of laws in biological explanations, that the theory reconstructed in this way essentially contains non-accidental regularities that qualify as laws, and that taking into account these lawful components, it is possible to explicate the unified character of the theory. Finally, we argue that, contrary to what some mechanists also claim, functional explanations that do not fully specify the mechanistic structure are not defective or incomplete in any relevant sense, and that functional components are perfectly explanatory. The conclusion is that, as some authors have emphasized in other fields (Walmsley 2008), particular elements of traditional approaches do not contradict but rather complement the new mechanist philosophy, and taken together they may offer a more complete understanding of special sciences and the variety of explanations they provide. Copyright © 2017 Elsevier Ltd. All rights reserved.

Problems in mechanistic theoretical models for cell transformation by ionizing radiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, A.; Holley, W.R.

1991-10-01

A mechanistic model based on yields of double strand breaks has been developed to determine the dose response curves for cell transformation frequencies. At its present stage the model is applicable to immortal cell lines and to various qualities (X-rays, Neon and Iron) of ionizing radiation. Presently, we have considered four types of processes which can lead to activation phenomena: (1) point mutation events on a regulatory segment of selected oncogenes, (2) inactivation of suppressor genes, through point mutation, (3) deletion of a suppressor gene by a single track, and (4) deletion of a suppressor gene by two tracks.

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

PubMed Central

Thomas-Vaslin, Véronique; Six, Adrien; Ganascia, Jean-Gabriel; Bersini, Hugues

2013-01-01

Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of “state-transitions” of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared. PMID:24101919

Current limitations and recommendations to improve testing ...

EPA Pesticide Factsheets

In this paper existing regulatory frameworks and test systems for assessing potential endocrine-active chemicals are described, and associated challenges discussed, along with proposed approaches to address these challenges. Regulatory frameworks vary somewhat across organizations, but all basically evaluate whether a chemical possesses endocrine activity and whether this activity can result in adverse outcomes either to humans or the environment. Current test systems include in silico, in vitro and in vivo techniques focused on detecting potential endocrine activity, and in vivo tests that collect apical data to detect possible adverse effects. These test systems are currently designed to robustly assess endocrine activity and/or adverse effects in the estrogen, androgen, and thyroid hormonal pathways; however, there are some limitations of current test systems for evaluating endocrine hazard and risk. These limitations include a lack of certainty regarding: 1)adequately sensitive species and life-stages, 2) mechanistic endpoints that are diagnostic for endocrine pathways of concern, and 3) the linkage between mechanistic responses and apical, adverse outcomes. Furthermore, some existing test methods are resource intensive in regard to time, cost, and use of animals. However, based on recent experiences, there are opportunities to improve approaches to, and guidance for existing test methods, and to reduce uncertainty. For example, in vitro high throughput

A mechanistic physicochemical model of carbon dioxide transport in blood.

PubMed

O'Neill, David P; Robbins, Peter A

2017-02-01

A number of mathematical models have been produced that, given the Pco 2 and Po 2 of blood, will calculate the total concentrations for CO 2 and O 2 in blood. However, all these models contain at least some empirical features, and thus do not represent all of the underlying physicochemical processes in an entirely mechanistic manner. The aim of this study was to develop a physicochemical model of CO 2 carriage by the blood to determine whether our understanding of the physical chemistry of the major chemical components of blood together with their interactions is sufficiently strong to predict the physiological properties of CO 2 carriage by whole blood. Standard values are used for the ionic composition of the blood, the plasma albumin concentration, and the hemoglobin concentration. All K m values required for the model are taken from the literature. The distribution of bicarbonate, chloride, and H + ions across the red blood cell membrane follows that of a Gibbs-Donnan equilibrium. The system of equations that results is solved numerically using constraints for mass balance and electroneutrality. The model reproduces the phenomena associated with CO 2 carriage, including the magnitude of the Haldane effect, very well. The structural nature of the model allows various hypothetical scenarios to be explored. Here we examine the effects of 1) removing the ability of hemoglobin to form carbamino compounds; 2) allowing a degree of Cl - binding to deoxygenated hemoglobin; and 3) removing the chloride (Hamburger) shift. The insights gained could not have been obtained from empirical models. This study is the first to incorporate a mechanistic model of chloride-bicarbonate exchange between the erythrocyte and plasma into a full physicochemical model of the carriage of carbon dioxide in blood. The mechanistic nature of the model allowed a theoretical study of the quantitative significance for carbon dioxide transport of carbamino compound formation; the putative binding of chloride to deoxygenated hemoglobin, and the chloride (Hamburger) shift. Copyright © 2017 the American Physiological Society.

A mechanistic physicochemical model of carbon dioxide transport in blood

PubMed Central

O’Neill, David P.

2017-01-01

A number of mathematical models have been produced that, given the Pco2 and Po2 of blood, will calculate the total concentrations for CO2 and O2 in blood. However, all these models contain at least some empirical features, and thus do not represent all of the underlying physicochemical processes in an entirely mechanistic manner. The aim of this study was to develop a physicochemical model of CO2 carriage by the blood to determine whether our understanding of the physical chemistry of the major chemical components of blood together with their interactions is sufficiently strong to predict the physiological properties of CO2 carriage by whole blood. Standard values are used for the ionic composition of the blood, the plasma albumin concentration, and the hemoglobin concentration. All Km values required for the model are taken from the literature. The distribution of bicarbonate, chloride, and H+ ions across the red blood cell membrane follows that of a Gibbs-Donnan equilibrium. The system of equations that results is solved numerically using constraints for mass balance and electroneutrality. The model reproduces the phenomena associated with CO2 carriage, including the magnitude of the Haldane effect, very well. The structural nature of the model allows various hypothetical scenarios to be explored. Here we examine the effects of 1) removing the ability of hemoglobin to form carbamino compounds; 2) allowing a degree of Cl− binding to deoxygenated hemoglobin; and 3) removing the chloride (Hamburger) shift. The insights gained could not have been obtained from empirical models. NEW & NOTEWORTHY This study is the first to incorporate a mechanistic model of chloride-bicarbonate exchange between the erythrocyte and plasma into a full physicochemical model of the carriage of carbon dioxide in blood. The mechanistic nature of the model allowed a theoretical study of the quantitative significance for carbon dioxide transport of carbamino compound formation; the putative binding of chloride to deoxygenated hemoglobin, and the chloride (Hamburger) shift. PMID:27881667

Characterizing the reproduction number of epidemics with early subexponential growth dynamics

PubMed Central

Viboud, Cécile; Simonsen, Lone; Moghadas, Seyed M.

2016-01-01

Early estimates of the transmission potential of emerging and re-emerging infections are increasingly used to inform public health authorities on the level of risk posed by outbreaks. Existing methods to estimate the reproduction number generally assume exponential growth in case incidence in the first few disease generations, before susceptible depletion sets in. In reality, outbreaks can display subexponential (i.e. polynomial) growth in the first few disease generations, owing to clustering in contact patterns, spatial effects, inhomogeneous mixing, reactive behaviour changes or other mechanisms. Here, we introduce the generalized growth model to characterize the early growth profile of outbreaks and estimate the effective reproduction number, with no need for explicit assumptions about the shape of epidemic growth. We demonstrate this phenomenological approach using analytical results and simulations from mechanistic models, and provide validation against a range of empirical disease datasets. Our results suggest that subexponential growth in the early phase of an epidemic is the rule rather the exception. Mechanistic simulations show that slight modifications to the classical susceptible–infectious–removed model result in subexponential growth, and in turn a rapid decline in the reproduction number within three to five disease generations. For empirical outbreaks, the generalized-growth model consistently outperforms the exponential model for a variety of directly and indirectly transmitted diseases datasets (pandemic influenza, measles, smallpox, bubonic plague, cholera, foot-and-mouth disease, HIV/AIDS and Ebola) with model estimates supporting subexponential growth dynamics. The rapid decline in effective reproduction number predicted by analytical results and observed in real and synthetic datasets within three to five disease generations contrasts with the expectation of invariant reproduction number in epidemics obeying exponential growth. The generalized-growth concept also provides us a compelling argument for the unexpected extinction of certain emerging disease outbreaks during the early ascending phase. Overall, our approach promotes a more reliable and data-driven characterization of the early epidemic phase, which is important for accurate estimation of the reproduction number and prediction of disease impact. PMID:27707909

Preparation of the implementation plan of AASHTO Mechanistic-Empirical Pavement Design Guide (M-EPDG) in Connecticut : Phase II : expanded sensitivity analysis and validation with pavement management data.

DOT National Transportation Integrated Search

2017-02-08

The study re-evaluates distress prediction models using the Mechanistic-Empirical Pavement Design Guide (MEPDG) and expands the sensitivity analysis to a wide range of pavement structures and soils. In addition, an extensive validation analysis of th...

Reinterpreting maximum entropy in ecology: a null hypothesis constrained by ecological mechanism.

PubMed

O'Dwyer, James P; Rominger, Andrew; Xiao, Xiao

2017-07-01

Simplified mechanistic models in ecology have been criticised for the fact that a good fit to data does not imply the mechanism is true: pattern does not equal process. In parallel, the maximum entropy principle (MaxEnt) has been applied in ecology to make predictions constrained by just a handful of state variables, like total abundance or species richness. But an outstanding question remains: what principle tells us which state variables to constrain? Here we attempt to solve both problems simultaneously, by translating a given set of mechanisms into the state variables to be used in MaxEnt, and then using this MaxEnt theory as a null model against which to compare mechanistic predictions. In particular, we identify the sufficient statistics needed to parametrise a given mechanistic model from data and use them as MaxEnt constraints. Our approach isolates exactly what mechanism is telling us over and above the state variables alone. © 2017 John Wiley & Sons Ltd/CNRS.

Regulatory Technology Development Plan - Sodium Fast Reactor. Mechanistic Source Term - Trial Calculation. Work Plan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grabaskas, David; Bucknor, Matthew; Jerden, James

2016-02-01

The overall objective of the SFR Regulatory Technology Development Plan (RTDP) effort is to identify and address potential impediments to the SFR regulatory licensing process. In FY14, an analysis by Argonne identified the development of an SFR-specific MST methodology as an existing licensing gap with high regulatory importance and a potentially long lead-time to closure. This work was followed by an initial examination of the current state-of-knowledge regarding SFR source term development (ANLART-3), which reported several potential gaps. Among these were the potential inadequacies of current computational tools to properly model and assess the transport and retention of radionuclides duringmore » a metal fuel pool-type SFR core damage incident. The objective of the current work is to determine the adequacy of existing computational tools, and the associated knowledge database, for the calculation of an SFR MST. To accomplish this task, a trial MST calculation will be performed using available computational tools to establish their limitations with regard to relevant radionuclide release/retention/transport phenomena. The application of existing modeling tools will provide a definitive test to assess their suitability for an SFR MST calculation, while also identifying potential gaps in the current knowledge base and providing insight into open issues regarding regulatory criteria/requirements. The findings of this analysis will assist in determining future research and development needs.« less

Comparison of Two-Phase Pipe Flow in OpenFOAM with a Mechanistic Model

NASA Astrophysics Data System (ADS)

Shuard, Adrian M.; Mahmud, Hisham B.; King, Andrew J.

2016-03-01

Two-phase pipe flow is a common occurrence in many industrial applications such as power generation and oil and gas transportation. Accurate prediction of liquid holdup and pressure drop is of vast importance to ensure effective design and operation of fluid transport systems. In this paper, a Computational Fluid Dynamics (CFD) study of a two-phase flow of air and water is performed using OpenFOAM. The two-phase solver, interFoam is used to identify flow patterns and generate values of liquid holdup and pressure drop, which are compared to results obtained from a two-phase mechanistic model developed by Petalas and Aziz (2002). A total of 60 simulations have been performed at three separate pipe inclinations of 0°, +10° and -10° respectively. A three dimensional, 0.052m diameter pipe of 4m length is used with the Shear Stress Transport (SST) k - ɷ turbulence model to solve the turbulent mixtures of air and water. Results show that the flow pattern behaviour and numerical values of liquid holdup and pressure drop compare reasonably well to the mechanistic model.

Mechanistic models versus machine learning, a fight worth fighting for the biological community?

PubMed

Baker, Ruth E; Peña, Jose-Maria; Jayamohan, Jayaratnam; Jérusalem, Antoine

2018-05-01

Ninety per cent of the world's data have been generated in the last 5 years ( Machine learning: the power and promise of computers that learn by example Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input-output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome? © 2018 The Author(s).

Estimates of Nitrogen, Phosphorus, Biochemical Oxygen Demand, and Fecal Coliforms Entering the Environment Due to Inadequate Sanitation Treatment Technologies in 108 Low and Middle Income Countries.

PubMed

Fuhrmeister, Erica R; Schwab, Kellogg J; Julian, Timothy R

2015-10-06

Understanding the excretion and treatment of human waste (feces and urine) in low and middle income countries (LMICs) is necessary to design appropriate waste management strategies. However, excretion and treatment are often difficult to quantify due to decentralization of excreta management. We address this gap by developing a mechanistic, stochastic model to characterize phosphorus, nitrogen, biochemical oxygen demand (BOD), and fecal coliform pollution from human excreta for 108 LMICs. The model estimates excretion and treatment given three scenarios: (1) use of existing sanitation systems, (2) use of World Health Organization-defined "improved sanitation", and (3) use of best available technologies. Our model estimates that more than 10(9) kg/yr each of phosphorus, nitrogen and BOD are produced. Of this, 22(19-27)%, 11(7-15)%, 17(10-23)%, and 35 (23-47)% (mean and 95% range) BOD, nitrogen, phosphorus, and fecal coliforms, respectively, are removed by existing sanitation systems. Our model estimates that upgrading to "improved sanitation" increases mean removal slightly to between 17 and 53%. Under the best available technology scenario, only approximately 60-80% of pollutants are treated. To reduce impact of nutrient and microbial pollution on human and environmental health, improvements in both access to adequate sanitation and sanitation treatment efficiency are needed.

“Scar-cinoma”: viewing the fibrotic lung mesenchymal cell in the context of cancer biology

PubMed Central

Horowitz, Jeffrey C.; Osterholzer, John J.; Marazioti, Antonia; Stathopoulos, Georgios T.

2017-01-01

Lung cancer and pulmonary fibrosis are common, yet distinct, pathological processes that represent urgent unmet medical needs. Striking clinical and mechanistic parallels exist between these distinct disease entities. The goal of this article is to examine lung fibrosis from the perspective of cancer-associated phenotypic hallmarks, to discuss areas of mechanistic overlap and distinction, and to highlight profibrotic mechanisms that contribute to carcinogenesis. Ultimately, we speculate that such comparisons might identify opportunities to leverage our current understanding of the pathobiology of each disease process in order to advance novel therapeutic approaches for both. We anticipate that such “outside the box” concepts could be translated to a more precise and individualised approach to fibrotic diseases of the lung. PMID:27030681

Aiptasia sp. larvae as a model to reveal mechanisms of symbiont selection in cnidarians

NASA Astrophysics Data System (ADS)

Wolfowicz, Iliona; Baumgarten, Sebastian; Voss, Philipp A.; Hambleton, Elizabeth A.; Voolstra, Christian R.; Hatta, Masayuki; Guse, Annika

2016-09-01

Symbiosis, defined as the persistent association between two distinct species, is an evolutionary and ecologically critical phenomenon facilitating survival of both partners in diverse habitats. The biodiversity of coral reef ecosystems depends on a functional symbiosis with photosynthetic dinoflagellates of the highly diverse genus Symbiodinium, which reside in coral host cells and continuously support their nutrition. The mechanisms underlying symbiont selection to establish a stable endosymbiosis in non-symbiotic juvenile corals are unclear. Here we show for the first time that symbiont selection patterns for larvae of two Acropora coral species and the model anemone Aiptasia are similar under controlled conditions. We find that Aiptasia larvae distinguish between compatible and incompatible symbionts during uptake into the gastric cavity and phagocytosis. Using RNA-Seq, we identify a set of candidate genes potentially involved in symbiosis establishment. Together, our data complement existing molecular resources to mechanistically dissect symbiont phagocytosis in cnidarians under controlled conditions, thereby strengthening the role of Aiptasia larvae as a powerful model for cnidarian endosymbiosis establishment.

Affectionate Touch to Promote Relational, Psychological, and Physical Well-Being in Adulthood: A Theoretical Model and Review of the Research.

PubMed

Jakubiak, Brittany K; Feeney, Brooke C

2017-08-01

Throughout the life span, individuals engage in affectionate touch with close others. Touch receipt promotes well-being in infancy, but the impacts of touch in adult close relationships have been largely unexplored. In this article, we propose that affectionate touch receipt promotes relational, psychological, and physical well-being in adulthood, and we present a theoretical mechanistic model to explain why affectionate touch may promote these outcomes. The model includes pathways through which touch could affect well-being by reducing stress and by promoting well-being independent of stress. Specifically, two immediate outcomes of affectionate touch receipt-relational-cognitive changes and neurobiological changes-are described as important mechanisms underlying the effects of affectionate touch on well-being. We also review and evaluate the existing research linking affectionate touch to well-being in adulthood and propose an agenda to advance research in this area. This theoretical perspective provides a foundation for future work on touch in adult close relationships.

Modeling Niemann Pick type C1 using human embryonic and induced pluripotent stem cells.

PubMed

Ordoñez, M Paulina; Steele, John W

2017-02-01

Data generated in Niemann Pick type C1 (NPC1) human embryonic and human induced pluripotent stem cell derived neurons complement on-going studies in animal models and provide the first example, in disease-relevant human cells, of processes that underlie preferential neuronal defects in a NPC1. Our work and that of other investigators in human neurons derived from stem cells highlight the importance of performing rigorous mechanistic studies in relevant cell types to guide drug discovery and therapeutic development, alongside of existing animal models. Through the use of human stem cell-derived models of disease, we can identify and discover or repurpose drugs that revert early events that lead to neuronal failure in NPC1. Together with the study of disease pathogenesis and efficacy of therapies in animal models, these strategies will fulfill the promise of stem cell technology in the development of new treatments for human diseases. This article is part of a Special Issue entitled SI: Exploiting human neurons. Copyright © 2016 Elsevier B.V. All rights reserved.

Honey bee (Apis mellifera) drones survive oxidative stress due to increased tolerance instead of avoidance or repair of oxidative damage

USDA-ARS?s Scientific Manuscript database

Oxidative stress can lead to premature aging symptoms and cause acute mortality at higher doses in a range of organisms. Oxidative stress resistance and longevity are mechanistically and phenotypically linked: considerable variation in oxidative stress resistance exists among and within species and ...

Organizational (role structuring) and personal (organizational commitment and job involvement) factors: do they predict interprofessional team effectiveness?

PubMed

Freund, Anat; Drach-Zahavy, Anat

2007-06-01

Teamwork in community clinics was examined to propose and test a model that views the different kinds of commitment (job involvement and organizational commitment) and the potential conflict between them, as mediators between personal and organizational factors (mechanistic structuring and organic structuring) and the effectiveness of interprofessional teamwork. Differences among the professional groups became evident with regard to their views of the goals of teamwork and the ways to achieve them. As for mechanistic structuring, although the clinic members saw their mechanistic structuring in a more bureaucratic sense, the combination of mechanistic structuring and organic structuring led to effective teamwork. In terms of commitment, while staff members were committed primarily to their job and not the organization, commitment to the organization produced effective teamwork in the clinics.

The great diversity of HMX conformers: probing the potential energy surface using CCSD(T).

PubMed

Molt, Robert W; Watson, Thomas; Bazanté, Alexandre P; Bartlett, Rodney J

2013-04-25

The octahydro-1,3,5,7-tetranitro-1,3,5,7-tetraazocine (HMX) molecule is a very commonly studied system, in all 3 phases, because of its importance as an explosive; however, no one has ever attempted a systematic study of what all the major gas-phase conformers are. This is critical to a mechanistic study of the kinetics involved, as well as the viability of various crystalline polymorphs based on the gas-phase conformers. We have used existing knowledge of basic cyclooctane chemistry to survey all possible HMX conformers based on its fundamental ring structure. After studying what geometries are possible after second-order many-body perturbation theory (MBPT(2)) geometry optimization, we calculated the energetics using coupled cluster singles, doubles, and perturbative triples (CCSD(T))/cc-pVTZ. These highly accurate energies allow us to better calculate starting points for future mechanistic studies. Additionally, the plethora of structures are compared to existing experimental data of crystals. It is found that the crystal field effect is sometimes large and sometimes small for HMX.

Simulating the Risk of Liver Fluke Infection using a Mechanistic Hydro-epidemiological Model

NASA Astrophysics Data System (ADS)

Beltrame, Ludovica; Dunne, Toby; Rose, Hannah; Walker, Josephine; Morgan, Eric; Vickerman, Peter; Wagener, Thorsten

2016-04-01

Liver Fluke (Fasciola hepatica) is a common parasite found in livestock and responsible for considerable economic losses throughout the world. Risk of infection is strongly influenced by climatic and hydrological conditions, which characterise the host environment for parasite development and transmission. Despite on-going control efforts, increases in fluke outbreaks have been reported in recent years in the UK, and have been often attributed to climate change. Currently used fluke risk models are based on empirical relationships derived between historical climate and incidence data. However, hydro-climate conditions are becoming increasingly non-stationary due to climate change and direct anthropogenic impacts such as land use change, making empirical models unsuitable for simulating future risk. In this study we introduce a mechanistic hydro-epidemiological model for Liver Fluke, which explicitly simulates habitat suitability for disease development in space and time, representing the parasite life cycle in connection with key environmental conditions. The model is used to assess patterns of Liver Fluke risk for two catchments in the UK under current and potential future climate conditions. Comparisons are made with a widely used empirical model employing different datasets, including data from regional veterinary laboratories. Results suggest that mechanistic models can achieve adequate predictive ability and support adaptive fluke control strategies under climate change scenarios.

A dynamic and mechanistic model of PCB bioaccumulation in the European hake ( Merluccius merluccius)

NASA Astrophysics Data System (ADS)

Bodiguel, Xavier; Maury, Olivier; Mellon-Duval, Capucine; Roupsard, François; Le Guellec, Anne-Marie; Loizeau, Véronique

2009-08-01

Bioaccumulation is difficult to document because responses differ among chemical compounds, with environmental conditions, and physiological processes characteristic of each species. We use a mechanistic model, based on the Dynamic Energy Budget (DEB) theory, to take into account this complexity and study factors impacting accumulation of organic pollutants in fish through ontogeny. The bioaccumulation model proposed is a comprehensive approach that relates evolution of hake PCB contamination to physiological information about the fish, such as diet, metabolism, reserve and reproduction status. The species studied is the European hake ( Merluccius merluccius, L. 1758). The model is applied to study the total concentration and the lipid normalised concentration of 4 PCB congeners in male and female hakes from the Gulf of Lions (NW Mediterranean sea) and the Bay of Biscay (NE Atlantic ocean). Outputs of the model compare consistently to measurements over the life span of fish. Simulation results clearly demonstrate the relative effects of food contamination, growth and reproduction on the PCB bioaccumulation in hake. The same species living in different habitats and exposed to different PCB prey concentrations exhibit marked difference in the body accumulation of PCBs. At the adult stage, female hakes have a lower PCB concentration compared to males for a given length. We successfully simulated these sex-specific PCB concentrations by considering two mechanisms: a higher energy allocation to growth for females and a transfer of PCBs from the female to its eggs when allocating lipids from reserve to eggs. Finally, by its mechanistic description of physiological processes, the model is relevant for other species and sets the stage for a mechanistic understanding of toxicity and ecological effects of organic contaminants in marine organisms.

Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women.

PubMed

Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2016-03-01

Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007-2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman's r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen's kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements.

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models.

PubMed

Zelić, B; Bolf, N; Vasić-Racki, D

2006-06-01

Three different models: the unstructured mechanistic black-box model, the input-output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249-258 (2004); Zelić et al. Eng Life Sci 3:299-305 (2003); Zelić et al Biotechnol Bioeng 85:638-646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

The coefficient of restitution of pressurized balls: a mechanistic model

NASA Astrophysics Data System (ADS)

Georgallas, Alex; Landry, Gaëtan

2016-01-01

Pressurized, inflated balls used in professional sports are regulated so that their behaviour upon impact can be anticipated and allow the game to have its distinctive character. However, the dynamics governing the impacts of such balls, even on stationary hard surfaces, can be extremely complex. The energy transformations, which arise from the compression of the gas within the ball and from the shear forces associated with the deformation of the wall, are examined in this paper. We develop a simple mechanistic model of the dependence of the coefficient of restitution, e, upon both the gauge pressure, P_G, of the gas and the shear modulus, G, of the wall. The model is validated using the results from a simple series of experiments using three different sports balls. The fits to the data are extremely good for P_G > 25 kPa and consistent values are obtained for the value of G for the wall material. As far as the authors can tell, this simple, mechanistic model of the pressure dependence of the coefficient of restitution is the first in the literature. *%K Coefficient of Restitution, Dynamics, Inflated Balls, Pressure, Impact Model

A Biologically Informed, Mechanistic Model of Desert Shrub Population Dynamics Bearing on Arid Landscape Evolution

NASA Astrophysics Data System (ADS)

Worman, Stacey; Furbish, David; Fathel, Siobhan

2014-05-01

In arid landscapes, desert shrubs individually and collectively modify how sediment is transported (e.g by wind, overland-flow, and rain-splash). Addressing how desert shrubs modify landscapes on geomorphic timescales therefore necessitates spanning multiple shrub lifetimes and accounting for how processes affecting shrub dynamics on these longer timescales (e.g. fire, grazing, drought, and climate change) may in turn impact sediment transport. To fulfill this need, we present a mechanistic model of the spatiotemporal dynamics of a desert-shrub population that uses a simple accounting framework and tracks individual shrubs as they enter, age, and exit the population (via recruitment, growth, and mortality). Our model is novel insomuch as it (1) features a strong biophysical foundation, (2) mimics well-documented aspects of how shrub populations respond to changes in precipitation, and (3) possesses the process granularity appropriate for use in geomorphic simulations. In a complimentary abstract (Fathel et al. 2014), we demonstrate the potential of this biological model by coupling it to a physical model of rain-splash sediment transport: We mechanistically reproduce the empirical observation that the erosion rate of a hillslope decreases as its vegetation coverage increases and we predict erosion rates under different climate-change scenarios.

Secondary dispersal driven by overland flow in drylands: Review and mechanistic model development.

PubMed

Thompson, Sally E; Assouline, Shmuel; Chen, Li; Trahktenbrot, Ana; Svoray, Tal; Katul, Gabriel G

2014-01-01

Seed dispersal alters gene flow, reproduction, migration and ultimately spatial organization of dryland ecosystems. Because many seeds in drylands lack adaptations for long-distance dispersal, seed transport by secondary processes such as tumbling in the wind or mobilization in overland flow plays a dominant role in determining where seeds ultimately germinate. Here, recent developments in modeling runoff generation in spatially complex dryland ecosystems are reviewed with the aim of proposing improvements to mechanistic modeling of seed dispersal processes. The objective is to develop a physically-based yet operational framework for determining seed dispersal due to surface runoff, a process that has gained recent experimental attention. A Buoyant OBject Coupled Eulerian - Lagrangian Closure model (BOB-CELC) is proposed to represent seed movement in shallow surface flows. The BOB-CELC is then employed to investigate the sensitivity of seed transport to landscape and storm properties and to the spatial configuration of vegetation patches interspersed within bare earth. The potential to simplify seed transport outcomes by considering the limiting behavior of multiple runoff events is briefly considered, as is the potential for developing highly mechanistic, spatially explicit models that link seed transport, vegetation structure and water movement across multiple generations of dryland plants.

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals.

PubMed

Forbes, Valery E; Salice, Chris J; Birnir, Bjorn; Bruins, Randy J F; Calow, Peter; Ducrot, Virginie; Galic, Nika; Garber, Kristina; Harvey, Bret C; Jager, Henriette; Kanarek, Andrew; Pastorok, Robert; Railsback, Steve F; Rebarber, Richard; Thorbek, Pernille

2017-04-01

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. Environ Toxicol Chem 2017;36:845-859. © 2017 SETAC. © 2017 SETAC.

Mechanistic Modeling of Hemoglobin Glycation and Red Blood Cell Kinetics Enables Personalized Diabetes Monitoring

PubMed Central

Malka, Roy; Nathan, David M.; Higgins, John M.

2017-01-01

The glycated hemoglobin assay (HbA1c) is essential for the diagnosis and management of diabetes because it provides the best estimate of a patient’s average blood glucose (AG) over the preceding 2–3 months and is the best predictor of disease complications. However, there is substantial unexplained glucose-independent variation in HbA1c that makes AG estimation inaccurate and limits the precision of medical care for diabetics. The true AG of a non-diabetic and a poorly-controlled diabetic may differ by less than 15 mg/dL, but patients with identical HbA1c and thus identical HbA1c-based estimates of AG may have true AG that differs by more than 60 mg/dl. We combine a mechanistic mathematical model of hemoglobin glycation and red blood cell flux with large sets of intra-patient glucose measurements to derive patient-specific estimates of non-glycemic determinants of HbA1c including mean red blood cell age (MRBC). We find that interpatient variation in derived MRBC explains all glucose-independent variation in HbA1c. We then use our model to personalize prospective estimates of AG and reduce errors by more than 50% in four independent sets of more than 200 patients. The current standard of care provided AG estimates with errors > 15 mg/dL for 1 in 3 patients. Our patient-specific method reduced this error rate to 1 in 10. This personalized approach to estimating AG from HbA1c should improve medical care for diabetes using existing clinical measurements. PMID:27708063

Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks.

PubMed

Hartemink, Nienke; Vanwambeke, Sophie O; Purse, Bethan V; Gilbert, Marius; Van Dyck, Hans

2015-11-01

Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional habitat of vectors or hosts, and hence of the pathogen. Empirical-statistical methods do not explicitly incorporate biological mechanisms, whereas current mechanistic models are rarely spatially explicit; both methods ignore the way animals use the landscape (i.e. movement ecology). We argue that applying a functional concept for habitat, i.e. the resource-based habitat concept (RBHC), can solve these issues. The RBHC offers a framework to identify systematically the different ecological resources that are necessary for the completion of the transmission cycle and to relate these resources to (combinations of) landscape features and other environmental factors. The potential of the RBHC as a framework for identifying suitable habitats for vector-borne pathogens is explored and illustrated with the case of bluetongue virus, a midge-transmitted virus affecting ruminants. The concept facilitates the study of functional habitats of the interacting species (vectors as well as hosts) and provides new insight into spatial and temporal variation in transmission opportunities and exposure that ultimately determine disease risks. It may help to identify knowledge gaps and control options arising from changes in the spatial configuration of key resources across the landscape. The RBHC framework may act as a bridge between existing mechanistic and statistical modelling approaches. © 2014 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.

Reliability of intracerebral hemorrhage classification systems: A systematic review.

PubMed

Rannikmäe, Kristiina; Woodfield, Rebecca; Anderson, Craig S; Charidimou, Andreas; Chiewvit, Pipat; Greenberg, Steven M; Jeng, Jiann-Shing; Meretoja, Atte; Palm, Frederic; Putaala, Jukka; Rinkel, Gabriel Je; Rosand, Jonathan; Rost, Natalia S; Strbian, Daniel; Tatlisumak, Turgut; Tsai, Chung-Fen; Wermer, Marieke Jh; Werring, David; Yeh, Shin-Joe; Al-Shahi Salman, Rustam; Sudlow, Cathie Lm

2016-08-01

Accurately distinguishing non-traumatic intracerebral hemorrhage (ICH) subtypes is important since they may have different risk factors, causal pathways, management, and prognosis. We systematically assessed the inter- and intra-rater reliability of ICH classification systems. We sought all available reliability assessments of anatomical and mechanistic ICH classification systems from electronic databases and personal contacts until October 2014. We assessed included studies' characteristics, reporting quality and potential for bias; summarized reliability with kappa value forest plots; and performed meta-analyses of the proportion of cases classified into each subtype. We included 8 of 2152 studies identified. Inter- and intra-rater reliabilities were substantial to perfect for anatomical and mechanistic systems (inter-rater kappa values: anatomical 0.78-0.97 [six studies, 518 cases], mechanistic 0.89-0.93 [three studies, 510 cases]; intra-rater kappas: anatomical 0.80-1 [three studies, 137 cases], mechanistic 0.92-0.93 [two studies, 368 cases]). Reporting quality varied but no study fulfilled all criteria and none was free from potential bias. All reliability studies were performed with experienced raters in specialist centers. Proportions of ICH subtypes were largely consistent with previous reports suggesting that included studies are appropriately representative. Reliability of existing classification systems appears excellent but is unknown outside specialist centers with experienced raters. Future reliability comparisons should be facilitated by studies following recently published reporting guidelines. © 2016 World Stroke Organization.

Assessment and improvement of biotransfer models to cow's milk and beef used in exposure assessment tools for organic pollutants.

PubMed

Takaki, Koki; Wade, Andrew J; Collins, Chris D

2015-11-01

The aim of this study was to assess and improve the accuracy of biotransfer models for the organic pollutants (PCBs, PCDD/Fs, PBDEs, PFCAs, and pesticides) into cow's milk and beef used in human exposure assessment. Metabolic rate in cattle is known as a key parameter for this biotransfer, however few experimental data and no simulation methods are currently available. In this research, metabolic rate was estimated using existing QSAR biodegradation models of microorganisms (BioWIN) and fish (EPI-HL and IFS-HL). This simulated metabolic rate was then incorporated into the mechanistic cattle biotransfer models (RAIDAR, ACC-HUMAN, OMEGA, and CKow). The goodness of fit tests showed that RAIDAR, ACC-HUMAN, OMEGA model performances were significantly improved using either of the QSARs when comparing the new model outputs to observed data. The CKow model is the only one that separates the processes in the gut and liver. This model showed the lowest residual error of all the models tested when the BioWIN model was used to represent the ruminant metabolic process in the gut and the two fish QSARs were used to represent the metabolic process in the liver. Our testing included EUSES and CalTOX which are KOW-regression models that are widely used in regulatory assessment. New regressions based on the simulated rate of the two metabolic processes are also proposed as an alternative to KOW-regression models for a screening risk assessment. The modified CKow model is more physiologically realistic, but has equivalent usability to existing KOW-regression models for estimating cattle biotransfer of organic pollutants. Copyright © 2015. Published by Elsevier Ltd.

How to make a tree ring: Coupling stem water flow and cambial activity in mature Alpine conifers

NASA Astrophysics Data System (ADS)

Peters, Richard L.; Frank, David C.; Treydte, Kerstin; Steppe, Kathy; Kahmen, Ansgar; Fonti, Patrick

2017-04-01

Inter-annual tree-ring measurements are used to understand tree-growth responses to climatic variability and reconstruct past climate conditions. In parallel, mechanistic models use experimentally defined plant-atmosphere interactions to explain past growth responses and predict future environmental impact on forest productivity. Yet, substantial inconsistencies within mechanistic model ensembles and mismatches with empirical data indicate that significant progress is still needed to understand the processes occurring at an intra-annual resolution that drive annual growth. However, challenges arise due to i) few datasets describing climatic responses of high-resolution physiological processes over longer time-scales, ii) uncertainties on the main mechanistic process limiting radial stem growth and iii) complex interactions between multiple environmental factors which obscure detection of the main stem growth driver, generating a gap between our understanding of intra- and inter-annual growth mechanisms. We attempt to bridge the gap between inter-annual tree-ring width and sub-daily radial stem-growth and provide a mechanistic perspective on how environmental conditions affect physiological processes that shape tree rings in conifers. We combine sub-hourly sap flow and point dendrometer measurements performed on mature Alpine conifers (Larix decidua) into an individual-based mechanistic tree-growth model to simulate sub-hourly cambial activity. The monitored trees are located along a high elevational transect in the Swiss Alps (Lötschental) to analyse the effect of increasing temperature. The model quantifies internal tree hydraulic pathways that regulate the turgidity within the cambial zone and induce cell enlargement for radial growth. The simulations are validated against intra-annual growth patterns derived from xylogenesis data and anatomical analyses. Our efforts advance the process-based understanding of how climate shapes the annual tree-ring structures and could potentially improve our ability to reconstruct the climate of the past and predict future growth under changing climate.

New Simulation Methods to Facilitate Achieving a Mechanistic Understanding of Basic Pharmacology Principles in the Classroom

ERIC Educational Resources Information Center

Grover, Anita; Lam, Tai Ning; Hunt, C. Anthony

2008-01-01

We present a simulation tool to aid the study of basic pharmacology principles. By taking advantage of the properties of agent-based modeling, the tool facilitates taking a mechanistic approach to learning basic concepts, in contrast to the traditional empirical methods. Pharmacodynamics is a particular aspect of pharmacology that can benefit from…

Mechanistic analysis of challenge-response experiments.

PubMed

Shotwell, M S; Drake, K J; Sidorov, V Y; Wikswo, J P

2013-09-01

We present an application of mechanistic modeling and nonlinear longitudinal regression in the context of biomedical response-to-challenge experiments, a field where these methods are underutilized. In this type of experiment, a system is studied by imposing an experimental challenge, and then observing its response. The combination of mechanistic modeling and nonlinear longitudinal regression has brought new insight, and revealed an unexpected opportunity for optimal design. Specifically, the mechanistic aspect of our approach enables the optimal design of experimental challenge characteristics (e.g., intensity, duration). This article lays some groundwork for this approach. We consider a series of experiments wherein an isolated rabbit heart is challenged with intermittent anoxia. The heart responds to the challenge onset, and recovers when the challenge ends. The mean response is modeled by a system of differential equations that describe a candidate mechanism for cardiac response to anoxia challenge. The cardiac system behaves more variably when challenged than when at rest. Hence, observations arising from this experiment exhibit complex heteroscedasticity and sharp changes in central tendency. We present evidence that an asymptotic statistical inference strategy may fail to adequately account for statistical uncertainty. Two alternative methods are critiqued qualitatively (i.e., for utility in the current context), and quantitatively using an innovative Monte-Carlo method. We conclude with a discussion of the exciting opportunities in optimal design of response-to-challenge experiments. © 2013, The International Biometric Society.

NEAMS update quarterly report for January - March 2012.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradley, K.S.; Hayes, S.; Pointer, D.

Quarterly highlights are: (1) The integration of Denovo and AMP was demonstrated in an AMP simulation of the thermo-mechanics of a complete fuel assembly; (2) Bison was enhanced with a mechanistic fuel cracking model; (3) Mechanistic algorithms were incorporated into various lower-length-scale models to represent fission gases and dislocations in UO2 fuels; (4) Marmot was improved to allow faster testing of mesoscale models using larger problem domains; (5) Component models of reactor piping were developed for use in Relap-7; (6) The mesh generator of Proteus was updated to accept a mesh specification from Moose and equations were formulated for themore » intermediate-fidelity Proteus-2D1D module; (7) A new pressure solver was implemented in Nek5000 and demonstrated to work 2.5 times faster than the previous solver; (8) Work continued on volume-holdup models for two fuel reprocessing operations: voloxidation and dissolution; (9) Progress was made on a pyroprocessing model and the characterization of pyroprocessing emission signatures; (10) A new 1D groundwater waste transport code was delivered to the used fuel disposition (UFD) campaign; (11) Efforts on waste form modeling included empirical simulation of sodium-borosilicate glass compositions; (12) The Waste team developed three prototypes for modeling hydride reorientation in fuel cladding during very long-term fuel storage; (13) A benchmark demonstration problem (fission gas bubble growth) was modeled to evaluate the capabilities of different meso-scale numerical methods; (14) Work continued on a hierarchical up-scaling framework to model structural materials by directly coupling dislocation dynamics and crystal plasticity; (15) New 'importance sampling' methods were developed and demonstrated to reduce the computational cost of rare-event inference; (16) The survey and evaluation of existing data and knowledge bases was updated for NE-KAMS; (17) The NEAMS Early User Program was launched; (18) The Nuclear Regulatory Commission (NRC) Office of Regulatory Research was introduced to the NEAMS program; (19) The NEAMS overall software quality assurance plan (SQAP) was revised to version 1.5; and (20) Work continued on NiCE and its plug-ins and other utilities, such as Cubit and VisIt.« less

Validation of a Mechanistic Model for Non-Invasive Study of Ecological Energetics in an Endangered Wading Bird with Counter-Current Heat Exchange in its Legs.

PubMed

Fitzpatrick, Megan J; Mathewson, Paul D; Porter, Warren P

2015-01-01

Mechanistic models provide a powerful, minimally invasive tool for gaining a deeper understanding of the ecology of animals across geographic space and time. In this paper, we modified and validated the accuracy of the mechanistic model Niche Mapper for simulating heat exchanges of animals with counter-current heat exchange mechanisms in their legs and animals that wade in water. We then used Niche Mapper to explore the effects of wading and counter-current heat exchange on the energy expenditures of Whooping Cranes, a long-legged wading bird. We validated model accuracy against the energy expenditure of two captive Whooping Cranes measured using the doubly-labeled water method and time energy budgets. Energy expenditure values modeled by Niche Mapper were similar to values measured by the doubly-labeled water method and values estimated from time-energy budgets. Future studies will be able to use Niche Mapper as a non-invasive tool to explore energy-based limits to the fundamental niche of Whooping Cranes and apply this knowledge to management decisions. Basic questions about the importance of counter-current exchange and wading to animal physiological tolerances can also now be explored with the model.

Validation of a Mechanistic Model for Non-Invasive Study of Ecological Energetics in an Endangered Wading Bird with Counter-Current Heat Exchange in its Legs

PubMed Central

Fitzpatrick, Megan J.; Mathewson, Paul D.; Porter, Warren P.

2015-01-01

Mechanistic models provide a powerful, minimally invasive tool for gaining a deeper understanding of the ecology of animals across geographic space and time. In this paper, we modified and validated the accuracy of the mechanistic model Niche Mapper for simulating heat exchanges of animals with counter-current heat exchange mechanisms in their legs and animals that wade in water. We then used Niche Mapper to explore the effects of wading and counter-current heat exchange on the energy expenditures of Whooping Cranes, a long-legged wading bird. We validated model accuracy against the energy expenditure of two captive Whooping Cranes measured using the doubly-labeled water method and time energy budgets. Energy expenditure values modeled by Niche Mapper were similar to values measured by the doubly-labeled water method and values estimated from time-energy budgets. Future studies will be able to use Niche Mapper as a non-invasive tool to explore energy-based limits to the fundamental niche of Whooping Cranes and apply this knowledge to management decisions. Basic questions about the importance of counter-current exchange and wading to animal physiological tolerances can also now be explored with the model. PMID:26308207

A mechanistic soil biogeochemistry model with explicit representation of microbial and macrofaunal activities and nutrient cycles

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Manzoni, Stefano; Or, Dani; Paschalis, Athanasios

2016-04-01

The potential of a given ecosystem to store and release carbon is inherently linked to soil biogeochemical processes. These processes are deeply connected to the water, energy, and vegetation dynamics above and belowground. Recently, it has been advocated that a mechanistic representation of soil biogeochemistry require: (i) partitioning of soil organic carbon (SOC) pools according to their functional role; (ii) an explicit representation of microbial dynamics; (iii) coupling of carbon and nutrient cycles. While some of these components have been introduced in specialized models, they have been rarely implemented in terrestrial biosphere models and tested in real cases. In this study, we combine a new soil biogeochemistry model with an existing model of land-surface hydrology and vegetation dynamics (T&C). Specifically the soil biogeochemistry component explicitly separates different litter pools and distinguishes SOC in particulate, dissolved and mineral associated fractions. Extracellular enzymes and microbial pools are explicitly represented differentiating the functional roles of bacteria, saprotrophic and mycorrhizal fungi. Microbial activity depends on temperature, soil moisture and litter or SOC stoichiometry. The activity of macrofauna is also modeled. Nutrient dynamics include the cycles of nitrogen, phosphorous and potassium. The model accounts for feedbacks between nutrient limitations and plant growth as well as for plant stoichiometric flexibility. In turn, litter input is a function of the simulated vegetation dynamics. Root exudation and export to mycorrhiza are computed based on a nutrient uptake cost function. The combined model is tested to reproduce respiration dynamics and nitrogen cycle in few sites where data were available to test plausibility of results across a range of different metrics. For instance in a Swiss grassland ecosystem, fine root, bacteria, fungal and macrofaunal respiration account for 40%, 23%, 33% and 4% of total belowground respiration, respectively. Root exudation and carbon export to mycorrhizal represent about 7% of plant Net Primary Production. The model allows exploring the temporal dynamics of respiration fluxes from the different ecosystem components and designing virtual experiments on the controls exerted by environmental variables and/or soil microbes and mycorrhizal associations on soil carbon storage, plant growth, and nutrient leaching.

Mechanistic understanding of cellular level of water in plant-based food material

NASA Astrophysics Data System (ADS)

Khan, Md. Imran H.; Kumar, C.; Karim, M. A.

2017-06-01

Understanding of water distribution in plant-based food material is crucial for developing an accurate heat and mass transfer drying model. Generally, in plant-based food tissue, water is distributed in three different spaces namely, intercellular water, intracellular water, and cell wall water. For hygroscopic material, these three types of water transport should be considered for actual understanding of heat and mass transfer during drying. However, there is limited study dedicated to the investigation of the moisture distribution in a different cellular environment in the plant-based food material. Therefore, the aim of the present study was to investigate the proportion of intercellular water, intracellular water, and cell wall water inside the plant-based food material. During this study, experiments were performed for two different plant-based food tissues namely, eggplant and potato tissue using 1H-NMR-T2 relaxometry. Various types of water component were calculated by using multicomponent fits of the T2 relaxation curves. The experimental result showed that in potato tissue 80-82% water exist in intracellular space; 10-13% water in intercellular space and only 4-6% water exist in the cell wall space. In eggplant tissue, 90-93% water in intracellular space, 4-6% water exists in intercellular space and the remaining percentage of water is recognized as cell wall water. The investigated results quantify different types of water in plant-based food tissue. The highest proportion of water exists in intracellular spaces. Therefore, it is necessary to include different transport mechanism for intracellular, intercellular and cell wall water during modelling of heat and mass transfer during drying.

Bird Migration Under Climate Change - A Mechanistic Approach Using Remote Sensing

NASA Technical Reports Server (NTRS)

Smith, James A.; Blattner, Tim; Messmer, Peter

2010-01-01

The broad-scale reductions and shifts that may be expected under climate change in the availability and quality of stopover habitat for long-distance migrants is an area of increasing concern for conservation biologists. Researchers generally have taken two broad approaches to the modeling of migration behaviour to understand the impact of these changes on migratory bird populations. These include models based on causal processes and their response to environmental stimulation, "mechanistic models", or models that primarily are based on observed animal distribution patterns and the correlation of these patterns with environmental variables, i.e. "data driven" models. Investigators have applied the latter technique to forecast changes in migration patterns with changes in the environment, for example, as might be expected under climate change, by forecasting how the underlying environmental data layers upon which the relationships are built will change over time. The learned geostatstical correlations are then applied to the modified data layers.. However, this is problematic. Even if the projections of how the underlying data layers will change are correct, it is not evident that the statistical relationships will remain the same, i.e. that the animal organism may not adapt its' behaviour to the changing conditions. Mechanistic models that explicitly take into account the physical, biological, and behaviour responses of an organism as well as the underlying changes in the landscape offer an alternative to address these shortcomings. The availability of satellite remote sensing observations at multiple spatial and temporal scales, coupled with advances in climate modeling and information technologies enable the application of the mechanistic models to predict how continental bird migration patterns may change in response to environmental change. In earlier work, we simulated the impact of effects of wetland loss and inter-annual variability on the fitness of migratory shorebirds in the central fly ways of North America. We demonstrated the phenotypic plasticity of a migratory population of Pectoral sandpipers consisting of an ensemble of 10,000 individual birds in response to changes in stopover locations using an individual based migration model driven by remotely sensed land surface data, climate data and biological field data. With the advent of new computing capabilities enabled hy recent GPU-GP computing paradigms and commodity hardware, it now is possible to simulate both larger ensemble populations and to incorporate more realistic mechanistic factors into migration models. Here, we take our first steps use these tools to study the impact of long-term drought variability on shorebird survival.

Animal Models of Diabetic Retinopathy: Summary and Comparison

PubMed Central

Lo, Amy C. Y.

2013-01-01

Diabetic retinopathy (DR) is a microvascular complication associated with chronic exposure to hyperglycemia and is a major cause of blindness worldwide. Although clinical assessment and retinal autopsy of diabetic patients provide information on the features and progression of DR, its underlying pathophysiological mechanism cannot be deduced. In order to have a better understanding of the development of DR at the molecular and cellular levels, a variety of animal models have been developed. They include pharmacological induction of hyperglycemia and spontaneous diabetic rodents as well as models of angiogenesis without diabetes (to compensate for the absence of proliferative DR symptoms). In this review, we summarize the existing protocols to induce diabetes using STZ. We also describe and compare the pathological presentations, in both morphological and functional aspects, of the currently available DR animal models. The advantages and disadvantages of using different animals, ranging from zebrafish, rodents to other higher-order mammals, are also discussed. Until now, there is no single model that displays all the clinical features of DR as seen in human. Yet, with the understanding of the pathological findings in these animal models, researchers can select the most suitable models for mechanistic studies or drug screening. PMID:24286086

Unification and mechanistic detail as drivers of model construction: models of networks in economics and sociology.

PubMed

Kuorikoski, Jaakko; Marchionni, Caterina

2014-12-01

We examine the diversity of strategies of modelling networks in (micro) economics and (analytical) sociology. Field-specific conceptions of what explaining (with) networks amounts to or systematic preference for certain kinds of explanatory factors are not sufficient to account for differences in modelling methodologies. We argue that network models in both sociology and economics are abstract models of network mechanisms and that differences in their modelling strategies derive to a large extent from field-specific conceptions of the way in which a good model should be a general one. Whereas the economics models aim at unification, the sociological models aim at a set of mechanism schemas that are extrapolatable to the extent that the underlying psychological mechanisms are general. These conceptions of generality induce specific biases in mechanistic explanation and are related to different views of when knowledge from different fields should be seen as relevant.

A physical description of fission product behavior fuels for advanced power reactors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaganas, G.; Rest, J.; Nuclear Engineering Division

2007-10-18

The Global Nuclear Energy Partnership (GNEP) is considering a list of reactors and nuclear fuels as part of its chartered initiative. Because many of the candidate materials have not been explored experimentally under the conditions of interest, and in order to economize on program costs, analytical support in the form of combined first principle and mechanistic modeling is highly desirable. The present work is a compilation of mechanistic models developed in order to describe the fission product behavior of irradiated nuclear fuel. The mechanistic nature of the model development allows for the possibility of describing a range of nuclear fuelsmore » under varying operating conditions. Key sources include the FASTGRASS code with an application to UO{sub 2} power reactor fuel and the Dispersion Analysis Research Tool (DART ) with an application to uranium-silicide and uranium-molybdenum research reactor fuel. Described behavior mechanisms are divided into subdivisions treating fundamental materials processes under normal operation as well as the effect of transient heating conditions on these processes. Model topics discussed include intra- and intergranular gas-atom and bubble diffusion, bubble nucleation and growth, gas-atom re-solution, fuel swelling and ?scion gas release. In addition, the effect of an evolving microstructure on these processes (e.g., irradiation-induced recrystallization) is considered. The uranium-alloy fuel, U-xPu-Zr, is investigated and behavior mechanisms are proposed for swelling in the {alpha}-, intermediate- and {gamma}-uranium zones of this fuel. The work reviews the FASTGRASS kinetic/mechanistic description of volatile ?scion products and, separately, the basis for the DART calculation of bubble behavior in amorphous fuels. Development areas and applications for physical nuclear fuel models are identified.« less

Using in vitro models for expression profiling studies on ethanol and drugs of abuse.

PubMed

Thibault, Christelle; Hassan, Sajida; Miles, Michael

2005-03-01

The use of expression profiling with microarrays offers great potential for studying the mechanisms of action of drugs of abuse. Studies with the intact nervous system seem likely to be most relevant to understanding the mechanisms of drug abuse-related behaviours. However, the use of expression profiling with in vitro culture models offers significant advantages for identifying details of cellular signalling actions and toxicity for drugs of abuse. This study discusses general issues of the use of microarrays and cell culture models for studies on drugs of abuse. Specific results from existing studies are also discussed, providing clear examples of relevance for in vitro studies on ethanol, nicotine, opiates, cannabinoids and hallucinogens such as LSD. In addition to providing details on signalling mechanisms relevant to the neurobiology of drugs of abuse, microarray studies on a variety of cell culture systems have also provided important information on mechanisms of cellular/organ toxicity with drugs of abuse. Efforts to integrate genomic studies on drugs of abuse with both in vivo and in vitro models offer the potential for novel mechanistic rigor and physiological relevance.

Charge transfer in model peptides: obtaining Marcus parameters from molecular simulation.

PubMed

Heck, Alexander; Woiczikowski, P Benjamin; Kubař, Tomáš; Giese, Bernd; Elstner, Marcus; Steinbrecher, Thomas B

2012-02-23

Charge transfer within and between biomolecules remains a highly active field of biophysics. Due to the complexities of real systems, model compounds are a useful alternative to study the mechanistic fundamentals of charge transfer. In recent years, such model experiments have been underpinned by molecular simulation methods as well. In this work, we study electron hole transfer in helical model peptides by means of molecular dynamics simulations. A theoretical framework to extract Marcus parameters of charge transfer from simulations is presented. We find that the peptides form stable helical structures with sequence dependent small deviations from ideal PPII helices. We identify direct exposure of charged side chains to solvent as a cause of high reorganization energies, significantly larger than typical for electron transfer in proteins. This, together with small direct couplings, makes long-range superexchange electron transport in this system very slow. In good agreement with experiment, direct transfer between the terminal amino acid side chains can be dicounted in favor of a two-step hopping process if appropriate bridging groups exist. © 2012 American Chemical Society

Temporal Regulation by Innate Type 2 Cytokines in Food Allergies.

PubMed

Graham, Michelle T; Andorf, Sandra; Spergel, Jonathan M; Chatila, Talal A; Nadeau, Kari C

2016-10-01

Food allergies (FAs) are a growing epidemic in western countries with poorly defined etiology. Defined as an adverse immune response to common food allergens, FAs present heterogeneously as a single- or multi-organ response that ranges in severity from localized hives and angioedema to systemic anaphylaxis. Current research focusing on epithelial-derived cytokines contends that temporal regulation by these factors impact initial sensitization and persistence of FA responses upon repeated food allergen exposure. Mechanistic understanding of FA draws insight from a myriad of atopic conditions studied in humans and modeled in mice. In this review, we will highlight how epithelial-derived cytokines initiate and then potentiate FAs. We will also review existing evidence of the contribution of other atopic diseases to FA pathogenesis and whether FA symptoms overlap with other atopic diseases.

Membrane curvature and its generation by BAR proteins

PubMed Central

Mim, Carsten; Unger, Vinzenz M

2012-01-01

Membranes are flexible barriers that surround the cell and its compartments. To execute vital functions such as locomotion or receptor turnover, cells need to control the shapes of their membranes. In part, this control is achieved through membrane-bending proteins, such as the bin/amphiphysin/rvs domain (BAR) proteins. Many open questions remain about the mechanisms by which membrane-bending proteins function. Addressing this shortfall, recent structures of BAR protein:membrane complexes support existing mechanistic models, but also produced novel insights into how BAR-domain proteins sense, stabilize and generate curvature. Here we review these recent findings, focusing on how BAR proteins interact with the membrane, and how the resulting scaffold structures might aid the recruitment of other proteins to the sites where membranes are bent. PMID:23058040

Using energy budgets to combine ecology and toxicology in a mammalian sentinel species

NASA Astrophysics Data System (ADS)

Desforges, Jean-Pierre W.; Sonne, Christian; Dietz, Rune

2017-04-01

Process-driven modelling approaches can resolve many of the shortcomings of traditional descriptive and non-mechanistic toxicology. We developed a simple dynamic energy budget (DEB) model for the mink (Mustela vison), a sentinel species in mammalian toxicology, which coupled animal physiology, ecology and toxicology, in order to mechanistically investigate the accumulation and adverse effects of lifelong dietary exposure to persistent environmental toxicants, most notably polychlorinated biphenyls (PCBs). Our novel mammalian DEB model accurately predicted, based on energy allocations to the interconnected metabolic processes of growth, development, maintenance and reproduction, lifelong patterns in mink growth, reproductive performance and dietary accumulation of PCBs as reported in the literature. Our model results were consistent with empirical data from captive and free-ranging studies in mink and other wildlife and suggest that PCB exposure can have significant population-level impacts resulting from targeted effects on fetal toxicity, kit mortality and growth and development. Our approach provides a simple and cross-species framework to explore the mechanistic interactions of physiological processes and ecotoxicology, thus allowing for a deeper understanding and interpretation of stressor-induced adverse effects at all levels of biological organization.

Modeling food matrix effects on chemical reactivity: Challenges and perspectives.

PubMed

Capuano, Edoardo; Oliviero, Teresa; van Boekel, Martinus A J S

2017-06-29

The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.

Mechanistic modeling of insecticide risks to breeding birds in North American agroecosystems

PubMed Central

Garber, Kristina; Odenkirchen, Edward

2017-01-01

Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. There is accumulating evidence that insecticides adversely affect non-target wildlife species, including birds, causing mortality, reproductive impairment, and indirect effects through loss of prey base, and the type and magnitude of such effects differs by chemical class, or mode of action. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. Current USEPA risk assessments for pesticides generally rely on endpoints from laboratory based toxicity studies focused on groups of individuals and do not directly assess population-level endpoints. In this paper, we present a mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to forage in agricultural fields during their breeding season. This model relies on individual-based toxicity data and translates effects into endpoints meaningful at the population level (i.e., magnitude of mortality and reproductive impairment). The model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model was used to assess the relative risk of 12 insecticides applied via aerial spray to control corn pests on a suite of 31 avian species known to forage in cornfields in agroecosystems of the Midwest, USA. We found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and λ-cyhalothrin (pyrethroids) posing the least risk. Comparative sensitivity analysis across the 31 species showed that ecological trait parameters related to the timing of breeding and reproductive output per nest attempt offered the greatest explanatory power for predicting the magnitude of risk. An important advantage of TIM/MCnest is that it allows risk assessors to rationally combine both acute (lethal) and chronic (reproductive) effects into a single unified measure of risk. PMID:28467479

Mechanistic modeling of insecticide risks to breeding birds in North American agroecosystems.

PubMed

Etterson, Matthew; Garber, Kristina; Odenkirchen, Edward

2017-01-01

Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. There is accumulating evidence that insecticides adversely affect non-target wildlife species, including birds, causing mortality, reproductive impairment, and indirect effects through loss of prey base, and the type and magnitude of such effects differs by chemical class, or mode of action. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. Current USEPA risk assessments for pesticides generally rely on endpoints from laboratory based toxicity studies focused on groups of individuals and do not directly assess population-level endpoints. In this paper, we present a mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to forage in agricultural fields during their breeding season. This model relies on individual-based toxicity data and translates effects into endpoints meaningful at the population level (i.e., magnitude of mortality and reproductive impairment). The model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model was used to assess the relative risk of 12 insecticides applied via aerial spray to control corn pests on a suite of 31 avian species known to forage in cornfields in agroecosystems of the Midwest, USA. We found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and λ-cyhalothrin (pyrethroids) posing the least risk. Comparative sensitivity analysis across the 31 species showed that ecological trait parameters related to the timing of breeding and reproductive output per nest attempt offered the greatest explanatory power for predicting the magnitude of risk. An important advantage of TIM/MCnest is that it allows risk assessors to rationally combine both acute (lethal) and chronic (reproductive) effects into a single unified measure of risk.

Mouse models to study the effect of cardiovascular risk factors on brain structure and cognition

PubMed Central

Bink, Diewertje I; Ritz, Katja; Aronica, Eleonora; van der Weerd, Louise; Daemen, Mat JAP

2013-01-01

Recent clinical data indicates that hemodynamic changes caused by cardiovascular diseases such as atherosclerosis, heart failure, and hypertension affect cognition. Yet, the underlying mechanisms of the resulting vascular cognitive impairment (VCI) are poorly understood. One reason for the lack of mechanistic insights in VCI is that research in dementia primarily focused on Alzheimer's disease models. To fill in this gap, we critically reviewed the published data and various models of VCI. Typical findings in VCI include reduced cerebral perfusion, blood–brain barrier alterations, white matter lesions, and cognitive deficits, which have also been reported in different cardiovascular mouse models. However, the tests performed are incomplete and differ between models, hampering a direct comparison between models and studies. Nevertheless, from the currently available data we conclude that a few existing surgical animal models show the key features of vascular cognitive decline, with the bilateral common carotid artery stenosis hypoperfusion mouse model as the most promising model. The transverse aortic constriction and myocardial infarction models may be good alternatives, but these models are as yet less characterized regarding the possible cerebral changes. Mixed models could be used to study the combined effects of different cardiovascular diseases on the deterioration of cognition during aging. PMID:23963364

Modeling the effects of ozone on soybean growth and yield.

PubMed

Kobayashi, K; Miller, J E; Flagler, R B; Heck, W W

1990-01-01

A simple mechanistic model was developed based on an existing growth model in order to address the mechanisms of the effects of ozone on growth and yield of soybean [Glycine max. (L.) Merr. 'Davis'] and interacting effects of other environmental stresses. The model simulates daily growth of soybean plants using environmental data including shortwave radiation, temperature, precipitation, irrigation and ozone concentration. Leaf growth, dry matter accumulation, water budget, nitrogen input and seed growth linked to senescence and abscission of leaves are described in the model. The effects of ozone are modeled as reduced photosynthate production and accelerated senescence. The model was applied to the open-top chamber experiments in which soybean plants were exposed to ozone under two levels of soil moisture regimes. After calibrating the model to the growth data and seed yield, goodness-of-fit of the model was tested. The model fitted well for top dry weight in the vegetative growth phase and also at maturity. The effect of ozone on seen yield was also described satisfactorily by the model. The simulation showed apparent interaction between the effect of ozone and soil moisture stress on the seed yield. The model revealed that further work is needed concerning the effect of ozone on the senescence process and the consequences of alteration of canopy microclimate by the open-top chambers.

Integration of biotic ligand models (BLM) and bioaccumulation kinetics into a mechanistic framework for metal uptake in aquatic organisms.

PubMed

Veltman, Karin; Huijbregts, Mark A J; Hendriks, A Jan

2010-07-01

Both biotic ligand models (BLM) and bioaccumulation models aim to quantify metal exposure based on mechanistic knowledge, but key factors included in the description of metal uptake differ between the two approaches. Here, we present a quantitative comparison of both approaches and show that BLM and bioaccumulation kinetics can be merged into a common mechanistic framework for metal uptake in aquatic organisms. Our results show that metal-specific absorption efficiencies calculated from BLM-parameters for freshwater fish are highly comparable, i.e. within a factor of 2.4 for silver, cadmium, copper, and zinc, to bioaccumulation-absorption efficiencies for predominantly marine fish. Conditional affinity constants are significantly related to the metal-specific covalent index. Additionally, the affinity constants of calcium, cadmium, copper, sodium, and zinc are significantly comparable across aquatic species, including molluscs, daphnids, and fish. This suggests that affinity constants can be estimated from the covalent index, and constants can be extrapolated across species. A new model is proposed that integrates the combined effect of metal chemodynamics, as speciation, competition, and ligand affinity, and species characteristics, as size, on metal uptake by aquatic organisms. An important direction for further research is the quantitative comparison of the proposed model with acute toxicity values for organisms belonging to different size classes.

Fidelity in Animal Modeling: Prerequisite for a Mechanistic Research Front Relevant to the Inflammatory Incompetence of Acute Pediatric Malnutrition.

PubMed

Woodward, Bill

2016-04-11

Inflammatory incompetence is characteristic of acute pediatric protein-energy malnutrition, but its underlying mechanisms remain obscure. Perhaps substantially because the research front lacks the driving force of a scholarly unifying hypothesis, it is adrift and research activity is declining. A body of animal-based research points to a unifying paradigm, the Tolerance Model, with some potential to offer coherence and a mechanistic impetus to the field. However, reasonable skepticism prevails regarding the relevance of animal models of acute pediatric malnutrition; consequently, the fundamental contributions of the animal-based component of this research front are largely overlooked. Design-related modifications to improve the relevance of animal modeling in this research front include, most notably, prioritizing essential features of pediatric malnutrition pathology rather than dietary minutiae specific to infants and children, selecting windows of experimental animal development that correspond to targeted stages of pediatric immunological ontogeny, and controlling for ontogeny-related confounders. In addition, important opportunities are presented by newer tools including the immunologically humanized mouse and outbred stocks exhibiting a magnitude of genetic heterogeneity comparable to that of human populations. Sound animal modeling is within our grasp to stimulate and support a mechanistic research front relevant to the immunological problems that accompany acute pediatric malnutrition.

Changes in Black-legged Tick Population in New England with Future Climate Change

NASA Astrophysics Data System (ADS)

Krishnan, S.; Huber, M.

2015-12-01

Lyme disease is one of the most frequently reported vector-borne diseases in the United States. In the Northeastern United States, vector transmission is maintained in a horizontal transmission cycle between the vector, the black-legged ticks, and the vertebrate reservoir hosts, which include white-tailed deer, rodents and other medium to large sized mammals. Predicting how vector populations change with future climate change is critical to understanding disease spread in the future, and for developing suitable regional adaptation strategies. For the United States, these predictions have mostly been made using regressions based on field and lab studies, or using spatial suitability studies. However, the relation between tick populations at various life-cycle stages and climate variables are complex, necessitating a mechanistic approach. In this study, we present a framework for driving a mechanistic tick population model with high-resolution regional climate modeling projections. The goal is to estimate changes in black-legged tick populations in New England for the 21st century. The tick population model used is based on the mechanistic approach of Ogden et al., (2005) developed for Canada. Dynamically downscaled climate projections at a 3-kms resolution using the Weather and Research Forecasting Model (WRF) are used to drive the tick population model.

A framework for predicting impacts on ecosystem services ...

EPA Pesticide Factsheets

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. The framework introduced here represents an ongoing initiative supported by the National Institute of Mathematical and Biological Synthesis (NIMBioS; http://www.nimbi

Progress toward bridging from atomistic to continuum modeling to predict nuclear waste glass dissolution.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zapol, Peter; Bourg, Ian; Criscenti, Louise Jacqueline

2011-10-01

This report summarizes research performed for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Subcontinuum and Upscaling Task. The work conducted focused on developing a roadmap to include molecular scale, mechanistic information in continuum-scale models of nuclear waste glass dissolution. This information is derived from molecular-scale modeling efforts that are validated through comparison with experimental data. In addition to developing a master plan to incorporate a subcontinuum mechanistic understanding of glass dissolution into continuum models, methods were developed to generate constitutive dissolution rate expressions from quantum calculations, force field models were selected to generate multicomponent glass structures and gel layers,more » classical molecular modeling was used to study diffusion through nanopores analogous to those in the interfacial gel layer, and a micro-continuum model (K{mu}C) was developed to study coupled diffusion and reaction at the glass-gel-solution interface.« less

A mechanistic investigation of the algae growth "Droop" model.

PubMed

Lemesle, V; Mailleret, L

2008-06-01

In this work a mechanistic explanation of the classical algae growth model built by M. R. Droop in the late sixties is proposed. We first recall the history of the construction of the "predictive" variable yield Droop model as well as the meaning of the introduced cell quota. We then introduce some theoretical hypotheses on the biological phenomena involved in nutrient storage by the algae that lead us to a "conceptual" model. Though more complex than Droop's one, our model remains accessible to a complete mathematical study: its confrontation to the Droop model shows both have the same asymptotic behavior. However, while Droop's cell quota comes from experimental bio-chemical measurements not related to intra-cellular biological phenomena, its analogous in our model directly follows our theoretical hypotheses. This new model should then be looked at as a re-interpretation of Droop's work from a theoretical biologist's point of view.

Investigating Mechanisms of Chronic Kidney Disease in Mouse Models

PubMed Central

Eddy, Allison A.; Okamura, Daryl M.; Yamaguchi, Ikuyo; López-Guisa, Jesús M.

2011-01-01

Animal models of chronic kidney disease (CKD) are important experimental tools that are used to investigate novel mechanistic pathways and to validate potential new therapeutic interventions prior to pre-clinical testing in humans. Over the past several years, mouse CKD models have been extensively used for these purposes. Despite significant limitations, the model of unilateral ureteral obstruction (UUO) has essentially become the high throughput in vivo model, as it recapitulates the fundamental pathogenetic mechanisms that typify all forms of CKD in a relatively short time span. In addition, several alternative mouse models are available that can be used to validate new mechanistic paradigms and/or novel therapies. Several models are reviewed – both genetic and experimentally induced – that provide investigators with an opportunity to include renal functional study end-points together with quantitative measures of fibrosis severity, something that is not possible with the UUO model. PMID:21695449

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757

Routes to DNA accessibility: alternative pathways for nucleosome unwinding.

PubMed

Schlingman, Daniel J; Mack, Andrew H; Kamenetska, Masha; Mochrie, Simon G J; Regan, Lynne

2014-07-15

The dynamic packaging of DNA into chromatin is a key determinant of eukaryotic gene regulation and epigenetic inheritance. Nucleosomes are the basic unit of chromatin, and therefore the accessible states of the nucleosome must be the starting point for mechanistic models regarding these essential processes. Although the existence of different unwound nucleosome states has been hypothesized, there have been few studies of these states. The consequences of multiple states are far reaching. These states will behave differently in all aspects, including their interactions with chromatin remodelers, histone variant exchange, and kinetic properties. Here, we demonstrate the existence of two distinct states of the unwound nucleosome, which are accessible at physiological forces and ionic strengths. Using optical tweezers, we measure the rates of unwinding and rewinding for these two states and show that the rewinding rates from each state are different. In addition, we show that the probability of unwinding into each state is dependent on the applied force and ionic strength. Our results demonstrate not only that multiple unwound states exist but that their accessibility can be differentially perturbed, suggesting possible roles for these states in gene regulation. For example, different histone variants or modifications may facilitate or suppress access to DNA by promoting unwinding into one state or the other. We anticipate that the two unwound states reported here will be the basis for future models of eukaryotic transcriptional control. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Methodological advances in predicting flow-induced dynamics of plants using mechanical-engineering theory.

PubMed

de Langre, Emmanuel

2012-03-15

The modeling of fluid-structure interactions, such as flow-induced vibrations, is a well-developed field of mechanical engineering. Many methods exist, and it seems natural to apply them to model the behavior of plants, and potentially other cantilever-like biological structures, under flow. Overcoming this disciplinary divide, and the application of such models to biological systems, will significantly advance our understanding of ecological patterns and processes and improve our predictive capabilities. Nonetheless, several methodological issues must first be addressed, which I describe here using two practical examples that have strong similarities: one from agricultural sciences and the other from nuclear engineering. Very similar issues arise in both: individual and collective behavior, small and large space and time scales, porous modeling, standard and extreme events, trade-off between the surface of exchange and individual or collective risk of damage, variability, hostile environments and, in some aspects, evolution. The conclusion is that, although similar issues do exist, which need to be exploited in some detail, there is a significant gap that requires new developments. It is obvious that living plants grow in and adapt to their environment, which certainly makes plant biomechanics fundamentally distinct from classical mechanical engineering. Moreover, the selection processes in biology and in human engineering are truly different, making the issue of safety different as well. A thorough understanding of these similarities and differences is needed to work efficiently in the application of a mechanistic approach to ecology.

LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

PubMed

Magnusson, Rasmus; Mariotti, Guido Pio; Köpsén, Mattias; Lövfors, William; Gawel, Danuta R; Jörnsten, Rebecka; Linde, Jörg; Nordling, Torbjörn E M; Nyman, Elin; Schulze, Sylvie; Nestor, Colm E; Zhang, Huan; Cedersund, Gunnar; Benson, Mikael; Tjärnberg, Andreas; Gustafsson, Mika

2017-06-01

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

Induction of a feed forward pro-apoptotic mechanistic loop by nitric oxide in a human breast cancer model.

PubMed

Sen, Suvajit; Kawahara, Brian; Fukuto, Jon; Chaudhuri, Gautam

2013-01-01

We have previously demonstrated that relatively high concentrations of NO [Nitric Oxide] as produced by activated macrophages induced apoptosis in the human breast cancer cell line, MDA-MB-468. More recently, we also demonstrated the importance of endogenous H2O2 in the regulation of growth in human breast cancer cells. In the present study we assessed the interplay between exogenously administered NO and the endogenously produced reactive oxygen species [ROS] in human breast cancer cells and evaluated the mechanism[s] in the induction of apoptosis. To this end we identified a novel mechanism by which NO down regulated endogenous hydrogen peroxide [H2O2] formation via the down-regulation of superoxide [O2 (.-)] and the activation of catalase. We further demonstrated the existence of a feed forward mechanistic loop involving protein phosphatase 2A [PP2A] and its downstream substrate FOXO1 in the induction of apoptosis and the synthesis of catalase. We utilized gene silencing of PP2A, FOXO1 and catalase to assess their relative importance and key roles in NO mediated apoptosis. This study provides the potential for a therapeutic approach in treating breast cancer by targeted delivery of NO where NO donors and activators of downstream players could initiate a self sustaining apoptotic cascade in breast cancer cells.

Induction of a Feed Forward Pro-Apoptotic Mechanistic Loop by Nitric Oxide in a Human Breast Cancer Model

PubMed Central

Sen, Suvajit; Kawahara, Brian; Fukuto, Jon; Chaudhuri, Gautam

2013-01-01

We have previously demonstrated that relatively high concentrations of NO [Nitric Oxide] as produced by activated macrophages induced apoptosis in the human breast cancer cell line, MDA-MB-468. More recently, we also demonstrated the importance of endogenous H2O2 in the regulation of growth in human breast cancer cells. In the present study we assessed the interplay between exogenously administered NO and the endogenously produced reactive oxygen species [ROS] in human breast cancer cells and evaluated the mechanism[s] in the induction of apoptosis. To this end we identified a novel mechanism by which NO down regulated endogenous hydrogen peroxide [H2O2] formation via the down-regulation of superoxide [O2 .−] and the activation of catalase. We further demonstrated the existence of a feed forward mechanistic loop involving protein phosphatase 2A [PP2A] and its downstream substrate FOXO1 in the induction of apoptosis and the synthesis of catalase. We utilized gene silencing of PP2A, FOXO1 and catalase to assess their relative importance and key roles in NO mediated apoptosis. This study provides the potential for a therapeutic approach in treating breast cancer by targeted delivery of NO where NO donors and activators of downstream players could initiate a self sustaining apoptotic cascade in breast cancer cells. PMID:23950968

A novel small molecule ameliorates ocular neovascularisation and synergises with anti-VEGF therapy.

PubMed

Sulaiman, Rania S; Merrigan, Stephanie; Quigley, Judith; Qi, Xiaoping; Lee, Bit; Boulton, Michael E; Kennedy, Breandán; Seo, Seung-Yong; Corson, Timothy W

2016-05-05

Ocular neovascularisation underlies blinding eye diseases such as retinopathy of prematurity, proliferative diabetic retinopathy, and wet age-related macular degeneration. These diseases cause irreversible vision loss, and provide a significant health and economic burden. Biologics targeting vascular endothelial growth factor (VEGF) are the major approach for treatment. However, up to 30% of patients are non-responsive to these drugs and they are associated with ocular and systemic side effects. Therefore, there is a need for small molecule ocular angiogenesis inhibitors to complement existing therapies. We examined the safety and therapeutic potential of SH-11037, a synthetic derivative of the antiangiogenic homoisoflavonoid cremastranone, in models of ocular neovascularisation. SH-11037 dose-dependently suppressed angiogenesis in the choroidal sprouting assay ex vivo and inhibited ocular developmental angiogenesis in zebrafish larvae. Additionally, intravitreal SH-11037 (1 μM) significantly reduced choroidal neovascularisation (CNV) lesion volume in the laser-induced CNV mouse model, comparable to an anti-VEGF antibody. Moreover, SH-11037 synergised with anti-VEGF treatments in vitro and in vivo. Up to 100 μM SH-11037 was not associated with signs of ocular toxicity and did not interfere with retinal function or pre-existing retinal vasculature. SH-11037 is thus a safe and effective treatment for murine ocular neovascularisation, worthy of further mechanistic and pharmacokinetic evaluation.

The Future of Pharmaceutical Manufacturing Sciences

PubMed Central

2015-01-01

The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial‐scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state‐of‐art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular‐based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot‐melt processing and printing‐based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:3612–3638, 2015 PMID:26280993

Withaferin-A—A Natural Anticancer Agent with Pleitropic Mechanisms of Action

PubMed Central

Lee, In-Chul; Choi, Bu Young

2016-01-01

Cancer, being the second leading cause of mortality, exists as a formidable health challenge. In spite of our enormous efforts, the emerging complexities in the molecular nature of disease progression limit the real success in finding an effective cancer cure. It is now conceivable that cancer is, in fact, a progressive illness, and the morbidity and mortality from cancer can be reduced by interfering with various oncogenic signaling pathways. A wide variety of structurally diverse classes of bioactive phytochemicals have been shown to exert anticancer effects in a large number of preclinical studies. Multiple lines of evidence suggest that withaferin-A can prevent the development of cancers of various histotypes. Accumulating data from different rodent models and cell culture experiments have revealed that withaferin-A suppresses experimentally induced carcinogenesis, largely by virtue of its potent anti-oxidative, anti-inflammatory, anti-proliferative and apoptosis-inducing properties. Moreover, withaferin-A sensitizes resistant cancer cells to existing chemotherapeutic agents. The purpose of this review is to highlight the mechanistic aspects underlying anticancer effects of withaferin-A. PMID:26959007

Withaferin-A--A Natural Anticancer Agent with Pleitropic Mechanisms of Action.

PubMed

Lee, In-Chul; Choi, Bu Young

2016-03-04

Cancer, being the second leading cause of mortality, exists as a formidable health challenge. In spite of our enormous efforts, the emerging complexities in the molecular nature of disease progression limit the real success in finding an effective cancer cure. It is now conceivable that cancer is, in fact, a progressive illness, and the morbidity and mortality from cancer can be reduced by interfering with various oncogenic signaling pathways. A wide variety of structurally diverse classes of bioactive phytochemicals have been shown to exert anticancer effects in a large number of preclinical studies. Multiple lines of evidence suggest that withaferin-A can prevent the development of cancers of various histotypes. Accumulating data from different rodent models and cell culture experiments have revealed that withaferin-A suppresses experimentally induced carcinogenesis, largely by virtue of its potent anti-oxidative, anti-inflammatory, anti-proliferative and apoptosis-inducing properties. Moreover, withaferin-A sensitizes resistant cancer cells to existing chemotherapeutic agents. The purpose of this review is to highlight the mechanistic aspects underlying anticancer effects of withaferin-A.

The Future of Pharmaceutical Manufacturing Sciences.

PubMed

Rantanen, Jukka; Khinast, Johannes

2015-11-01

The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association.

Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient.

PubMed

An, Gary; Faeder, James; Vodovotz, Yoram

2008-01-01

The pathophysiology of the burn patient manifests the full spectrum of the complexity of the inflammatory response. In the acute phase, inflammation may have negative effects via capillary leak, the propagation of inhalation injury, and development of multiple organ failure. Attempts to mediate these processes remain a central subject of burn care research. Conversely, inflammation is a necessary prologue and component in the later stage processes of wound healing. Despite the volume of information concerning the cellular and molecular processes involved in inflammation, there exists a significant gap between the knowledge of mechanistic pathophysiology and the development of effective clinical therapeutic regimens. Translational systems biology (TSB) is the application of dynamic mathematical modeling and certain engineering principles to biological systems to integrate mechanism with phenomenon and, importantly, to revise clinical practice. This study will review the existing applications of TSB in the areas of inflammation and wound healing, relate them to specific areas of interest to the burn community, and present an integrated framework that links TSB with traditional burn research.

Modeling the human Nav1.5 sodium channel: structural and mechanistic insights of ion permeation and drug blockade

PubMed Central

Ahmed, Marawan; Jalily Hasani, Horia; Ganesan, Aravindhan; Houghton, Michael; Barakat, Khaled

2017-01-01

Abnormalities in the human Nav1.5 (hNav1.5) voltage-gated sodium ion channel (VGSC) are associated with a wide range of cardiac problems and diseases in humans. Current structural models of hNav1.5 are still far from complete and, consequently, their ability to study atomistic interactions of this channel is very limited. Here, we report a comprehensive atomistic model of the hNav1.5 ion channel, constructed using homology modeling technique and refined through long molecular dynamics simulations (680 ns) in the lipid membrane bilayer. Our model was comprehensively validated by using reported mutagenesis data, comparisons with previous models, and binding to a panel of known hNav1.5 blockers. The relatively long classical MD simulation was sufficient to observe a natural sodium permeation event across the channel’s selectivity filters to reach the channel’s central cavity, together with the identification of a unique role of the lysine residue. Electrostatic potential calculations revealed the existence of two potential binding sites for the sodium ion at the outer selectivity filters. To obtain further mechanistic insight into the permeation event from the central cavity to the intracellular region of the channel, we further employed “state-of-the-art” steered molecular dynamics (SMD) simulations. Our SMD simulations revealed two different pathways through which a sodium ion can be expelled from the channel. Further, the SMD simulations identified the key residues that are likely to control these processes. Finally, we discuss the potential binding modes of a panel of known hNav1.5 blockers to our structural model of hNav1.5. We believe that the data presented here will enhance our understanding of the structure–property relationships of the hNav1.5 ion channel and the underlying molecular mechanisms in sodium ion permeation and drug interactions. The results presented here could be useful for designing safer drugs that do not block the hNav1.5 channel. PMID:28831242

Computational Modeling of Inflammation and Wound Healing

PubMed Central

Ziraldo, Cordelia; Mi, Qi; An, Gary; Vodovotz, Yoram

2013-01-01

Objective Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients. Approach To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing. Innovation We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models. Results A hybrid equation–agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment. Conclusions The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights. PMID:24527362

Application of response surface methodology and semi-mechanistic model to optimize fluoride removal using crushed concrete in a fixed-bed column.

PubMed

Gu, Bon-Wun; Lee, Chang-Gu; Park, Seong-Jik

2018-03-01

The aim of this study was to investigate the removal of fluoride from aqueous solutions by using crushed concrete fines as a filter medium under varying conditions of pH 3-7, flow rate of 0.3-0.7 mL/min, and filter depth of 10-20 cm. The performance of fixed-bed columns was evaluated on the basis of the removal ratio (Re), uptake capacity (qe), degree of sorbent used (DoSU), and sorbent usage rate (SUR) obtained from breakthrough curves (BTCs). Three widely used semi-mechanistic models, that is, Bohart-Adams, Thomas, and Yoon-Nelson models, were applied to simulate the BTCs and to derive the design parameters. The Box-Behnken design of response surface methodology (RSM) was used to elucidate the individual and interactive effects of the three operational parameters on the column performance and to optimize these parameters. The results demonstrated that pH is the most important factor in the performance of fluoride removal by a fixed-bed column. The flow rate had a significant negative influence on Re and DoSU, and the effect of filter depth was observed only in the regression model for DoSU. Statistical analysis indicated that the model attained from the RSM study is suitable for describing the semi-mechanistic model parameters.

Assessment of glycemic response to an oral glucokinase activator in a proof of concept study: application of a semi-mechanistic, integrated glucose-insulin-glucagon model.

PubMed

Schneck, Karen B; Zhang, Xin; Bauer, Robert; Karlsson, Mats O; Sinha, Vikram P

2013-02-01

A proof of concept study was conducted to investigate the safety and tolerability of a novel oral glucokinase activator, LY2599506, during multiple dose administration to healthy volunteers and subjects with Type 2 diabetes mellitus (T2DM). To analyze the study data, a previously established semi-mechanistic integrated glucose-insulin model was extended to include characterization of glucagon dynamics. The model captured endogenous glucose and insulin dynamics, including the amplifying effects of glucose on insulin production and of insulin on glucose elimination, as well as the inhibitory influence of glucose and insulin on hepatic glucose production. The hepatic glucose production in the model was increased by glucagon and glucagon production was inhibited by elevated glucose concentrations. The contribution of exogenous factors to glycemic response, such as ingestion of carbohydrates in meals, was also included in the model. The effect of LY2599506 on glucose homeostasis in subjects with T2DM was investigated by linking a one-compartment, pharmacokinetic model to the semi-mechanistic, integrated glucose-insulin-glucagon system. Drug effects were included on pancreatic insulin secretion and hepatic glucose production. The relationships between LY2599506, glucose, insulin, and glucagon concentrations were described quantitatively and consequently, the improved understanding of the drug-response system could be used to support further clinical study planning during drug development, such as dose selection.

Comparison of ligand migration and binding in heme proteins of the globin family

NASA Astrophysics Data System (ADS)

Karin, Nienhaus; Ulrich Nienhaus, G.

2015-12-01

The binding of small diatomic ligands such as carbon monoxide or dioxygen to heme proteins is among the simplest biological processes known. Still, it has taken many decades to understand the mechanistic aspects of this process in full detail. Here, we compare ligand binding in three heme proteins of the globin family, myoglobin, a dimeric hemoglobin, and neuroglobin. The combination of structural, spectroscopic, and kinetic experiments over many years by many laboratories has revealed common properties of globins and a clear mechanistic picture of ligand binding at the molecular level. In addition to the ligand binding site at the heme iron, a primary ligand docking site exists that ensures efficient ligand binding to and release from the heme iron. Additional, secondary docking sites can greatly facilitate ligand escape after its dissociation from the heme. Although there is only indirect evidence at present, a preformed histidine gate appears to exist that allows ligand entry to and exit from the active site. The importance of these features can be assessed by studies involving modified proteins (via site-directed mutagenesis) and comparison with heme proteins not belonging to the globin family.

Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions.

PubMed

Lombrozo, Tania

2010-12-01

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. Copyright © 2010 Elsevier Inc. All rights reserved.

Bedrock hillslopes to deltas: New insights into landscape mechanics

NASA Astrophysics Data System (ADS)

Lamb, M. P.

2012-12-01

A powerful record of environmental history on Earth and other planets is preserved in landscapes and sedimentary deposits formed by erosion, transport and deposition of sediment. To understand this record and predict future landscape change, we need mechanistic theories for transport processes and morphodynamics. Significant progress has been made to develop and test transport laws in some settings including soil-mantled landscapes and modest gradient alluvial channels where conventional hydraulic and sediment-transport theories apply. Fundamental opportunities exist, however, to explore the physical limits of existing theories and develop new theories at the frontier of our knowledge base. In this presentation, I will describe recent case studies from sediment-source areas to depositional sinks designed to test the limits of existing transport theories and identify transitions in dominant geomorphic processes by pushing to steeper slopes, greater discharges, and dynamic boundary conditions. First, field measurements, laboratory experiments and theory suggest that sediment transport on steep, rocky hillslopes is different than on soil mantled landscapes, and may be controlled by vegetation dams and sediment release following wildfire. Second, laboratory experiments show that grain stability is enhanced in steep mountain channels compared to moderate gradient channels due to the coincident change in the ratio of flow depth to grain size, until the threshold for mass failure is surpassed. Third, field observations and theory indicate that canyons can be cut by extreme floods at rates that exceed typical river-incision rates by many orders of magnitude due to the transition from abrasion to plucking and toppling of jointed rock, with implications for megafloods on Mars. Finally, numerical modeling, laboratory experiments, and field work show that coastal rivers are coupled to their offshore plumes, and that this coupling may determine the size of deltas. These case studies illustrate that relevant processes, from turbulence to tectonics, act over a wide range of spatial and temporal scales. A major future opportunity I see is to integrate topographic and geochronologic constraints on landscape kinematics with field and laboratory experiments on process dynamics to build mechanistic transport theories applicable across human and geomorphic timescales.

SIMULATION MODELING OF GASTROINTESTINAL ABSORPTION

EPA Science Inventory

Mathematical dosimetry models incorporate mechanistic determinants of chemical disposition in a living organism to describe relationships between exposure concentration and the internal dose needed for PBPK models and human health risk assessment. Because they rely on determini...

Regulatory Technology Development Plan - Sodium Fast Reactor. Mechanistic Source Term - Metal Fuel Radionuclide Release

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grabaskas, David; Bucknor, Matthew; Jerden, James

2016-02-01

The development of an accurate and defensible mechanistic source term will be vital for the future licensing efforts of metal fuel, pool-type sodium fast reactors. To assist in the creation of a comprehensive mechanistic source term, the current effort sought to estimate the release fraction of radionuclides from metal fuel pins to the primary sodium coolant during fuel pin failures at a variety of temperature conditions. These release estimates were based on the findings of an extensive literature search, which reviewed past experimentation and reactor fuel damage accidents. Data sources for each radionuclide of interest were reviewed to establish releasemore » fractions, along with possible release dependencies, and the corresponding uncertainty levels. Although the current knowledge base is substantial, and radionuclide release fractions were established for the elements deemed important for the determination of offsite consequences following a reactor accident, gaps were found pertaining to several radionuclides. First, there is uncertainty regarding the transport behavior of several radionuclides (iodine, barium, strontium, tellurium, and europium) during metal fuel irradiation to high burnup levels. The migration of these radionuclides within the fuel matrix and bond sodium region can greatly affect their release during pin failure incidents. Post-irradiation examination of existing high burnup metal fuel can likely resolve this knowledge gap. Second, data regarding the radionuclide release from molten high burnup metal fuel in sodium is sparse, which makes the assessment of radionuclide release from fuel melting accidents at high fuel burnup levels difficult. This gap could be addressed through fuel melting experimentation with samples from the existing high burnup metal fuel inventory.« less

Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models

PubMed Central

Becher, Matthias A; Osborne, Juliet L; Thorbek, Pernille; Kennedy, Peter J; Grimm, Volker

2013-01-01

The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee – varroa mite – virus interactions. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions. PMID:24223431

Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models.

PubMed

Becher, Matthias A; Osborne, Juliet L; Thorbek, Pernille; Kennedy, Peter J; Grimm, Volker

2013-08-01

The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality.However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management.We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions.We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. Synthesis and applications . We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.

In-air and pressurized water reactor environment fatigue experiments of 316 stainless steel to study the effect of environment on cyclic hardening

NASA Astrophysics Data System (ADS)

Mohanty, Subhasish; Soppet, William K.; Majumdar, Saurindranath; Natesan, Krishnamurti

2016-05-01

Argonne National Laboratory (ANL), under the sponsorship of Department of Energy's Light Water Reactor Sustainability (LWRS) program, is trying to develop a mechanistic approach for more accurate life estimation of LWR components. In this context, ANL has conducted many fatigue experiments under different test and environment conditions on type 316 stainless steel (316 SS) material which is widely used in the US reactors. Contrary to the conventional S ∼ N curve based empirical fatigue life estimation approach, the aim of the present DOE sponsored work is to develop an understanding of the material ageing issues more mechanistically (e.g. time dependent hardening and softening) under different test and environmental conditions. Better mechanistic understanding will help develop computer-based advanced modeling tools to better extrapolate stress-strain evolution of reactor components under multi-axial stress states and hence help predict their fatigue life more accurately. Mechanics-based modeling of fatigue such as by using finite element (FE) tools requires the time/cycle dependent material hardening properties. Presently such time-dependent material hardening properties are hardly available in fatigue modeling literature even under in-air conditions. Getting those material properties under PWR environment, are even harder. Through this work we made preliminary attempt to generate time/cycle dependent stress-strain data both under in-air and PWR water conditions for further study such as for possible development of material models and constitutive relations for FE model implementation. Although, there are open-ended possibility to further improve the discussed test methods and related material estimation techniques we anticipate that the data presented in this paper will help the metal fatigue research community particularly, the researchers who are dealing with mechanistic modeling of metal fatigue such as using FE tools. In this paper the fatigue experiments under different test and environment conditions and related stress-strain results for 316 SS are discussed.

Mechanistic species distribution modeling reveals a niche shift during invasion.

PubMed

Chapman, Daniel S; Scalone, Romain; Štefanić, Edita; Bullock, James M

2017-06-01

Niche shifts of nonnative plants can occur when they colonize novel climatic conditions. However, the mechanistic basis for niche shifts during invasion is poorly understood and has rarely been captured within species distribution models. We quantified the consequence of between-population variation in phenology for invasion of common ragweed (Ambrosia artemisiifolia L.) across Europe. Ragweed is of serious concern because of its harmful effects as a crop weed and because of its impact on public health as a major aeroallergen. We developed a forward mechanistic species distribution model based on responses of ragweed development rates to temperature and photoperiod. The model was parameterized and validated from the literature and by reanalyzing data from a reciprocal common garden experiment in which native and invasive populations were grown within and beyond the current invaded range. It could therefore accommodate between-population variation in the physiological requirements for flowering, and predict the potentially invaded ranges of individual populations. Northern-origin populations that were established outside the generally accepted climate envelope of the species had lower thermal requirements for bud development, suggesting local adaptation of phenology had occurred during the invasion. The model predicts that this will extend the potentially invaded range northward and increase the average suitability across Europe by 90% in the current climate and 20% in the future climate. Therefore, trait variation observed at the population scale can trigger a climatic niche shift at the biogeographic scale. For ragweed, earlier flowering phenology in established northern populations could allow the species to spread beyond its current invasive range, substantially increasing its risk to agriculture and public health. Mechanistic species distribution models offer the possibility to represent niche shifts by varying the traits and niche responses of individual populations. Ignoring such effects could substantially underestimate the extent and impact of invasions. © 2017 by the Ecological Society of America.

STATISTICAL METHODOLOGY FOR ESTIMATING PARAMETERS IN PBPK/PD MODELS

EPA Science Inventory

PBPK/PD models are large dynamic models that predict tissue concentration and biological effects of a toxicant before PBPK/PD models can be used in risk assessments in the arena of toxicological hypothesis testing, models allow the consequences of alternative mechanistic hypothes...

A Global Study of GPP focusing on Light Use Efficiency in a Random Forest Regression Model

NASA Astrophysics Data System (ADS)

Fang, W.; Wei, S.; Yi, C.; Hendrey, G. R.

2016-12-01

Light use efficiency (LUE) is at the core of mechanistic modeling of global gross primary production (GPP). However, most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this paper, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated key environmental and biological variables into the tower-based LUE estimates, at 0.5o x 0.5o grid-cell resolution, using a random forest regression (RFR) approach. We then developed an RFR-LUE-GPP model using the grid-cell LUE data, and compared it to a tower-LUE-GPP model by the conventional way of treating LUE as a series of biome-specific constants. In order to calibrate the LUE models, we developed a data-driven RFR-GPP model using a random forest regression method. Our results showed that LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.21 gC m-2 MJ-1 APAR, which led to an estimated global GPP of 102.9 Gt C /year from 2000 to 2005. The tower-LUE-GPP model tended to overestimate forest GPP in tropical and boreal regions. Large uncertainties exist in GPP estimates over sparsely vegetated areas covered by savannas and woody savannas around the middle to low latitudes (i.g. 20oS to 40oS and 5oN to 15oN) due to lack of available data. Model results were improved by incorporating Köppen climate types to represent climate /meteorological information in machine learning modeling. This shed new light on the recognized issues of climate dependence of spring onset of photosynthesis and the challenges in modeling the biome GPP of evergreen broad leaf forests (EBF) accurately. The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echoed the necessity of modeling GPP separately by latitudes. This work provided a global distribution of LUE estimate, and developed a comprehensive algorithm modeling global terrestrial carbon with high spatial and temporal resolutions.

Hierarchical Modeling of Activation Mechanisms in the ABL and EGFR Kinase Domains: Thermodynamic and Mechanistic Catalysts of Kinase Activation by Cancer Mutations

PubMed Central

Dixit, Anshuman; Verkhivker, Gennady M.

2009-01-01

Structural and functional studies of the ABL and EGFR kinase domains have recently suggested a common mechanism of activation by cancer-causing mutations. However, dynamics and mechanistic aspects of kinase activation by cancer mutations that stimulate conformational transitions and thermodynamic stabilization of the constitutively active kinase form remain elusive. We present a large-scale computational investigation of activation mechanisms in the ABL and EGFR kinase domains by a panel of clinically important cancer mutants ABL-T315I, ABL-L387M, EGFR-T790M, and EGFR-L858R. We have also simulated the activating effect of the gatekeeper mutation on conformational dynamics and allosteric interactions in functional states of the ABL-SH2-SH3 regulatory complexes. A comprehensive analysis was conducted using a hierarchy of computational approaches that included homology modeling, molecular dynamics simulations, protein stability analysis, targeted molecular dynamics, and molecular docking. Collectively, the results of this study have revealed thermodynamic and mechanistic catalysts of kinase activation by major cancer-causing mutations in the ABL and EGFR kinase domains. By using multiple crystallographic states of ABL and EGFR, computer simulations have allowed one to map dynamics of conformational fluctuations and transitions in the normal (wild-type) and oncogenic kinase forms. A proposed multi-stage mechanistic model of activation involves a series of cooperative transitions between different conformational states, including assembly of the hydrophobic spine, the formation of the Src-like intermediate structure, and a cooperative breakage and formation of characteristic salt bridges, which signify transition to the active kinase form. We suggest that molecular mechanisms of activation by cancer mutations could mimic the activation process of the normal kinase, yet exploiting conserved structural catalysts to accelerate a conformational transition and the enhanced stabilization of the active kinase form. The results of this study reconcile current experimental data with insights from theoretical approaches, pointing to general mechanistic aspects of activating transitions in protein kinases. PMID:19714203

Annual temperature variation as a time machine to understand the effects of long-term climate change on a poleward range shift.

PubMed

Crickenberger, Sam; Wethey, David S

2018-05-10

Range shifts due to annual variation in temperature are more tractable than range shifts linked to decadal to century long temperature changes due to climate change, providing natural experiments to determine the mechanisms responsible for driving long-term distributional shifts. In this study we couple physiologically grounded mechanistic models with biogeographic surveys in 2 years with high levels of annual temperature variation to disentangle the drivers of a historical range shift driven by climate change. The distribution of the barnacle Semibalanus balanoides has shifted 350 km poleward in the past half century along the east coast of the United States. Recruits were present throughout the historical range following the 2015 reproductive season, when temperatures were similar to those in the past century, and absent following the 2016 reproductive season when temperatures were warmer than they have been since 1870, the earliest date for temperature records. Our dispersal dependent mechanistic models of reproductive success were highly accurate and predicted patterns of reproduction success documented in field surveys throughout the historical range in 2015 and 2016. Our mechanistic models of reproductive success not only predicted recruitment dynamics near the range edge but also predicted interior range fragmentation in a number of years between 1870 and 2016. All recruits monitored within the historical range following the 2015 colonization died before 2016 suggesting juvenile survival was likely the primary driver of the historical range retraction. However, if 2016 is indicative of future temperatures mechanisms of range limitation will shift and reproductive failure will lead to further range retraction in the future. Mechanistic models are necessary for accurately predicting the effects of climate change on ranges of species. © 2018 John Wiley & Sons Ltd.

Whole-body iron transport and metabolism: Mechanistic, multi-scale model to improve treatment of anemia in chronic kidney disease

PubMed Central

Sarkar, Joydeep

2018-01-01

Iron plays vital roles in the human body including enzymatic processes, oxygen-transport via hemoglobin and immune response. Iron metabolism is characterized by ~95% recycling and minor replenishment through diet. Anemia of chronic kidney disease (CKD) is characterized by a lack of synthesis of erythropoietin leading to reduced red blood cell (RBC) formation and aberrant iron recycling. Treatment of CKD anemia aims to normalize RBC count and serum hemoglobin. Clinically, the various fluxes of iron transport and accumulation are not measured so that changes during disease (e.g., CKD) and treatment are unknown. Unwanted iron accumulation in patients is known to lead to adverse effects. Current whole-body models lack the mechanistic details of iron transport related to RBC maturation, transferrin (Tf and TfR) dynamics and assume passive iron efflux from macrophages. Hence, they are not predictive of whole-body iron dynamics and cannot be used to design individualized patient treatment. For prediction, we developed a mechanistic, multi-scale computational model of whole-body iron metabolism incorporating four compartments containing major pools of iron and RBC generation process. The model accounts for multiple forms of iron in vivo, mechanisms involved in iron uptake and release and their regulation. Furthermore, the model is interfaced with drug pharmacokinetics to allow simulation of treatment dynamics. We calibrated our model with experimental and clinical data from peer-reviewed literature to reliably simulate CKD anemia and the effects of current treatment involving combination of epoietin-alpha and iron dextran. This in silico whole-body model of iron metabolism predicts that a year of treatment can potentially lead to 90% downregulation of ferroportin (FPN) levels, 15-fold increase in iron stores with only a 20% increase in iron flux from the reticulo-endothelial system (RES). Model simulations quantified unmeasured iron fluxes, previously unknown effects of treatment on FPN-level and iron stores in the RES. This mechanistic whole-body model can be the basis for future studies that incorporate iron metabolism together with related clinical experiments. Such an approach could pave the way for development of effective personalized treatment of CKD anemia. PMID:29659573

Root structural and functional dynamics in terrestrial biosphere models--evaluation and recommendations.

PubMed

Warren, Jeffrey M; Hanson, Paul J; Iversen, Colleen M; Kumar, Jitendra; Walker, Anthony P; Wullschleger, Stan D

2015-01-01

There is wide breadth of root function within ecosystems that should be considered when modeling the terrestrial biosphere. Root structure and function are closely associated with control of plant water and nutrient uptake from the soil, plant carbon (C) assimilation, partitioning and release to the soils, and control of biogeochemical cycles through interactions within the rhizosphere. Root function is extremely dynamic and dependent on internal plant signals, root traits and morphology, and the physical, chemical and biotic soil environment. While plant roots have significant structural and functional plasticity to changing environmental conditions, their dynamics are noticeably absent from the land component of process-based Earth system models used to simulate global biogeochemical cycling. Their dynamic representation in large-scale models should improve model veracity. Here, we describe current root inclusion in models across scales, ranging from mechanistic processes of single roots to parameterized root processes operating at the landscape scale. With this foundation we discuss how existing and future root functional knowledge, new data compilation efforts, and novel modeling platforms can be leveraged to enhance root functionality in large-scale terrestrial biosphere models by improving parameterization within models, and introducing new components such as dynamic root distribution and root functional traits linked to resource extraction. No claim to original US Government works. New Phytologist © 2014 New Phytologist Trust.

Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.

PubMed

Li, Michael; Dushoff, Jonathan; Bolker, Benjamin M

2018-07-01

Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

Modeling receptor kinetics in the analysis of survival data for organophosphorus pesticides.

PubMed

Jager, Tjalling; Kooijman, Sebastiaan A L M

2005-11-01

Acute ecotoxicological tests usually focus on survival at a standardized exposure time. However, LC50's decrease in time in a manner that depends both on the chemical and on the organism. DEBtox is an existing approach to analyze toxicity data in time, based on hazard modeling (the internal concentration increases the probability to die). However, certain chemicals elicit their response through (irreversible) interaction with a specific receptor, such as inhibition of acetylcholinesterase (AChE). Effects therefore do not solely depend on the actual internal concentration, but also on its (recent) past. In this paper, the DEBtox method is extended with a simple mechanistic model to deal with receptor interactions. We analyzed data from the literature for organophosphorus pesticides in guppies, fathead minnows, and springtails. Overall, the observed survival patterns do not clearly differ from those of chemicals with a less-specific mode of action. However, using the receptor model, resulting parameter estimates are easier to interpret in terms of underlying mechanisms and reveal similarities between the various pesticides. We observed thatthe no-effect concentration estimated from the receptor model is basically identical to the value from standard DEBtox, illustrating the robustness of this summary statistic.

Experimental estimation of migration and transfer of organic substances from consumer articles to cotton wipes: Evaluation of underlying mechanisms.

PubMed

Clausen, Per Axel; Spaan, Suzanne; Brouwer, Derk H; Marquart, Hans; le Feber, Maaike; Engel, Roel; Geerts, Lieve; Jensen, Keld Alstrup; Kofoed-Sørensen, Vivi; Hansen, Brian; De Brouwere, Katleen

2016-01-01

The aim of this work was to identify the key mechanisms governing transport of organic chemical substances from consumer articles to cotton wipes. The results were used to establish a mechanistic model to improve assessment of dermal contact exposure. Four types of PVC flooring, 10 types of textiles and one type of inkjet printed paper were used to establish the mechanisms and model. Kinetic extraction studies in methanol demonstrated existence of matrix diffusion and indicated the presence of a substance surface layer on some articles. Consequently, the proposed substance transfer model considers mechanical transport from a surface film and matrix diffusion in an article with a known initial total substance concentration. The estimated chemical substance transfer values to cotton wipes were comparable to the literature data (relative transfer ∼ 2%), whereas relative transfer efficiencies from spiked substrates were high (∼ 50%). For consumer articles, high correlation (r(2)=0.92) was observed between predicted and measured transfer efficiencies, but concentrations were overpredicted by a factor of 10. Adjusting the relative transfer from about 50% used in the model to about 2.5% removed overprediction. Further studies are required to confirm the model for generic use.

A Mechanistic Model of the Actin Cycle

PubMed Central

Bindschadler, M.; Osborn, E. A.; Dewey, C. F.; McGrath, J. L.

2004-01-01

We have derived a broad, deterministic model of the steady-state actin cycle that includes its major regulatory mechanisms. Ours is the first model to solve the complete nucleotide profile within filaments, a feature that determines the dynamics and geometry of actin networks at the leading edges of motile cells, and one that has challenged investigators developing models to interpret steady-state experiments. We arrived at the nucleotide profile through analytic and numerical approaches that completely agree. Our model reproduces behaviors seen in numerous experiments with purified proteins, but allows a detailed inspection of the concentrations and fluxes that might exist in these experiments. These inspections provide new insight into the mechanisms that determine the rate of actin filament treadmilling. Specifically, we find that mechanisms for enhancing Pi release from the ADP·Pi intermediate on filaments, for increasing the off rate of ADP-bound subunits at pointed ends, and the multiple, simultaneous functions of profilin, make unique and essential contributions to increased treadmilling. In combination, these mechanisms have a theoretical capacity to increase treadmilling to levels limited only by the amount of available actin. This limitation arises because as the cycle becomes more dynamic, it tends toward the unpolymerized state. PMID:15111391

Plug-and-play inference for disease dynamics: measles in large and small populations as a case study

PubMed Central

He, Daihai; Ionides, Edward L.; King, Aaron A.

2010-01-01

Statistical inference for mechanistic models of partially observed dynamic systems is an active area of research. Most existing inference methods place substantial restrictions upon the form of models that can be fitted and hence upon the nature of the scientific hypotheses that can be entertained and the data that can be used to evaluate them. In contrast, the so-called plug-and-play methods require only simulations from a model and are thus free of such restrictions. We show the utility of the plug-and-play approach in the context of an investigation of measles transmission dynamics. Our novel methodology enables us to ask and answer questions that previous analyses have been unable to address. Specifically, we demonstrate that plug-and-play methods permit the development of a modelling and inference framework applicable to data from both large and small populations. We thereby obtain novel insights into the nature of heterogeneity in mixing and comment on the importance of including extra-demographic stochasticity as a means of dealing with environmental stochasticity and model misspecification. Our approach is readily applicable to many other epidemiological and ecological systems. PMID:19535416

Triaryl Benzimidazoles as a New Class of Antibacterial Agents against Resistant Pathogenic Microorganisms.

PubMed

Picconi, Pietro; Hind, Charlotte; Jamshidi, Shirin; Nahar, Kazi; Clifford, Melanie; Wand, Matthew E; Sutton, J Mark; Rahman, Khondaker Miraz

2017-07-27

A new class of nontoxic triaryl benzimidazole compounds, derived from existing classes of DNA minor groove binders, were designed, synthesized, and evaluated for their antibacterial activity against multidrug resistant (MDR) Gram-positive and Gram-negative species. Molecular modeling experiments suggest that the newly synthesized class cannot be accommodated within the minor groove of DNA due to a change in the shape of the molecules. Compounds 8, 13, and 14 were found to be the most active of the series, with MICs in the range of 0.5-4 μg/mL against the MDR Staphylococci and Enterococci species. Compound 13 showed moderate activity against the MDR Gram-negative strains, with MICs in the range of 16-32 μg/mL. Active compounds showed a bactericidal mode of action, and a mechanistic study suggested the inhibition of bacterial gyrase as the mechanism of action (MOA) of this chemical class. The MOA was further supported by the molecular modeling study.

Free-Energy Landscape of the Dissolution of Gibbsite at High pH

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Zhizhang; Kerisit, Sebastien N.; Stack, Andrew G.

The individual elementary reactions involved in the dissolution of a solid into solution remain mostly speculative due to a lack of suitable, direct experimental probes. In this regard, we have applied atomistic simulations to map the free energy landscape of the dissolution of gibbsite from a step edge, as a model of metal hydroxide dissolution. The overall reaction combines kink site formation and kink site propagation. Two individual reactions were found to be rate-limiting for kink site formation, that is, the displacement of Al from a step site to a ledge adatom site and its detachment from ledge/terrace adatom sitesmore » into the solution. As a result, a pool of mobile and labile Al adsorbed species, or adatoms, exists before the release of Al into solution. Because of the quasi-hexagonal symmetry of gibbsite, kink site propagation can occur in multiple directions. Overall, the simulation results will enable the development of microscopic mechanistic models of metal oxide dissolution.« less

Unravelling the physics of size-dependent dislocation-mediated plasticity

NASA Astrophysics Data System (ADS)

El-Awady, Jaafar A.

2015-01-01

Size-affected dislocation-mediated plasticity is important in a wide range of materials and technologies. Here we develop a generalized size-dependent dislocation-based model that predicts strength as a function of crystal/grain size and the dislocation density. Three-dimensional (3D) discrete dislocation dynamics (DDD) simulations reveal the existence of a well-defined relationship between strength and dislocation microstructure at all length scales for both single crystals and polycrystalline materials. The results predict a transition from dislocation-source strengthening to forest-dominated strengthening at a size-dependent critical dislocation density. It is also shown that the Hall-Petch relationship can be physically interpreted by coupling with an appropriate kinetic equation of the evolution of the dislocation density in polycrystals. The model is shown to be in remarkable agreement with experiments. This work presents a micro-mechanistic framework to predict and interpret strength size-scale effects, and provides an avenue towards performing multiscale simulations without ad hoc assumptions.

The role of IDH1 mutated tumour cells in secondary glioblastomas: an evolutionary game theoretical view

NASA Astrophysics Data System (ADS)

Basanta, David; Scott, Jacob G.; Rockne, Russ; Swanson, Kristin R.; Anderson, Alexander R. A.

2011-02-01

Recent advances in clinical medicine have elucidated two significantly different subtypes of glioblastoma which carry very different prognoses, both defined by mutations in isocitrate dehydrogenase-1 (IDH-1). The mechanistic consequences of this mutation have not yet been fully clarified, with conflicting opinions existing in the literature; however, IDH-1 mutation may be used as a surrogate marker to distinguish between primary and secondary glioblastoma multiforme (sGBM) from malignant progression of a lower grade glioma. We develop a mathematical model of IDH-1 mutated secondary glioblastoma using evolutionary game theory to investigate the interactions between four different phenotypic populations within the tumor: autonomous growth, invasive, glycolytic, and the hybrid invasive/glycolytic cells. Our model recapitulates glioblastoma behavior well and is able to reproduce two recent experimental findings, as well as make novel predictions concerning the rate of invasive growth as a function of vascularity, and fluctuations in the proportions of phenotypic populations that a glioblastoma will experience under different microenvironmental constraints.

The Experimental Evidence in Support of Glycosylation Mechanisms at the SN1-SN2 Interface.

PubMed

Adero, Philip Ouma; Amarasekara, Harsha; Wen, Peng; Bohé, Luis; Crich, David

2018-05-30

A critical review of the state-of-the-art evidence in support of the mechanisms of glycosylation reactions is provided. Factors affecting the stability of putative oxocarbenium ions as intermediates at the S N 1 end of the mechanistic continuum are first surveyed before the evidence, spectroscopic and indirect, for the existence of such species on the time scale of glycosylation reactions is presented. Current models for diastereoselectivity in nucleophilic attack on oxocarbenium ions are then described. Evidence in support of the intermediacy of activated covalent glycosyl donors is reviewed, before the influences of the structure of the nucleophile, of the solvent, of temperature, and of donor-acceptor hydrogen bonding on the mechanism of glycosylation reactions are surveyed. Studies on the kinetics of glycosylation reactions and the use of kinetic isotope effects for the determination of transition-state structure are presented, before computational models are finally surveyed. The review concludes with a critical appraisal of the state of the art.

Mechanistic model to predict colostrum intake based on deuterium oxide dilution technique data and impact of gestation and prefarrowing diets on piglet intake and sow yield of colostrum.

PubMed

Theil, P K; Flummer, C; Hurley, W L; Kristensen, N B; Labouriau, R L; Sørensen, M T

2014-12-01

The aims of the present study were to quantify colostrum intake (CI) of piglets using the D2O dilution technique, to develop a mechanistic model to predict CI, to compare these data with CI predicted by a previous empirical predictive model developed for bottle-fed piglets, and to study how composition of diets fed to gestating sows affected piglet CI, sow colostrum yield (CY), and colostrum composition. In total, 240 piglets from 40 litters were enriched with D2O. The CI measured by D2O from birth until 24 h after the birth of first-born piglet was on average 443 g (SD 151). Based on measured CI, a mechanistic model to predict CI was developed using piglet characteristics (24-h weight gain [WG; g], BW at birth [BWB; kg], and duration of CI [D; min]: CI, g=-106+2.26 WG+200 BWB+0.111 D-1,414 WG/D+0.0182 WG/BWB (R2=0.944). This model was used to predict the CI for all colostrum suckling piglets within the 40 litters (n=500, mean=437 g, SD=153 g) and was compared with the CI predicted by a previous empirical predictive model (mean=305 g, SD=140 g). The previous empirical model underestimated the CI by 30% compared with that obtained by the new mechanistic model. The sows were fed 1 of 4 gestation diets (n=10 per diet) based on different fiber sources (low fiber [17%] or potato pulp, pectin residue, or sugarbeet pulp [32 to 40%]) from mating until d 108 of gestation. From d 108 of gestation until parturition, sows were fed 1 of 5 prefarrowing diets (n=8 per diet) varying in supplemented fat (3% animal fat, 8% coconut oil, 8% sunflower oil, 8% fish oil, or 4% fish oil+4% octanoic acid). Sows fed diets with pectin residue or sugarbeet pulp during gestation produced colostrum with lower protein, fat, DM, and energy concentrations and higher lactose concentrations, and their piglets had greater CI as compared with sows fed potato pulp or the low-fiber diet (P<0.05), and sows fed pectin residue had a greater CY than potato pulp-fed sows (P<0.05). Prefarrowing diets affected neither CI nor CY, but the prefarrowing diet with coconut oil decreased lactose and increased DM concentrations of colostrum compared with other prefarrowing diets (P<0.05). In conclusion, the new mechanistic predictive model for CI suggests that the previous empirical predictive model underestimates CI of sow-reared piglets by 30%. It was also concluded that nutrition of sows during gestation affected CY and colostrum composition.

Multiscale Systems Analysis of Root Growth and Development: Modeling Beyond the Network and Cellular Scales

PubMed Central

Band, Leah R.; Fozard, John A.; Godin, Christophe; Jensen, Oliver E.; Pridmore, Tony; Bennett, Malcolm J.; King, John R.

2012-01-01

Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties. PMID:23110897

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Causality, mediation and time: a dynamic viewpoint

PubMed Central

Aalen, Odd O; Røysland, Kjetil; Gran, Jon Michael; Ledergerber, Bruno

2012-01-01

Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented. PMID:23193356

Murine Models of Heart Failure with Preserved Ejection Fraction: a “Fishing Expedition”

PubMed Central

Valero-Muñoz, Maria; Backman, Warren; Sam, Flora

2017-01-01

Summary Heart failure with preserved ejection fraction (HFpEF) is characterized by signs and symptoms of HF in the presence of a normal left ventricular (LV) ejection fraction (EF). Despite accounting for up to 50% of all clinical presentations of HF, the mechanisms implicated in HFpEF are poorly understood, thus precluding effective therapy. The pathophysiological heterogeneity in the HFpEF phenotype also contributes to this disease and likely to the absence of evidence-based therapies. Limited access to human samples and imperfect animal models that completely recapitulate the human HFpEF phenotype have impeded our understanding of the mechanistic underpinnings that exist in this disease. Aging and comorbidities such as atrial fibrillation, hypertension, diabetes and obesity, pulmonary hypertension and renal dysfunction are highly associated with HFpEF. Yet, the relationship and contribution between them remains ill-defined. This review discusses some of the distinctive clinical features of HFpEF in association with these comorbidities and highlights the advantages and disadvantage of commonly used murine models, used to study the HFpEF phenotype. PMID:29333506

Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis

PubMed Central

Gherib, Rami; Dokainish, Hisham M.; Gauld, James W.

2014-01-01

Elucidating the origin of enzymatic catalysis stands as one the great challenges of contemporary biochemistry and biophysics. The recent emergence of computational enzymology has enhanced our atomistic-level description of biocatalysis as well the kinetic and thermodynamic properties of their mechanisms. There exists a diversity of computational methods allowing the investigation of specific enzymatic properties. Small or large density functional theory models allow the comparison of a plethora of mechanistic reactive species and divergent catalytic pathways. Molecular docking can model different substrate conformations embedded within enzyme active sites and determine those with optimal binding affinities. Molecular dynamics simulations provide insights into the dynamics and roles of active site components as well as the interactions between substrate and enzymes. Hybrid quantum mechanical/molecular mechanical (QM/MM) can model reactions in active sites while considering steric and electrostatic contributions provided by the surrounding environment. Using previous studies done within our group, on OvoA, EgtB, ThrRS, LuxS and MsrA enzymatic systems, we will review how these methods can be used either independently or cooperatively to get insights into enzymatic catalysis. PMID:24384841

Kinetics of the maintenance of the epidermis

NASA Astrophysics Data System (ADS)

Zhdanov, Vladimir P.; Cho, Nam-Joon

2013-08-01

The epidermis is the outermost layer of skin. It is comprised of keratin-containing cells called keratinocytes. Functionally, the epidermis serves as a physical barrier that can prevent infection and regulate body hydration. Maintenance and repair of the epidermis are important for human health. Mechanistically, these processes occur primarily via proliferation and differentiation of stem cells located in the basal monolayer. These processes are believed to depend on cell-cell communication and spatial constraints but existing kinetic models focus mainly on proliferation and differentiation. To address this issue, we present a mean-field kinetic model that takes these additional factors into account and describes the epidermis at a biosystem level. The corresponding equations operate with the populations of stem cells and differentiated cells in the basal layer. The keratinocytes located above the basal layer are treated at a more coarse-grained level by considering the thickness of the epidermis. The model clarifies the likely role of various negative feedbacks that may control the epidermis and, accordingly, provides insight into the cellular mechanisms underlying complex biological phenomena such as wound healing.

Modeling Coherent Structures in Canopy Flows

NASA Astrophysics Data System (ADS)

Luhar, Mitul

2017-11-01

It is well known that flows over vegetation canopies are characterized by the presence of energetic coherent structures. Since the mean profile over dense canopies exhibits an inflection point, the emergence of such structures is often attributed to a Kelvin-Helmholtz instability. However, though stability analyses provide useful mechanistic insights into canopy flows, they are limited in their ability to generate predictions for spectra and coherent structure. The present effort seeks to address this limitation by extending the resolvent formulation (McKeon and Sharma, 2010, J. Fluid Mech.) to canopy flows. Under the resolvent formulation, the turbulent velocity field is expressed as a superposition of propagating modes, identified via a gain-based (singular value) decomposition of the Navier-Stokes equations. A key advantage of this approach is that it reconciles multiple mechanisms that lead to high amplification in turbulent flows, including modal instability, transient growth, and critical-layer phenomena. Further, individual high-gain modes can be combined to generate more complete models for coherent structure and velocity spectra. Preliminary resolvent-based model predictions for canopy flows agree well with existing experiments and simulations.

Prospective Design of Anti‐Transferrin Receptor Bispecific Antibodies for Optimal Delivery into the Human Brain

PubMed Central

Kanodia, JS; Gadkar, K; Bumbaca, D; Zhang, Y; Tong, RK; Luk, W; Hoyte, K; Lu, Y; Wildsmith, KR; Couch, JA; Watts, RJ; Dennis, MS; Ernst, JA; Scearce‐Levie, K; Atwal, JK; Joseph, S

2016-01-01

Anti‐transferrin receptor (TfR)‐based bispecific antibodies have shown promise for boosting antibody uptake in the brain. Nevertheless, there are limited data on the molecular properties, including affinity required for successful development of TfR‐based therapeutics. A complex nonmonotonic relationship exists between affinity of the anti‐TfR arm and brain uptake at therapeutically relevant doses. However, the quantitative nature of this relationship and its translatability to humans is heretofore unexplored. Therefore, we developed a mechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model for bispecific anti‐TfR/BACE1 antibodies that accounts for antibody‐TfR interactions at the blood‐brain barrier (BBB) as well as the pharmacodynamic (PD) effect of anti‐BACE1 arm. The calibrated model correctly predicted the optimal anti‐TfR affinity required to maximize brain exposure of therapeutic antibodies in the cynomolgus monkey and was scaled to predict the optimal affinity of anti‐TfR bispecifics in humans. Thus, this model provides a framework for testing critical translational predictions for anti‐TfR bispecific antibodies, including choice of candidate molecule for clinical development. PMID:27299941

Countercurrent flow limited (CCFL) heat flux in the high flux isotope reactor (HFIR) fuel element

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruggles, A.E.

1990-10-12

The countercurrent flow (CCF) performance in the fuel element region of the HFIR is examined experimentally and theoretically. The fuel element consists of two concentric annuli filled with aluminum clad fuel plates of 1.27 mm thickness separated by 1.27 mm flow channels. The plates are curved as they go radially outward to accomplish constant flow channel width and constant metal-to-coolant ratio. A full-scale HFIR fuel element mock-up is studied in an adiabatic air-water CCF experiment. A review of CCF models for narrow channels is presented along with the treatment of CCFs in system of parallel channels. The experimental results aremore » related to the existing models and a mechanistic model for the annular'' CCF in a narrow channel is developed that captures the data trends well. The results of the experiment are used to calculate the CCFL heat flux of the HFIR fuel assembly. It was determined that the HFIR fuel assembly can reject 0.62 Mw of thermal power in the CCFL situation. 31 refs., 17 figs.« less

A Mechanistically Informed User-Friendly Model to Predict Greenhouse Gas (GHG) Fluxes and Carbon Storage from Coastal Wetlands

NASA Astrophysics Data System (ADS)

Abdul-Aziz, O. I.; Ishtiaq, K. S.

2015-12-01

We present a user-friendly modeling tool on MS Excel to predict the greenhouse gas (GHG) fluxes and estimate potential carbon sequestration from the coastal wetlands. The dominant controls of wetland GHG fluxes and their relative mechanistic linkages with various hydro-climatic, sea level, biogeochemical and ecological drivers were first determined by employing a systematic data-analytics method, including Pearson correlation matrix, principal component and factor analyses, and exploratory partial least squares regressions. The mechanistic knowledge and understanding was then utilized to develop parsimonious non-linear (power-law) models to predict wetland carbon dioxide (CO2) and methane (CH4) fluxes based on a sub-set of climatic, hydrologic and environmental drivers such as the photosynthetically active radiation, soil temperature, water depth, and soil salinity. The models were tested with field data for multiple sites and seasons (2012-13) collected from the Waquoit Bay, MA. The model estimated the annual wetland carbon storage by up-scaling the instantaneous predicted fluxes to an extended growing season (e.g., May-October) and by accounting for the net annual lateral carbon fluxes between the wetlands and estuary. The Excel Spreadsheet model is a simple ecological engineering tool for coastal carbon management and their incorporation into a potential carbon market under a changing climate, sea level and environment. Specifically, the model can help to determine appropriate GHG offset protocols and monitoring plans for projects that focus on tidal wetland restoration and maintenance.

Transgenerational Adaptation to Pollution Changes Energy Allocation in Populations of Nematodes.

PubMed

Goussen, Benoit; Péry, Alexandre R R; Bonzom, Jean-Marc; Beaudouin, Rémy

2015-10-20

Assessing the evolutionary responses of long-term exposed populations requires multigeneration ecotoxicity tests. However, the analysis of the data from these tests is not straightforward. Mechanistic models allow the in-depth analysis of the variation of physiological traits over many generations, by quantifying the trend of the physiological and toxicological parameters of the model. In the present study, a bioenergetic mechanistic model has been used to assess the evolution of two populations of the nematode Caenorhabditis elegans in control conditions or exposed to uranium. This evolutionary pressure resulted in a brood size reduction of 60%. We showed an adaptation of individuals of both populations to experimental conditions (increase of maximal length, decrease of growth rate, decrease of brood size, and decrease of the elimination rate). In addition, differential evolution was also highlighted between the two populations once the maternal effects had been diminished after several generations. Thus, individuals that were greater in maximal length, but with apparently a greater sensitivity to uranium were selected in the uranium population. In this study, we showed that this bioenergetics mechanistic modeling approach provided a precise, certain, and powerful analysis of the life strategy of C. elegans populations exposed to heavy metals resulting in an evolutionary pressure across successive generations.

In Silico, Experimental, Mechanistic Model for Extended-Release Felodipine Disposition Exhibiting Complex Absorption and a Highly Variable Food Interaction

PubMed Central

Kim, Sean H. J.; Jackson, Andre J.; Hunt, C. Anthony

2014-01-01

The objective of this study was to develop and explore new, in silico experimental methods for deciphering complex, highly variable absorption and food interaction pharmacokinetics observed for a modified-release drug product. Toward that aim, we constructed an executable software analog of study participants to whom product was administered orally. The analog is an object- and agent-oriented, discrete event system, which consists of grid spaces and event mechanisms that map abstractly to different physiological features and processes. Analog mechanisms were made sufficiently complicated to achieve prespecified similarity criteria. An equation-based gastrointestinal transit model with nonlinear mixed effects analysis provided a standard for comparison. Subject-specific parameterizations enabled each executed analog’s plasma profile to mimic features of the corresponding six individual pairs of subject plasma profiles. All achieved prespecified, quantitative similarity criteria, and outperformed the gastrointestinal transit model estimations. We observed important subject-specific interactions within the simulation and mechanistic differences between the two models. We hypothesize that mechanisms, events, and their causes occurring during simulations had counterparts within the food interaction study: they are working, evolvable, concrete theories of dynamic interactions occurring within individual subjects. The approach presented provides new, experimental strategies for unraveling the mechanistic basis of complex pharmacological interactions and observed variability. PMID:25268237

Multi input single output model predictive control of non-linear bio-polymerization process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arumugasamy, Senthil Kumar; Ahmad, Z.

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state spacemore » model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.« less

Investigating Atmospheric Oxidation with Molecular Dynamics Imaging and Spectroscopy

NASA Astrophysics Data System (ADS)

Merrill, W. G.; Case, A. S.; Keutsch, F. N.

2013-06-01

Volatile organic compounds (VOCs) in the Earth's atmosphere constitute trace gas species emitted primarily from the biosphere, and are the subject of inquiry for a variety of air quality and climate studies. Reactions intiated (primarily) by the hydroxyl radical (OH) lead to a myriad of oxygenated species (OVOCs), which in turn are prone to further oxidation. Investigations of the role that VOC oxidation plays in tropospheric chemistry have brought to light two troubling scenarios: (1) VOCs are responsible in part for the production of two EPA-regulated pollutants---tropospheric ozone and organic aerosol---and (2) the mechanistic details of VOC oxidation remain convoluted and poorly understood. The latter issue hampers the implementation of near-explicit atmospheric simulations, and large discrepancies in OH reactivity exist between measurements and models at present. Such discrepancies underscore the need for a more thorough description of VOC oxidation. Time-of-flight measurements and ion-imaging techniques are viable options for resolving some of the mechanistic and energetic details of VOC oxidation. Molecular beam studies have the advantage of foregoing unwanted bimolecular reactions, allowing for the characterization of specific processes which must typically compete with the complex manifold of VOC oxidation pathways. The focus of this work is on the unimolecular channels of organic peroxy radical intermediates, which are necessarily generated during VOC oxidation. Such intermediates may isomerize and decompose into distinct chemical channels, enabling the unambiguous detection of each pathway. For instance, a (1 + 1') resonance enhanced multiphoton ionization (REMPI) scheme may be employed to detect carbon monoxide generated from a particular unimolecular process. A number of more subtle mechanistic details may be explored as well. By varying the mean free path of the peroxy radicals in a flow tube, the role of collisional quenching in these unimolecular channels can be assessed. Reactive species may also be introduced to explore the competition between bimolecular and unimolecular pathways. Vibrational modes may also be excited by an IR laser, providing insight about the role of vibrational mediation in VOC oxidation.

Mechanistic and Evolutionary Insights from Comparative Enzymology of Phosphomonoesterases and Phosphodiesterases across the Alkaline Phosphatase Superfamily

PubMed Central

2016-01-01

Naively one might have expected an early division between phosphate monoesterases and diesterases of the alkaline phosphatase (AP) superfamily. On the contrary, prior results and our structural and biochemical analyses of phosphate monoesterase PafA, from Chryseobacterium meningosepticum, indicate similarities to a superfamily phosphate diesterase [Xanthomonas citri nucleotide pyrophosphatase/phosphodiesterase (NPP)] and distinct differences from the three metal ion AP superfamily monoesterase, from Escherichia coli AP (EcAP). We carried out a series of experiments to map out and learn from the differences and similarities between these enzymes. First, we asked why there would be independent instances of monoesterases in the AP superfamily? PafA has a much weaker product inhibition and slightly higher activity relative to EcAP, suggesting that different metabolic evolutionary pressures favored distinct active-site architectures. Next, we addressed the preferential phosphate monoester and diester catalysis of PafA and NPP, respectively. We asked whether the >80% sequence differences throughout these scaffolds provide functional specialization for each enzyme’s cognate reaction. In contrast to expectations from this model, PafA and NPP mutants with the common subset of active-site groups embedded in each native scaffold had the same monoesterase:diesterase specificities; thus, the >107-fold difference in native specificities appears to arise from distinct interactions at a single phosphoryl substituent. We also uncovered striking mechanistic similarities between the PafA and EcAP monoesterases, including evidence for ground-state destabilization and functional active-site networks that involve different active-site groups but may play analogous catalytic roles. Discovering common network functions may reveal active-site architectural connections that are critical for function, and identifying regions of functional modularity may facilitate the design of new enzymes from existing promiscuous templates. More generally, comparative enzymology and analysis of catalytic promiscuity can provide mechanistic and evolutionary insights. PMID:27670607

Constraining 3-PG with a new δ13C submodel: a test using the δ13C of tree rings.

PubMed

Wei, Liang; Marshall, John D; Link, Timothy E; Kavanagh, Kathleen L; DU, Enhao; Pangle, Robert E; Gag, Peter J; Ubierna, Nerea

2014-01-01

A semi-mechanistic forest growth model, 3-PG (Physiological Principles Predicting Growth), was extended to calculate δ(13)C in tree rings. The δ(13)C estimates were based on the model's existing description of carbon assimilation and canopy conductance. The model was tested in two ~80-year-old natural stands of Abies grandis (grand fir) in northern Idaho. We used as many independent measurements as possible to parameterize the model. Measured parameters included quantum yield, specific leaf area, soil water content and litterfall rate. Predictions were compared with measurements of transpiration by sap flux, stem biomass, tree diameter growth, leaf area index and δ(13)C. Sensitivity analysis showed that the model's predictions of δ(13)C were sensitive to key parameters controlling carbon assimilation and canopy conductance, which would have allowed it to fail had the model been parameterized or programmed incorrectly. Instead, the simulated δ(13)C of tree rings was no different from measurements (P > 0.05). The δ(13)C submodel provides a convenient means of constraining parameter space and avoiding model artefacts. This δ(13)C test may be applied to any forest growth model that includes realistic simulations of carbon assimilation and transpiration. © 2013 John Wiley & Sons Ltd.

Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models

NASA Astrophysics Data System (ADS)

Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea

2014-05-01

Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.

Improving Interpretation of New and Old Serum Biomarkers of Drug-Induced Liver Injury Through Mechanistic Modeling.

PubMed

Watkins, Paul B

2018-04-26

The study by Mason et al. in this issue used mechanistic modeling and simulation to address how both the dose of acetaminophen consumed and the time since ingestion can be estimated from biomarkers measured in a single serum sample in mice. Translation into the clinic would potentially be an advance in the treatment of acetaminophen poisoning. Importantly, this approach could transform the evaluation of liver safety in clinical trials of new drug candidates. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Energy efficiency drives the global seasonal distribution of birds.

PubMed

Somveille, Marius; Rodrigues, Ana S L; Manica, Andrea

2018-06-01

The uneven distribution of biodiversity on Earth is one of the most general and puzzling patterns in ecology. Many hypotheses have been proposed to explain it, based on evolutionary processes or on constraints related to geography and energy. However, previous studies investigating these hypotheses have been largely descriptive due to the logistical difficulties of conducting controlled experiments on such large geographical scales. Here, we use bird migration-the seasonal redistribution of approximately 15% of bird species across the world-as a natural experiment for testing the species-energy relationship, the hypothesis that animal diversity is driven by energetic constraints. We develop a mechanistic model of bird distributions across the world, and across seasons, based on simple ecological and energetic principles. Using this model, we show that bird species distributions optimize the balance between energy acquisition and energy expenditure while taking into account competition with other species. These findings support, and provide a mechanistic explanation for, the species-energy relationship. The findings also provide a general explanation of migration as a mechanism that allows birds to optimize their energy budget in the face of seasonality and competition. Finally, our mechanistic model provides a tool for predicting how ecosystems will respond to global anthropogenic change.

Stochastic Human Exposure and Dose Simulation Model for Pesticides

EPA Science Inventory

SHEDS-Pesticides (Stochastic Human Exposure and Dose Simulation Model for Pesticides) is a physically-based stochastic model developed to quantify exposure and dose of humans to multimedia, multipathway pollutants. Probabilistic inputs are combined in physical/mechanistic algorit...

Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale

NASA Astrophysics Data System (ADS)

Marçais, J.; Gupta, H. V.; De Dreuzy, J. R.; Troch, P. A. A.

2016-12-01

Geomorphological structure and geological heterogeneity of hillslopes are major controls on runoff responses. The diversity of hillslopes (morphological shapes and geological structures) on one hand, and the highly non linear runoff mechanism response on the other hand, make it difficult to transpose what has been learnt at one specific hillslope to another. Therefore, making reliable predictions on runoff appearance or river flow for a given hillslope is a challenge. Applying a classic model calibration (based on inverse problems technique) requires doing it for each specific hillslope and having some data available for calibration. When applied to thousands of cases it cannot always be promoted. Here we propose a novel modeling framework based on coupling process based models with data based approach. First we develop a mechanistic model, based on hillslope storage Boussinesq equations (Troch et al. 2003), able to model non linear runoff responses to rainfall at the hillslope scale. Second we set up a model database, representing thousands of non calibrated simulations. These simulations investigate different hillslope shapes (real ones obtained by analyzing 5m digital elevation model of Brittany and synthetic ones), different hillslope geological structures (i.e. different parametrizations) and different hydrologic forcing terms (i.e. different infiltration chronicles). Then, we use this model library to train a machine learning model on this physically based database. Machine learning model performance is then assessed by a classic validating phase (testing it on new hillslopes and comparing machine learning with mechanistic outputs). Finally we use this machine learning model to learn what are the hillslope properties controlling runoffs. This methodology will be further tested combining synthetic datasets with real ones.

The PI3K inhibitor GDC-0941 combines with existing clinical regimens for superior activity in multiple myeloma.

PubMed

Munugalavadla, V; Mariathasan, S; Slaga, D; Du, C; Berry, L; Del Rosario, G; Yan, Y; Boe, M; Sun, L; Friedman, L S; Chesi, M; Leif Bergsagel, P; Ebens, A

2014-01-16

The phosphatidylinositol 3'-kinase (PI3K) pathway is dysregulated in multiple myeloma (MM); we therefore tested a highly selective class I PI3K inhibitor, GDC-0941, for anti-myeloma activity. Functional and mechanistic studies were first performed in MM cell lines, then extended to primary MM patient samples cultured in vitro. GDC-0941 was then assessed as a single agent and in various combinations in myeloma tumor xenograft models. We show p110 α and β are the predominant PI3K catalytic subunits in MM and that a highly selective class I PI3K inhibitor, GDC-0941, has robust activity as a single agent to induce cell cycle arrest and apoptosis of both MM cell lines and patient myeloma cells. Mechanistic studies revealed an induction of cell cycle arrest at G0/G1, with decreased phospho-FoxO1/3a levels, decreased cyclin D1 and c-myc expression, and an increase in the cell cycle inhibitor, p27kip. Induction of apoptosis correlated with increased expression of the pro-apoptotic BH3-only protein BIM, cleaved caspase 3 and cleaved poly (ADP-ribose) polymerase (PARP). In vitro, GDC-0941 synergized with dexamethasone (Dex) and lenalidomide (combination index values of 0.3-0.4 and 0.4-0.8, respectively); in vivo GDC-0941 has anti-myeloma activity and significantly increases the activity of the standard of care agents in several murine xenograft tumor models (additional tumor growth inhibition of 37-53% (Dex) and 22-72% (lenalidomide)). These data provide a clear therapeutic hypothesis for the inhibition of PI3K and provide a rationale for clinical development of GDC-0941 in myeloma.

Use of Mechanistic Modeling to Assess Interindividual Variability and Interspecies Differences in Active Uptake in Human and Rat Hepatocytes

PubMed Central

Ménochet, Karelle; Kenworthy, Kathryn E.; Houston, J. Brian

2012-01-01

Interindividual variability in activity of uptake transporters is evident in vivo, yet limited data exist in vitro, confounding in vitro-in vivo extrapolation. The uptake kinetics of seven organic anion-transporting polypeptide substrates was investigated over a concentration range in plated cryopreserved human hepatocytes. Active uptake clearance (CLactive, u), bidirectional passive diffusion (Pdiff), intracellular binding, and metabolism were estimated for bosentan, pitavastatin, pravastatin, repaglinide, rosuvastatin, telmisartan, and valsartan in HU4122 donor using a mechanistic two-compartment model in Matlab. Full uptake kinetics of rosuvastatin and repaglinide were also characterized in two additional donors, whereas for the remaining drugs CLactive, u was estimated at a single concentration. The unbound affinity constant (Km, u) and Pdiff values were consistent across donors, whereas Vmax was on average up to 2.8-fold greater in donor HU4122. Consistency in Km, u values allowed extrapolation of single concentration uptake activity data and assessment of interindividual variability in CLactive across donors. The maximal contribution of active transport to total uptake differed among donors, for example, 85 to 96% and 68 to 87% for rosuvastatin and repaglinide, respectively; however, in all cases the active process was the major contributor. In vitro-in vivo extrapolation indicated a general underprediction of hepatic intrinsic clearance, an average empirical scaling factor of 17.1 was estimated on the basis of seven drugs investigated in three hepatocyte donors, and donor-specific differences in empirical factors are discussed. Uptake Km, u and CLactive, u were on average 4.3- and 7.1-fold lower in human hepatocytes compared with our previously published rat data. A strategy for the use of rat uptake data to facilitate the experimental design in human hepatocytes is discussed. PMID:22665271

Depth- and range-dependent variation in the performance of aquatic telemetry systems: understanding and predicting the susceptibility of acoustic tag-receiver pairs to close proximity detection interference.

PubMed

Scherrer, Stephen R; Rideout, Brendan P; Giorli, Giacomo; Nosal, Eva-Marie; Weng, Kevin C

2018-01-01

Passive acoustic telemetry using coded transmitter tags and stationary receivers is a popular method for tracking movements of aquatic animals. Understanding the performance of these systems is important in array design and in analysis. Close proximity detection interference (CPDI) is a condition where receivers fail to reliably detect tag transmissions. CPDI generally occurs when the tag and receiver are near one another in acoustically reverberant settings. Here we confirm transmission multipaths reflected off the environment arriving at a receiver with sufficient delay relative to the direct signal cause CPDI. We propose a ray-propagation based model to estimate the arrival of energy via multipaths to predict CPDI occurrence, and we show how deeper deployments are particularly susceptible. A series of experiments were designed to develop and validate our model. Deep (300 m) and shallow (25 m) ranging experiments were conducted using Vemco V13 acoustic tags and VR2-W receivers. Probabilistic modeling of hourly detections was used to estimate the average distance a tag could be detected. A mechanistic model for predicting the arrival time of multipaths was developed using parameters from these experiments to calculate the direct and multipath path lengths. This model was retroactively applied to the previous ranging experiments to validate CPDI observations. Two additional experiments were designed to validate predictions of CPDI with respect to combinations of deployment depth and distance. Playback of recorded tags in a tank environment was used to confirm multipaths arriving after the receiver's blanking interval cause CPDI effects. Analysis of empirical data estimated the average maximum detection radius (AMDR), the farthest distance at which 95% of tag transmissions went undetected by receivers, was between 840 and 846 m for the deep ranging experiment across all factor permutations. From these results, CPDI was estimated within a 276.5 m radius of the receiver. These empirical estimations were consistent with mechanistic model predictions. CPDI affected detection at distances closer than 259-326 m from receivers. AMDR determined from the shallow ranging experiment was between 278 and 290 m with CPDI neither predicted nor observed. Results of validation experiments were consistent with mechanistic model predictions. Finally, we were able to predict detection/nondetection with 95.7% accuracy using the mechanistic model's criterion when simulating transmissions with and without multipaths. Close proximity detection interference results from combinations of depth and distance that produce reflected signals arriving after a receiver's blanking interval has ended. Deployment scenarios resulting in CPDI can be predicted with the proposed mechanistic model. For deeper deployments, sea-surface reflections can produce CPDI conditions, resulting in transmission rejection, regardless of the reflective properties of the seafloor.

Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

USGS Publications Warehouse

Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

2018-01-01

Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate risks posed by mobile disease hosts. More broadly, we demonstrate how mechanistic movement models can provide predictions of ecological conditions that are consistent with climate change but may be more extreme than has been observed historically.

Agonistic TAM-163 antibody targeting tyrosine kinase receptor-B: applying mechanistic modeling to enable preclinical to clinical translation and guide clinical trial design.

PubMed

Vugmeyster, Yulia; Rohde, Cynthia; Perreault, Mylene; Gimeno, Ruth E; Singh, Pratap

2013-01-01

TAM-163, an agonist monoclonal antibody targeting tyrosine receptor kinase-B (TrkB), is currently being investigated as a potential body weight modulatory agent in humans. To support the selection of the dose range for the first-in-human (FIH) trial of TAM-163, we conducted a mechanistic analysis of the pharmacokinetic (PK) and pharmacodynamic (PD) data (e.g., body weight gain) obtained in lean cynomolgus and obese rhesus monkeys following single doses ranging from 0.3 to 60 mg/kg. A target-mediated drug disposition (TMDD) model was used to describe the observed nonlinear PK and Emax approach was used to describe the observed dose-dependent PD effect. The TMDD model development was supported by the experimental determination of the binding affinity constant (9.4 nM) and internalization rate of the drug-target complex (2.08 h(-1)). These mechanistic analyses enabled linking of exposure, target (TrkB) coverage, and pharmacological activity (e.g., PD) in monkeys, and indicated that ≥ 38% target coverage (time-average) was required to achieve significant body weight gain in monkeys. Based on the scaling of the TMDD model from monkeys to humans and assuming similar relationship between the target coverage and pharmacological activity between monkey and humans, subcutaneous (SC) doses of 1 and 15 mg/kg in humans were projected to be the minimally and the fully pharmacologically active doses, respectively. Based on the minimal anticipated biological effect level (MABEL) approach for starting dose selection, the dose of 0.05 mg/kg (3 mg for a 60 kg human) SC was recommended as the starting dose for FIH trials, because at this dose level<10% target coverage was projected at Cmax (and all other time points). This study illustrates a rational mechanistic approach for the selection of FIH dose range for a therapeutic protein with a complex model of action.

How Does Rumination Impact Cognition? A First Mechanistic Model.

PubMed

van Vugt, Marieke K; van der Velde, Maarten

2018-01-01

Rumination is a process of uncontrolled, narrowly focused negative thinking that is often self-referential, and that is a hallmark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we developed on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumination is caused by maladaptive habits of thought. These habits of thought are modeled by adjusting the number of memory chunks and their associative structure, which changes the sequence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memories is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sampling study in healthy and depressed participants. On the basis of this empirically derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better understand the cognitive mechanisms underlying rumination and depression. Copyright © 2018 The Authors. Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

TLRs to cytokines: mechanistic insights from the imiquimod mouse model of psoriasis.

PubMed

Flutter, Barry; Nestle, Frank O

2013-12-01

Psoriasis is an inflammatory disease of the skin affecting 2-3% of the population, characterized by a thickening of the epidermis and immune infiltrates throughout the dermis and epidermis, causing skin lesions that can seriously affect quality of life. The study of psoriasis has historically been hampered by the lack of good animal models. Various genetically induced models exist, which have provided some information about possible mechanisms of disease, but these models rely mostly on intrinsic imbalances of homeostasis. However, a mouse model of psoriasiform dermatitis caused by the repeated topical application of Aldara™ containing 5% imiquimod was described in 2009. The mechanisms of action of Aldara™ are complex. Imiquimod is an effective ligand for TLR7 (and TLR8 in humans) and also interferes with adenosine receptor signaling. In addition, isostearic acid present in the Aldara™ vehicle has been shown to be biologically active and of importance for activating the inflammasome. Interestingly, the repetitive application of Aldara™ reveals a complex aetiology involving multiple cell types, cytokines, and inflammatory pathways. In this review, we will dissect the findings of the imiquimod model to date and ask how this model can inform us about the immunological aspects of human disease. © 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.

Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization.

PubMed

Roberts, David W; Patlewicz, Grace; Kern, Petra S; Gerberick, Frank; Kimber, Ian; Dearman, Rebecca J; Ryan, Cindy A; Basketter, David A; Aptula, Aynur O

2007-07-01

The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.

Censored rainfall modelling for estimation of fine-scale extremes

NASA Astrophysics Data System (ADS)

Cross, David; Onof, Christian; Winter, Hugo; Bernardara, Pietro

2018-01-01

Reliable estimation of rainfall extremes is essential for drainage system design, flood mitigation, and risk quantification. However, traditional techniques lack physical realism and extrapolation can be highly uncertain. In this study, we improve the physical basis for short-duration extreme rainfall estimation by simulating the heavy portion of the rainfall record mechanistically using the Bartlett-Lewis rectangular pulse (BLRP) model. Mechanistic rainfall models have had a tendency to underestimate rainfall extremes at fine temporal scales. Despite this, the simple process representation of rectangular pulse models is appealing in the context of extreme rainfall estimation because it emulates the known phenomenology of rainfall generation. A censored approach to Bartlett-Lewis model calibration is proposed and performed for single-site rainfall from two gauges in the UK and Germany. Extreme rainfall estimation is performed for each gauge at the 5, 15, and 60 min resolutions, and considerations for censor selection discussed.

Do the same traffic rules apply? Directional chromosome segregation by SpoIIIE and FtsK.

PubMed

Besprozvannaya, Marina; Burton, Briana M

2014-08-01

Over a decade of studies have tackled the question of how FtsK/SpoIIIE translocases establish and maintain directional DNA translocation during chromosome segregation in bacteria. FtsK/SpoIIIE translocases move DNA in a highly processive, directional manner, where directionality is facilitated by sequences on the substrate DNA molecules that are being transported. In recent years, structural, biochemical, single-molecule and high-resolution microscopic studies have provided new insight into the mechanistic details of directional DNA segregation. Out of this body of work, a series of models have emerged and, ultimately, yielded two seemingly opposing models: the loading model and the target search model. We review these recent mechanistic insights into directional DNA movement and discuss the data that may serve to unite these suggested models, as well as propose future directions that may ultimately solve the debate. © 2014 John Wiley & Sons Ltd.

Understanding essential tremor: progress on the biological front.

PubMed

Louis, Elan D

2014-06-01

For many years, little was written about the underlying biology of ET, despite its high prevalence. Discussions of disease mechanisms were dominated by a focus on tremor physiology. The traditional model of ET, the olivary model, was proposed in the 1970s. The model suffers from several critical problems, and its relevance to ET has been questioned. Recent mechanistic research has focused on the cerebellum. Clinical and neuroimaging studies strongly implicate the importance of this brain region in ET. Recent mechanistic research has been grounded more in tissue-based changes (i.e., postmortem studies of the brain). These studies have collectively and systematically identified a sizable number of changes in the ET cerebellum, and have led to a new model of ET, referred to as the cerebellar degenerative model. Hence, there is a renewed interest in the science behind the biology of ET. How the new understanding of ET will translate into treatment changes is an open question.

Development and validation of deterioration models for concrete bridge decks - phase 2 : mechanics-based degradation models.

DOT National Transportation Integrated Search

2013-06-01

This report summarizes a research project aimed at developing degradation models for bridge decks in the state of Michigan based on durability mechanics. A probabilistic framework to implement local-level mechanistic-based models for predicting the c...

Mechanisms of Developmental Change in Infant Categorization

ERIC Educational Resources Information Center

Westermann, Gert; Mareschal, Denis

2012-01-01

Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in…

Molybdenum Enzymes, Cofactors, and Model Systems.

ERIC Educational Resources Information Center

Burgmayer, S. J. N; Stiefel, E. I.

1985-01-01

Discusses: (l) molybdoenzymes (examining their distribution and metabolic role, composition and redox strategy, cofactors, substrate reactions, and mechanistic possibilities); (2) structural information on molybdenum (Mo) centers; (3) modeling studies (Mo-co models, nitrogenase models, and the MO-S duo); and (4) the copper-molybdenum antagonism.…

Modeling of the nearshore marine ecosystem with the AQUATOX model

EPA Science Inventory

Process-based models can be used to forecast the responses of coastal ecosystems to changes under future scenarios. However, most models applied to coastal systems do not include higher trophic levels, which are important providers of ecosystem services. AQUATOX is a mechanistic...

Simulating effects of fire on northern Rocky Mountain landscapes with the ecological process model FIRE-BGC.

PubMed

Keane, R E; Ryan, K C; Running, S W

1996-03-01

A mechanistic, biogeochemical succession model, FIRE-BGC, was used to investigate the role of fire on long-term landscape dynamics in northern Rocky Mountain coniferous forests of Glacier National Park, Montana, USA. FIRE-BGC is an individual-tree model-created by merging the gap-phase process-based model FIRESUM with the mechanistic ecosystem biogeochemical model FOREST-BGC-that has mixed spatial and temporal resolution in its simulation architecture. Ecological processes that act at a landscape level, such as fire and seed dispersal, are simulated annually from stand and topographic information. Stand-level processes, such as tree establishment, growth and mortality, organic matter accumulation and decomposition, and undergrowth plant dynamics are simulated both daily and annually. Tree growth is mechanistically modeled based on the ecosystem process approach of FOREST-BGC where carbon is fixed daily by forest canopy photosynthesis at the stand level. Carbon allocated to the tree stem at the end of the year generates the corresponding diameter and height growth. The model also explicitly simulates fire behavior and effects on landscape characteristics. We simulated the effects of fire on ecosystem characteristics of net primary productivity, evapotranspiration, standing crop biomass, nitrogen cycling and leaf area index over 200 years for the 50,000-ha McDonald Drainage in Glacier National Park. Results show increases in net primary productivity and available nitrogen when fires are included in the simulation. Standing crop biomass and evapotranspiration decrease under a fire regime. Shade-intolerant species dominate the landscape when fires are excluded. Model tree increment predictions compared well with field data.

Modeling process-structure-property relationships for additive manufacturing

NASA Astrophysics Data System (ADS)

Yan, Wentao; Lin, Stephen; Kafka, Orion L.; Yu, Cheng; Liu, Zeliang; Lian, Yanping; Wolff, Sarah; Cao, Jian; Wagner, Gregory J.; Liu, Wing Kam

2018-02-01

This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the processstructure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a highefficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

Technical note: Bayesian calibration of dynamic ruminant nutrition models.

PubMed

Reed, K F; Arhonditsis, G B; France, J; Kebreab, E

2016-08-01

Mechanistic models of ruminant digestion and metabolism have advanced our understanding of the processes underlying ruminant animal physiology. Deterministic modeling practices ignore the inherent variation within and among individual animals and thus have no way to assess how sources of error influence model outputs. We introduce Bayesian calibration of mathematical models to address the need for robust mechanistic modeling tools that can accommodate error analysis by remaining within the bounds of data-based parameter estimation. For the purpose of prediction, the Bayesian approach generates a posterior predictive distribution that represents the current estimate of the value of the response variable, taking into account both the uncertainty about the parameters and model residual variability. Predictions are expressed as probability distributions, thereby conveying significantly more information than point estimates in regard to uncertainty. Our study illustrates some of the technical advantages of Bayesian calibration and discusses the future perspectives in the context of animal nutrition modeling. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A mechanistic pharmacokinetic model to assess modified oral drug bioavailability post bariatric surgery in morbidly obese patients: interplay between CYP3A gut wall metabolism, permeability and dissolution.

PubMed

Darwich, Adam S; Pade, Devendra; Ammori, Basil J; Jamei, Masoud; Ashcroft, Darren M; Rostami-Hodjegan, Amin

2012-07-01

Due to the multi-factorial physiological implications of bariatric surgery, attempts to explain trends in oral bioavailability following bariatric surgery using singular attributes of drugs or simplified categorisations such as the biopharmaceutics classification system have been unsuccessful. So we have attempted to use mechanistic models to assess changes to bioavailability of model drugs. Pharmacokinetic post bariatric surgery models were created for Roux-en-Y gastric bypass, biliopancreatic diversion with duodenal switch, sleeve gastrectomy and jejunoileal bypass, through altering the 'Advanced Dissolution Absorption and Metabolism' (ADAM) model incorporated into the Simcyp® Simulator. Post to pre surgical simulations were carried out for five drugs with varying characteristics regarding their gut wall metabolism, dissolution and permeability (simvastatin, omeprazole, diclofenac, fluconazole and ciprofloxacin). The trends in oral bioavailability pre to post surgery were found to be dependent on a combination of drug parameters, including solubility, permeability and gastrointestinal metabolism as well as the surgical procedure carried out. In the absence of clinical studies, the ability to project the direction and the magnitude of changes in bioavailability of drug therapy, using evidence-based mechanistic pharmacokinetic in silico models would be of significant value in guiding prescribers to make the necessary adjustments to dosage regimens for an increasing population of patients who are undergoing bariatric surgery. © 2012 The Authors. JPP © 2012 Royal Pharmaceutical Society.

Calculating Henry’s Constants of Charged Molecules Using SPARC

EPA Science Inventory

SPARC Performs Automated Reasoning in Chemistry is a computer program designed to model physical and chemical properties of molecules solely based on thier chemical structure. SPARC uses a toolbox of mechanistic perturbation models to model intermolecular interactions. SPARC has ...

Operationalizing resilience using state and transition models

USDA-ARS?s Scientific Manuscript database

In management, restoration, and policy contexts, the notion of resilience can be confusing. Systematic development of conceptual models of ecological state change (state transition models; STMs) can help overcome semantic confusion and promote a mechanistic understanding of resilience. Drawing on ex...

Estimating heterotrophic respiration at large scales: Challenges, approaches, and next steps

USGS Publications Warehouse

Bond-Lamberty, Ben; Epron, Daniel; Harden, Jennifer W.; Harmon, Mark E.; Hoffman, Forrest; Kumar, Jitendra; McGuire, Anthony David; Vargas, Rodrigo

2016-01-01

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of “Decomposition Functional Types” (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelers and experimentalists to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present an example clustering analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from (but complementary to) already-existing PFTs. A similar analysis incorporating observational data could form the basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with rigorous testing of analytical results; using point measurements and realistic forcing variables to constrain process-based models; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR.

Overview: early history of crop growth and photosynthesis modeling.

PubMed

El-Sharkawy, Mabrouk A

2011-02-01

As in industrial and engineering systems, there is a need to quantitatively study and analyze the many constituents of complex natural biological systems as well as agro-ecosystems via research-based mechanistic modeling. This objective is normally addressed by developing mathematically built descriptions of multilevel biological processes to provide biologists a means to integrate quantitatively experimental research findings that might lead to a better understanding of the whole systems and their interactions with surrounding environments. Aided with the power of computational capacities associated with computer technology then available, pioneering cropping systems simulations took place in the second half of the 20th century by several research groups across continents. This overview summarizes that initial pioneering effort made to simulate plant growth and photosynthesis of crop canopies, focusing on the discovery of gaps that exist in the current scientific knowledge. Examples are given for those gaps where experimental research was needed to improve the validity and application of the constructed models, so that their benefit to mankind was enhanced. Such research necessitates close collaboration among experimentalists and model builders while adopting a multidisciplinary/inter-institutional approach. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Distinguishing Antimicrobial Models with Different Resistance Mechanisms via Population Pharmacodynamic Modeling

PubMed Central

Jacobs, Matthieu; Grégoire, Nicolas; Couet, William; Bulitta, Jurgen B.

2016-01-01

Semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling is increasingly used for antimicrobial drug development and optimization of dosage regimens, but systematic simulation-estimation studies to distinguish between competing PD models are lacking. This study compared the ability of static and dynamic in vitro infection models to distinguish between models with different resistance mechanisms and support accurate and precise parameter estimation. Monte Carlo simulations (MCS) were performed for models with one susceptible bacterial population without (M1) or with a resting stage (M2), a one population model with adaptive resistance (M5), models with pre-existing susceptible and resistant populations without (M3) or with (M4) inter-conversion, and a model with two pre-existing populations with adaptive resistance (M6). For each model, 200 datasets of the total bacterial population were simulated over 24h using static antibiotic concentrations (256-fold concentration range) or over 48h under dynamic conditions (dosing every 12h; elimination half-life: 1h). Twelve-hundred random datasets (each containing 20 curves for static or four curves for dynamic conditions) were generated by bootstrapping. Each dataset was estimated by all six models via population PD modeling to compare bias and precision. For M1 and M3, most parameter estimates were unbiased (<10%) and had good imprecision (<30%). However, parameters for adaptive resistance and inter-conversion for M2, M4, M5 and M6 had poor bias and large imprecision under static and dynamic conditions. For datasets that only contained viable counts of the total population, common statistical criteria and diagnostic plots did not support sound identification of the true resistance mechanism. Therefore, it seems advisable to quantify resistant bacteria and characterize their MICs and resistance mechanisms to support extended simulations and translate from in vitro experiments to animal infection models and ultimately patients. PMID:26967893

Evolutionary and mechanistic theories of aging.

PubMed

Hughes, Kimberly A; Reynolds, Rose M

2005-01-01

Senescence (aging) is defined as a decline in performance and fitness with advancing age. Senescence is a nearly universal feature of multicellular organisms, and understanding why it occurs is a long-standing problem in biology. Here we present a concise review of both evolutionary and mechanistic theories of aging. We describe the development of the general evolutionary theory, along with the mutation accumulation, antagonistic pleiotropy, and disposable soma versions of the evolutionary model. The review of the mechanistic theories focuses on the oxidative stress resistance, cellular signaling, and dietary control mechanisms of life span extension. We close with a discussion of how an approach that makes use of both evolutionary and molecular analyses can address a critical question: Which of the mechanisms that can cause variation in aging actually do cause variation in natural populations?

Disentangling the Role of Domain-Specific Knowledge in Student Modeling

NASA Astrophysics Data System (ADS)

Ruppert, John; Duncan, Ravit Golan; Chinn, Clark A.

2017-08-01

This study explores the role of domain-specific knowledge in students' modeling practice and how this knowledge interacts with two domain-general modeling strategies: use of evidence and developing a causal mechanism. We analyzed models made by middle school students who had a year of intensive model-based instruction. These models were made to explain a familiar but unstudied biological phenomenon: late onset muscle pain. Students were provided with three pieces of evidence related to this phenomenon and asked to construct a model to account for this evidence. Findings indicate that domain-specific resources play a significant role in the extent to which the models accounted for provided evidence. On the other hand, familiarity with the situation appeared to contribute to the mechanistic character of models. Our results indicate that modeling strategies alone are insufficient for the development of a mechanistic model that accounts for provided evidence and that, while learners can develop a tentative model with a basic familiarity of the situation, scaffolding certain domain-specific knowledge is necessary to assist students with incorporating evidence in modeling tasks.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

A mechanistic model for bromodeoxyuridine dilution naturally explains labelling data of self-renewing T cell populations

PubMed Central

Ganusov, Vitaly V.; De Boer, Rob J.

2013-01-01

Bromodeoxyuridine (BrdU) is widely used in immunology to detect cell division, and several mathematical models have been proposed to estimate proliferation and death rates of lymphocytes from BrdU labelling and de-labelling curves. One problem in interpreting BrdU data is explaining the de-labelling curves. Because shortly after label withdrawal, BrdU+ cells are expected to divide into BrdU+ daughter cells, one would expect a flat down-slope. As for many cell types, the fraction of BrdU+ cells decreases during de-labelling, previous mathematical models had to make debatable assumptions to be able to account for the data. We develop a mechanistic model tracking the number of divisions that each cell has undergone in the presence and absence of BrdU, and allow cells to accumulate and dilute their BrdU content. From the same mechanistic model, one can naturally derive expressions for the mean BrdU content (MBC) of all cells, or the MBC of the BrdU+ subset, which is related to the mean fluorescence intensity of BrdU that can be measured in experiments. The model is extended to include subpopulations with different rates of division and death (i.e. kinetic heterogeneity). We fit the extended model to previously published BrdU data from memory T lymphocytes in simian immunodeficiency virus-infected and uninfected macaques, and find that the model describes the data with at least the same quality as previous models. Because the same model predicts a modest decline in the MBC of BrdU+ cells, which is consistent with experimental observations, BrdU dilution seems a natural explanation for the observed down-slopes in self-renewing populations. PMID:23034350

Preparation and Analysis of Oligonucleotides Containing the C4′-Oxidized Abasic Site and Related Mechanistic Probes

PubMed Central

Kim, Jaeseung; Kreller, Cortney R.; Greenberg, Marc M.

2005-01-01

The C4′-oxidized abasic site (C4-AP) is produced by a variety of DNA damaging agents. This alkali labile lesion can exist in up to four diastereomeric cyclic forms, in addition to the acyclic keto-aldehyde. Synthetic oligonucleotides containing the lesion were prepared from a stable photochemical precursor. Chemical integrity of the lesion containing oligonucleotides was probed using phosphodiesterase lability. Analysis of the 3′,5′-phosphate diester of the monomeric lesion released from single diastereomers of photolabile precursors by 1H NMR indicates that isomerization of the hemiacetal and/or hemiketal is rapid. The syntheses and characterization of oligonucleotides containing configurationally stable analogues of C4-AP, which serve as mechanistic probes for deciphering the structural basis of the biochemical and biological effects of the C4′-oxidized abasic lesion, are also described. PMID:16277338

The value of mechanistic biophysical information for systems-level understanding of complex biological processes such as cytokinesis.

PubMed

Pollard, Thomas D

2014-12-02

This review illustrates the value of quantitative information including concentrations, kinetic constants and equilibrium constants in modeling and simulating complex biological processes. Although much has been learned about some biological systems without these parameter values, they greatly strengthen mechanistic accounts of dynamical systems. The analysis of muscle contraction is a classic example of the value of combining an inventory of the molecules, atomic structures of the molecules, kinetic constants for the reactions, reconstitutions with purified proteins and theoretical modeling to account for the contraction of whole muscles. A similar strategy is now being used to understand the mechanism of cytokinesis using fission yeast as a favorable model system. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A rat model system to study complex disease risks, fitness, aging, and longevity.

PubMed

Koch, Lauren Gerard; Britton, Steven L; Wisløff, Ulrik

2012-02-01

The association between low exercise capacity and all-cause morbidity and mortality is statistically strong yet mechanistically unresolved. By connecting clinical observation with a theoretical base, we developed a working hypothesis that variation in capacity for oxygen metabolism is the central mechanistic determinant between disease and health (aerobic hypothesis). As an unbiased test, we show that two-way artificial selective breeding of rats for low and high intrinsic endurance exercise capacity also produces rats that differ for numerous disease risks, including the metabolic syndrome, cardiovascular complications, premature aging, and reduced longevity. This contrasting animal model system may prove to be translationally superior relative to more widely used simplistic models for understanding geriatric biology and medicine. Copyright © 2012 Elsevier Inc. All rights reserved.

The Photo-3 model: A Python-based model for C3, C4, and CAM photosynthesis coupled with environmental conditions

NASA Astrophysics Data System (ADS)

Hartzell, S. R.; Bartlett, M. S., Jr.; Porporato, A. M.

2017-12-01

The ability to depict all three photosynthetic types (C3, C4, and CAM) has important implications for the study of both natural and agroecosystems. Currently no model exists which covers all types of photosynthesis in a consistent way and which can be fully integrated with environmental conditions. This is partially because, despite the fact that Crassulacean acid metabolism (CAM) photosynthesis is prevalent in many plants in arid and semi-arid ecosystems, where it may comprise nearly 50% of all plant biomass, CAM modelling remains understudied. The Photo-3 model takes advantage of recent advances in mechanistic modeling of CAM photosynthesis to provide a direct comparison of CAM functioning with C3 and C4 functioning under a wide range of soil and atmospheric conditions. The model is based on a core Farquhar photosynthetic model with additional functions to represent the spatial and temporal separations of carbon uptake and assimilation in the case of C4 and CAM photosynthesis. We have parameterized the model for one representative species of each photosynthetic type: Opuntia ficus-indica (CAM), Sorghum bicolor (C4), and Triticum aestivum (C3). Results agree well with experimental data on carbon assimilation and water use for the three species. Model runs using climate data from Temple, TX; Sicily, Italy; Zacatecas, Mexico; Pernambuco, Brazil and Adias Ababa, Ethiopia illustrate the high water use efficiency of CAM plants and its cumulative effects on long-term productivity in water-limited environments. The Photo-3 model, which is written in Python, will be made publicly available on GitHub and its outputs may be coupled to existing models of plant growth and phenology. The model may be used to evaluate potential productivity and water use for C3, C4, and CAM plants, and to devise optimal strategies for cropping systems and irrigation in water-limited environments.

Estimating Cumulative Traffic Loads, Final Report for Phase 1

DOT National Transportation Integrated Search

2000-07-01

The knowledge of traffic loads is a prerequisite for the pavement analysis process, especially for the development of load-related distress prediction models. Furthermore, the emerging mechanistically based pavement performance models and pavement de...

Iowa calibration of MEPDG performance prediction models.

DOT National Transportation Integrated Search

2013-06-01

This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

How to use mechanistic effect models in environmental risk assessment of pesticides: Case studies and recommendations from the SETAC workshop MODELINK.

PubMed

Hommen, Udo; Forbes, Valery; Grimm, Volker; Preuss, Thomas G; Thorbek, Pernille; Ducrot, Virginie

2016-01-01

Mechanistic effect models (MEMs) are useful tools for ecological risk assessment of chemicals to complement experimentation. However, currently no recommendations exist for how to use them in risk assessments. Therefore, the Society of Environmental Toxicology and Chemistry (SETAC) MODELINK workshop aimed at providing guidance for when and how to apply MEMs in regulatory risk assessments. The workshop focused on risk assessment of plant protection products under Regulation (EC) No 1107/2009 using MEMs at the organism and population levels. Realistic applications of MEMs were demonstrated in 6 case studies covering assessments for plants, invertebrates, and vertebrates in aquatic and terrestrial habitats. From the case studies and their evaluation, 12 recommendations on the future use of MEMs were formulated, addressing the issues of how to translate specific protection goals into workable questions, how to select species and scenarios to be modeled, and where and how to fit MEMs into current and future risk assessment schemes. The most important recommendations are that protection goals should be made more quantitative; the species to be modeled must be vulnerable not only regarding toxic effects but also regarding their life history and dispersal traits; the models should be as realistic as possible for a specific risk assessment question, and the level of conservatism required for a specific risk assessment should be reached by designing appropriately conservative environmental and exposure scenarios; scenarios should include different regions of the European Union (EU) and different crops; in the long run, generic MEMs covering relevant species based on representative scenarios should be developed, which will require EU-level joint initiatives of all stakeholders involved. The main conclusion from the MODELINK workshop is that the considerable effort required for making MEMs an integral part of environmental risk assessment of pesticides is worthwhile, because it will make risk assessments not only more ecologically relevant and less uncertain but also more comprehensive, coherent, and cost effective. © 2015 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of SETAC.

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

Modeling Environment for Total Risk-2E

EPA Science Inventory

MENTOR-2E uses an integrated, mechanistically consistent source-to-dose-to-response modeling framework to quantify inhalation exposure and doses resulting from emergency events. It is an implementation of the MENTOR system that is focused towards modeling of the impacts of rele...

A model-specific role of microRNA-223 as a mediator of kidney injury during experimental sepsis.

PubMed

Colbert, James F; Ford, Joshay A; Haeger, Sarah M; Yang, Yimu; Dailey, Kyrie L; Allison, Kristen C; Neudecker, Viola; Evans, Christopher M; Richardson, Vanessa L; Brodsky, Kelley S; Faubel, Sarah; Eltzschig, Holger K; Schmidt, Eric P; Ginde, Adit A

2017-08-01

Sepsis outcomes are heavily dependent on the development of septic organ injury, but no interventions exist to interrupt or reverse this process. microRNA-223 (miR-223) is known to be involved in both inflammatory gene regulation and host-pathogen interactions key to the pathogenesis of sepsis. The goal of this study was to determine the role of miR-223 as a mediator of septic kidney injury. Using miR-223 knockout mice and multiple models of experimental sepsis, we found that miR-223 differentially influences acute kidney injury (AKI) based on the model used. In the absence of miR-223, mice demonstrated exaggerated AKI in sterile models of sepsis (LPS injection) and attenuated AKI in a live-infection model of sepsis (cecal ligation and puncture). We demonstrated that miR-223 expression is induced in kidney homogenate after cecal ligation and puncture, but not after LPS or fecal slurry injection. We investigated additional potential mechanistic explanations including differences in peritoneal bacterial clearance and host stool virulence. Our findings highlight the complex role of miR-223 in the pathogenesis of septic kidney injury, as well as the importance of differences in experimental sepsis models and their consequent translational applicability. Copyright © 2017 the American Physiological Society.

Physiological constraints on organismal response to global warming: Mechanistic insights from clinally varying populations and implications for assessing endangerment.

PubMed

Bernardo, Joseph; Spotila, James R

2006-03-22

Recent syntheses indicate that global warming affects diverse biological processes, but also highlight the potential for some species to adapt behaviourally or evolutionarily to rapid climate change. Far less attention has addressed the alternative, that organisms lacking this ability may face extinction, a fate projected to befall one-quarter of global biodiversity. This conclusion is controversial, in part because there exist few mechanistic studies that show how climate change could precipitate extinction. We provide a concrete, mechanistic example of warming as a stressor of organisms that are closely adapted to cool climates from a comparative analysis of organismal tolerance among clinally varying populations along a natural thermal gradient. We found that two montane salamanders exhibit significant metabolic depression at temperatures within the natural thermal range experienced by low and middle elevation populations. Moreover, the magnitude of depression was inversely related to native elevation, suggesting that low elevation populations are already living near the limit of their physiological tolerances. If this finding generally applies to other montane specialists, the prognosis for biodiversity loss in typically diverse montane systems is sobering. We propose that indices of warming-induced stress tolerance may provide a critical new tool for quantitative assessments of endangerment due to anthropogenic climate change across diverse species.

Data-Driven Mechanistic Modeling of Influence of Microstructure on High-Cycle Fatigue Life of Nickel Titanium

NASA Astrophysics Data System (ADS)

Kafka, Orion L.; Yu, Cheng; Shakoor, Modesar; Liu, Zeliang; Wagner, Gregory J.; Liu, Wing Kam

2018-04-01

A data-driven mechanistic modeling technique is applied to a system representative of a broken-up inclusion ("stringer") within drawn nickel-titanium wire or tube, e.g., as used for arterial stents. The approach uses a decomposition of the problem into a training stage and a prediction stage. It is applied to compute the fatigue crack incubation life of a microstructure of interest under high-cycle fatigue. A parametric study of a matrix-inclusion-void microstructure is conducted. The results indicate that, within the range studied, a larger void between halves of the inclusion increases fatigue life, while larger inclusion diameter reduces fatigue life.

Mechanistic systems modeling to guide drug discovery and development

PubMed Central

Schmidt, Brian J.; Papin, Jason A.; Musante, Cynthia J.

2013-01-01

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. PMID:22999913

Mechanistic systems modeling to guide drug discovery and development.

PubMed

Schmidt, Brian J; Papin, Jason A; Musante, Cynthia J

2013-02-01

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Adrenomedullin and Pregnancy: Perspectives from Animal Models to Humans

PubMed Central

Lenhart, Patricia M.; Caron, Kathleen M.

2012-01-01

A healthy pregnancy requires strict coordination of genetic, physiologic, and environmental factors. The relatively common incidence of infertility and pregnancy complications has resulted in increased interest in understanding the mechanisms that underlie normal versus abnormal pregnancy. The peptide hormone adrenomedullin has recently been the focus of some exciting breakthroughs in the pregnancy field. Supported by mechanistic studies in genetic animal models, there continues to be a growing body of evidence demonstrating the importance of adrenomedullin protein levels in a variety of human pregnancy complications. With more extensive mechanistic studies and improved consistency in clinical measurements of adrenomedullin, there is great potential for the development of adrenomedullin as a clinically-relevant biomarker in pregnancy and pregnancy complications. PMID:22425034

Ground-Based Gas-Liquid Flow Research in Microgravity Conditions: State of Knowledge

NASA Technical Reports Server (NTRS)

McQuillen, J.; Colin, C.; Fabre, J.

1999-01-01

During the last decade, ground-based microgravity facilities have been utilized in order to obtain predictions for spacecraft system designers and further the fundamental understanding of two-phase flow. Although flow regime, pressure drop and heat transfer coefficient data has been obtained for straight tubes and a limited number of fittings, measurements of the void fraction, film thickness, wall shear stress, local velocity and void information are also required in order to develop general mechanistic models that can be utilized to ascertain the effects of fluid properties, tube geometry and acceleration levels. A review of this research is presented and includes both empirical data and mechanistic models of the flow behavior.

Structure-activity relationships and prediction of the phototoxicity and phototoxic potential of new drugs.

PubMed

Barratt, Martin D

2004-11-01

Relationships between the structure and properties of chemicals can be programmed into knowledge-based systems such as DEREK for Windows (DEREK is an acronym for "Deductive Estimation of Risk from Existing Knowledge"). The DEREK for Windows computer system contains a subset of over 60 rules describing chemical substructures (toxophores) responsible for skin sensitisation. As part of the European Phototox Project, the rule base was supplemented by a number of rules for the prospective identification of photoallergens, either by extension of the scope of existing rules or by the generation of new rules where a sound mechanistic rationale for the biological activity could be established. The scope of the rules for photoallergenicity was then further refined by assessment against a list of chemicals identified as photosensitisers by the Centro de Farmacovigilancia de la Comunidad Valenciana, Valencia, Spain. This paper contains an analysis of the mechanistic bases of activity for eight important groups of photoallergens and phototoxins, together with rules for the prospective identification of the photobiological activity of new or untested chemicals belonging to those classes. The mechanism of action of one additional chemical, nitrofurantoin, is well established; however, it was deemed inappropriate to write a rule on the basis of a single chemical structure.

A Parsimonious Model of the Rabbit Action Potential Elucidates the Minimal Physiological Requirements for Alternans and Spiral Wave Breakup

PubMed Central

2016-01-01

Elucidating the underlying mechanisms of fatal cardiac arrhythmias requires a tight integration of electrophysiological experiments, models, and theory. Existing models of transmembrane action potential (AP) are complex (resulting in over parameterization) and varied (leading to dissimilar predictions). Thus, simpler models are needed to elucidate the “minimal physiological requirements” to reproduce significant observable phenomena using as few parameters as possible. Moreover, models have been derived from experimental studies from a variety of species under a range of environmental conditions (for example, all existing rabbit AP models incorporate a formulation of the rapid sodium current, INa, based on 30 year old data from chick embryo cell aggregates). Here we develop a simple “parsimonious” rabbit AP model that is mathematically identifiable (i.e., not over parameterized) by combining a novel Hodgkin-Huxley formulation of INa with a phenomenological model of repolarization similar to the voltage dependent, time-independent rectifying outward potassium current (IK). The model was calibrated using the following experimental data sets measured from the same species (rabbit) under physiological conditions: dynamic current-voltage (I-V) relationships during the AP upstroke; rapid recovery of AP excitability during the relative refractory period; and steady-state INa inactivation via voltage clamp. Simulations reproduced several important “emergent” phenomena including cellular alternans at rates > 250 bpm as observed in rabbit myocytes, reentrant spiral waves as observed on the surface of the rabbit heart, and spiral wave breakup. Model variants were studied which elucidated the minimal requirements for alternans and spiral wave break up, namely the kinetics of INa inactivation and the non-linear rectification of IK.The simplicity of the model, and the fact that its parameters have physiological meaning, make it ideal for engendering generalizable mechanistic insight and should provide a solid “building-block” to generate more detailed ionic models to represent complex rabbit electrophysiology. PMID:27749895

A Parsimonious Model of the Rabbit Action Potential Elucidates the Minimal Physiological Requirements for Alternans and Spiral Wave Breakup.

PubMed

Gray, Richard A; Pathmanathan, Pras

2016-10-01

Elucidating the underlying mechanisms of fatal cardiac arrhythmias requires a tight integration of electrophysiological experiments, models, and theory. Existing models of transmembrane action potential (AP) are complex (resulting in over parameterization) and varied (leading to dissimilar predictions). Thus, simpler models are needed to elucidate the "minimal physiological requirements" to reproduce significant observable phenomena using as few parameters as possible. Moreover, models have been derived from experimental studies from a variety of species under a range of environmental conditions (for example, all existing rabbit AP models incorporate a formulation of the rapid sodium current, INa, based on 30 year old data from chick embryo cell aggregates). Here we develop a simple "parsimonious" rabbit AP model that is mathematically identifiable (i.e., not over parameterized) by combining a novel Hodgkin-Huxley formulation of INa with a phenomenological model of repolarization similar to the voltage dependent, time-independent rectifying outward potassium current (IK). The model was calibrated using the following experimental data sets measured from the same species (rabbit) under physiological conditions: dynamic current-voltage (I-V) relationships during the AP upstroke; rapid recovery of AP excitability during the relative refractory period; and steady-state INa inactivation via voltage clamp. Simulations reproduced several important "emergent" phenomena including cellular alternans at rates > 250 bpm as observed in rabbit myocytes, reentrant spiral waves as observed on the surface of the rabbit heart, and spiral wave breakup. Model variants were studied which elucidated the minimal requirements for alternans and spiral wave break up, namely the kinetics of INa inactivation and the non-linear rectification of IK.The simplicity of the model, and the fact that its parameters have physiological meaning, make it ideal for engendering generalizable mechanistic insight and should provide a solid "building-block" to generate more detailed ionic models to represent complex rabbit electrophysiology.

Chemistry meets biology in colitis-associated carcinogenesis

PubMed Central

Mangerich, Aswin; Dedon, Peter C.; Fox, James G.; Tannenbaum, Steven R.; Wogan, Gerald N.

2015-01-01

The intestine comprises an exceptional venue for a dynamic and complex interplay of numerous chemical and biological processes. Here, multiple chemical and biological systems, including the intestinal tissue itself, its associated immune system, the gut microbiota, xenobiotics, and metabolites meet and interact to form a sophisticated and tightly regulated state of tissue homoeostasis. Disturbance of this homeostasis can cause inflammatory bowel disease (IBD) – a chronic disease of multifactorial etiology that is strongly associated with increased risk for cancer development. This review addresses recent developments in research into chemical and biological mechanisms underlying the etiology of inflammation-induced colon cancer. Beginning with a general overview of reactive chemical species generated during colonic inflammation, the mechanistic interplay between chemical and biological mediators of inflammation, the role of genetic toxicology and microbial pathogenesis in disease development are discussed. When possible, we systematically compare evidence from studies utilizing human IBD patients with experimental investigations in mice. The comparison reveals that many strong pathological and mechanistic correlates exist between mouse models of colitis-associated cancer, and the clinically relevant situation in humans. We also summarize several emerging issues in the field, such as the carcinogenic potential of novel inflammation-related DNA adducts and genotoxic microbial factors, the systemic dimension of inflammation-induced genotoxicity, and the complex role of genome maintenance mechanisms during these processes. Taken together, current evidence points to the induction of genetic and epigenetic alterations by chemical and biological inflammatory stimuli ultimately leading to cancer formation. PMID:23926919

Cerebrovascular Hemodynamics in Women.

PubMed

Duque, Cristina; Feske, Steven K; Sorond, Farzaneh A

2017-12-01

Sex and gender, as biological and social factors, significantly influence health outcomes. Among the biological factors, sex differences in vascular physiology may be one specific mechanism contributing to the observed differences in clinical presentation, response to treatment, and clinical outcomes in several vascular disorders. This review focuses on the cerebrovascular bed and summarizes the existing literature on sex differences in cerebrovascular hemodynamics to highlight the knowledge deficit that exists in this domain. The available evidence is used to generate mechanistically plausible and testable hypotheses to underscore the unmet need in understanding sex-specific mechanisms as targets for more effective therapeutic and preventive strategies. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

PubMed Central

Bouhaddou, Mehdi; Koch, Rick J.; DiStefano, Matthew S.; Tan, Annie L.; Mertz, Alex E.

2018-01-01

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy. PMID:29579036

Development of a Physiologically Based Pharmacokinetic Model for Sinogliatin, a First-in-Class Glucokinase Activator, by Integrating Allometric Scaling, In Vitro to In Vivo Exploration and Steady-State Concentration-Mean Residence Time Methods: Mechanistic Understanding of its Pharmacokinetics.

PubMed

Song, Ling; Zhang, Yi; Jiang, Ji; Ren, Shuang; Chen, Li; Liu, Dongyang; Chen, Xijing; Hu, Pei

2018-04-06

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model for sinogliatin (HMS-5552, dorzagliatin) by integrating allometric scaling (AS), in vitro to in vivo exploration (IVIVE), and steady-state concentration-mean residence time (C ss -MRT) methods and to provide mechanistic insight into its pharmacokinetic properties in humans. Human major pharmacokinetic parameters were analyzed using AS, IVIVE, and C ss -MRT methods with available preclinical in vitro and in vivo data to understand sinogliatin drug metabolism and pharmacokinetic (DMPK) characteristics and underlying mechanisms. On this basis, an initial mechanistic PBPK model of sinogliatin was developed. The initial PBPK model was verified using observed data from a single ascending dose (SAD) study and further optimized with various strategies. The final model was validated by simulating sinogliatin pharmacokinetics under a fed condition. The validated model was applied to support a clinical drug-drug interaction (DDI) study design and to evaluate the effects of intrinsic (hepatic cirrhosis, genetic) factors on drug exposure. The two-species scaling method using rat and dog data (TS- rat,dog ) was the best AS method in predicting human systemic clearance in the central compartment (CL). The IVIVE method confirmed that sinogliatin was predominantly metabolized by cytochrome P450 (CYP) 3A4. The C ss -MRT method suggested dog pharmacokinetic profiles were more similar to human pharmacokinetic profiles. The estimated CL using the AS and IVIVE approaches was within 1.5-fold of that observed. The C ss -MRT method in dogs also provided acceptable prediction of human pharmacokinetic characteristics. For the PBPK approach, the 90% confidence intervals (CIs) of the simulated maximum concentration (C max ), CL, and area under the plasma concentration-time curve (AUC) of sinogliatin were within those observed and the 90% CI of simulated time to C max (t max ) was closed to that observed for a dose range of 5-50 mg in the SAD study. The final PBPK model was validated by simulating sinogliatin pharmacokinetics with food. The 90% CIs of the simulated C max , CL, and AUC values for sinogliatin were within those observed and the 90% CI of the simulated t max was partially within that observed for the dose range of 25-200 mg in the multiple ascending dose (MAD) study. This PBPK model selected a final clinical DDI study design with itraconazole from four potential designs and also evaluated the effects of intrinsic (hepatic cirrhosis, genetic) factors on drug exposure. Sinogliatin pharmacokinetic properties were mechanistically understood by integrating all four methods and a mechanistic PBPK model was successfully developed and validated using clinical data. This PBPK model was applied to support the development of sinogliatin.

Calculations on the Back of an Envelope Model: Applying Seasonal Fecundity Models to Species’ Range Limits

EPA Science Inventory

Most predictions of the effect of climate change on species’ ranges are based on correlations between climate and current species’ distributions. These so-called envelope models may be a good first approximation, but we need demographically mechanistic models to incorporate the ...

The History and Generality of AQUATOX, a Robust Mechanistic Model

EPA Science Inventory

In 1987, the U.S. Environmental Protection Agency sponsored a workshop in Baltimore on modeling the fate and effects of toxic organics. The specifications for the AQUATOX model came out of this workshop, and the first paper on the modeling concept was published soon after. Since ...

MATHEMATICAL MODEL OF STERIODOGENESIS TO PREDICT DYNAMIC RESPONSE TO ENDOCRINE DISRUPTORS

EPA Science Inventory

WE ARE DEVELOPING A MECHANISTIC MATHEMATICAL MODEL OF THE INTRATESTICULAR AND INTRAOVARIAN METABOLIC NETWORK THAT MEDIATES STEROID SYNTHESIS, AND THE KINETICS FOR ENZYME INHIBITION BY EDCs TO PREDICT THE TIME AND DOSE-RESPONSE.

Body size mediated coexistence of consumers competing for resources in space

USGS Publications Warehouse

Basset, A.; Angelis, D.L.

2007-01-01

Body size is a major phenotypic trait of individuals that commonly differentiates co-occurring species. We analyzed inter-specific competitive interactions between a large consumer and smaller competitors, whose energetics, selection and giving-up behaviour on identical resource patches scaled with individual body size. The aim was to investigate whether pure metabolic constraints on patch behaviour of vagile species can determine coexistence conditions consistent with existing theoretical and experimental evidence. We used an individual-based spatially explicit simulation model at a spatial scale defined by the home range of the large consumer, which was assumed to be parthenogenic and semelparous. Under exploitative conditions, competitive coexistence occurred in a range of body size ratios between 2 and 10. Asymmetrical competition and the mechanism underlying asymmetry, determined by the scaling of energetics and patch behaviour with consumer body size, were the proximate determinant of inter-specific coexistence. The small consumer exploited patches more efficiently, but searched for profitable patches less effectively than the larger competitor. Therefore, body-size related constraints induced niche partitioning, allowing competitive coexistence within a set of conditions where the large consumer maintained control over the small consumer and resource dynamics. The model summarises and extends the existing evidence of species coexistence on a limiting resource, and provides a mechanistic explanation for decoding the size-abundance distribution patterns commonly observed at guild and community levels. ?? Oikos.

Monte Carlo modeling of atomic oxygen attack of polymers with protective coatings on LDEF

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Degroh, Kim K.; Sechkar, Edward A.

1992-01-01

Characterization of the behavior of atomic oxygen interaction with materials on the Long Duration Exposure Facility (LDEF) will assist in understanding the mechanisms involved, and will lead to improved reliability in predicting in-space durability of materials based on ground laboratory testing. A computational simulation of atomic oxygen interaction with protected polymers was developed using Monte Carlo techniques. Through the use of assumed mechanistic behavior of atomic oxygen and results of both ground laboratory and LDEF data, a predictive Monte Carlo model was developed which simulates the oxidation processes that occur on polymers with applied protective coatings that have defects. The use of high atomic oxygen fluence-directed ram LDEF results has enabled mechanistic implications to be made by adjusting Monte Carlo modeling assumptions to match observed results based on scanning electron microscopy. Modeling assumptions, implications, and predictions are presented, along with comparison of observed ground laboratory and LDEF results.

The DART dispersion analysis research tool: A mechanistic model for predicting fission-product-induced swelling of aluminum dispersion fuels. User`s guide for mainframe, workstation, and personal computer applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rest, J.

1995-08-01

This report describes the primary physical models that form the basis of the DART mechanistic computer model for calculating fission-product-induced swelling of aluminum dispersion fuels; the calculated results are compared with test data. In addition, DART calculates irradiation-induced changes in the thermal conductivity of the dispersion fuel, as well as fuel restructuring due to aluminum fuel reaction, amorphization, and recrystallization. Input instructions for execution on mainframe, workstation, and personal computers are provided, as is a description of DART output. The theory of fission gas behavior and its effect on fuel swelling is discussed. The behavior of these fission products inmore » both crystalline and amorphous fuel and in the presence of irradiation-induced recrystallization and crystalline-to-amorphous-phase change phenomena is presented, as are models for these irradiation-induced processes.« less

Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women

PubMed Central

Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2015-01-01

Background Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Objectives Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Methods Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007–2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. Results CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman’s r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen’s kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. Conclusions The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements. Citation Nøst TH, Breivik K, Wania F, Rylander C, Odland JØ, Sandanger TM. 2016. Estimating time-varying PCB exposures using person-specific predictions to supplement measured values: a comparison of observed and predicted values in two cohorts of Norwegian women. Environ Health Perspect 124:299–305; http://dx.doi.org/10.1289/ehp.1409191 PMID:26186800

Mechanistic modeling of developmental defects through computational embryology (WC10th)

EPA Science Inventory

Abstract: An important consideration for 3Rs is to identify developmental hazards utilizing mechanism-based in vitro assays (e.g., ToxCast) and in silico predictive models. Steady progress has been made with agent-based models that recapitulate morphogenetic drivers for angiogen...

Microbial Kinetic Model for the Degradation of Poorly Soluble Organic Materials

EPA Science Inventory

A novel mechanistic model is presented that describes the aerobic biodegradation kinetics of soybean biodiesel and petroleum diesel in batch experiments. The model was built on the assumptions that biodegradation takes place in the aqueous phase according to Monod kinetics, and ...

ASSESSING MULTIMEDIA/MULTIPATHWAY EXPOSURE TO ARSENIC USING A MECHANISTIC SOURCE-TO-DOSE MODELING FRAMEWORK

EPA Science Inventory

A series of case studies is presented focusing on multimedia/multipathway population exposures to arsenic, employing the Population Based Modeling approach of the MENTOR (Modeling Environment for Total Risks) framework. This framework considers currently five exposure routes: i...

Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics.

PubMed

Kirk, Devin; Jones, Natalie; Peacock, Stephanie; Phillips, Jessica; Molnár, Péter K; Krkošek, Martin; Luijckx, Pepijn

2018-02-01

The complexity of host-parasite interactions makes it difficult to predict how host-parasite systems will respond to climate change. In particular, host and parasite traits such as survival and virulence may have distinct temperature dependencies that must be integrated into models of disease dynamics. Using experimental data from Daphnia magna and a microsporidian parasite, we fitted a mechanistic model of the within-host parasite population dynamics. Model parameters comprising host aging and mortality, as well as parasite growth, virulence, and equilibrium abundance, were specified by relationships arising from the metabolic theory of ecology. The model effectively predicts host survival, parasite growth, and the cost of infection across temperature while using less than half the parameters compared to modeling temperatures discretely. Our results serve as a proof of concept that linking simple metabolic models with a mechanistic host-parasite framework can be used to predict temperature responses of parasite population dynamics at the within-host level.

Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics

PubMed Central

Jones, Natalie; Peacock, Stephanie; Phillips, Jessica; Molnár, Péter K.; Krkošek, Martin; Luijckx, Pepijn

2018-01-01

The complexity of host–parasite interactions makes it difficult to predict how host–parasite systems will respond to climate change. In particular, host and parasite traits such as survival and virulence may have distinct temperature dependencies that must be integrated into models of disease dynamics. Using experimental data from Daphnia magna and a microsporidian parasite, we fitted a mechanistic model of the within-host parasite population dynamics. Model parameters comprising host aging and mortality, as well as parasite growth, virulence, and equilibrium abundance, were specified by relationships arising from the metabolic theory of ecology. The model effectively predicts host survival, parasite growth, and the cost of infection across temperature while using less than half the parameters compared to modeling temperatures discretely. Our results serve as a proof of concept that linking simple metabolic models with a mechanistic host–parasite framework can be used to predict temperature responses of parasite population dynamics at the within-host level. PMID:29415043

Connecting Urbanization to Precipitation: the case of Mexico City

NASA Astrophysics Data System (ADS)

Georgescu, Matei

2017-04-01

Considerable evidence exists illustrating the influence of urban environments on precipitation. We revisit this theme of significant interest to a broad spectrum of disciplines ranging from urban planning to engineering to urban numerical modeling and climate, by detailing the simulated effect of Mexico City's built environment on regional precipitation. Utilizing the Weather Research and Forecasting (WRF) system to determine spatiotemporal changes in near-surface air temperature, precipitation, and boundary layer conditions induced by the modern-day urban landscape relative to presettlement conditions, I mechanistically link the built environment-induced increase in air temperature to simulated increases in rainfall during the evening hours. This simulated increase in precipitation is in agreement with historical observations documenting observed rainfall increase. These results have important implications for understanding the meteorological conditions leading to the widespread and recurrent urban flooding that continues to plague the Mexico City Metropolitan Area.

A quality by design approach to scale-up of high-shear wet granulation process.

PubMed

Pandey, Preetanshu; Badawy, Sherif

2016-01-01

High-shear wet granulation is a complex process that in turn makes scale-up a challenging task. Scale-up of high-shear wet granulation process has been studied extensively in the past with various different methodologies being proposed in the literature. This review article discusses existing scale-up principles and categorizes the various approaches into two main scale-up strategies - parameter-based and attribute-based. With the advent of quality by design (QbD) principle in drug product development process, an increased emphasis toward the latter approach may be needed to ensure product robustness. In practice, a combination of both scale-up strategies is often utilized. In a QbD paradigm, there is also a need for an increased fundamental and mechanistic understanding of the process. This can be achieved either by increased experimentation that comes at higher costs, or by using modeling techniques, that are also discussed as part of this review.

Cyclic electron flow is redox-controlled but independent of state transition.

PubMed

Takahashi, Hiroko; Clowez, Sophie; Wollman, Francis-André; Vallon, Olivier; Rappaport, Fabrice

2013-01-01

Photosynthesis is the biological process that feeds the biosphere with reduced carbon. The assimilation of CO2 requires the fine tuning of two co-existing functional modes: linear electron flow, which provides NADPH and ATP, and cyclic electron flow, which only sustains ATP synthesis. Although the importance of this fine tuning is appreciated, its mechanism remains equivocal. Here we show that cyclic electron flow as well as formation of supercomplexes, thought to contribute to the enhancement of cyclic electron flow, are promoted in reducing conditions with no correlation with the reorganization of the thylakoid membranes associated with the migration of antenna proteins towards Photosystems I or II, a process known as state transition. We show that cyclic electron flow is tuned by the redox power and this provides a mechanistic model applying to the entire green lineage including the vast majority of the cases in which state transition only involves a moderate fraction of the antenna.

Stomatal regulation based on competition for water, stochastic rainfall, and xylem hydraulic vulnerability - a new theoretical model

NASA Astrophysics Data System (ADS)

Lu, Y.; Duursma, R.; Farrior, C.; Medlyn, B. E.

2016-12-01

Stomata control the exchange of soil water for atmospheric CO2, which is one of the most important resource trade-offs for plants. This trade-off has been studied a lot but not in the context of competition. Based on the theory of evolutionarily stable strategy, we search for the uninvadable (or the ESS) response of stomatal conductance to soil water content under stochastic rainfall, with which the dominant plant population should never be invaded by any rare mutants in the water competition due to a higher fitness. In this study, we define the fitness as the difference between the long-term average photosynthetic carbon gain and a carbon cost of stomatal opening. This cost has traditionally been considered an unknown constant. Here we extend this framework by assuming it as the energy required for xylem embolism refilling. With regard to the refilling process, we explore 2 questions 1) to what extent the embolized xylem vessels can be repaired via refilling; and 2) whether this refilling is immediate or has a time delay following the formation of xylem embolism. We compare various assumptions in a total of 5 scenarios and find that the ESS exists only if the xylem damage can be repaired completely. Then, with this ESS, we estimate annual vegetation photosynthesis and water consumption and compare them with empirical results. In conclusion, this study provides a different insight from the existing empirical and mechanistic models as well as the theoretical models based on the optimization theory. In addition, as the model result is a simple quantitative relation between stomatal conductance and soil water content, it can be easily incorporated into other vegetation function models.

Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bond-Lamberty, Benjamin; Epron, Daniel; Harden, Jennifer W.

2016-06-27

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of "Decomposition Functional Types" (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelsmore » to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present and discuss an example clustering analysis to show how model-produced annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from already-existing PFTs. A similar analysis, incorporating observational data, could form a basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with high-performance computing; rigorous testing of analytical results; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR at large scales.« less

Estimating heterotrophic respiration at large scales: Challenges, approaches, and next steps

DOE PAGES

Bond-Lamberty, Ben; Epron, Daniel; Harden, Jennifer; ...

2016-06-27

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of Decomposition Functional Types (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelersmore » and experimentalists to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present an example clustering analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from (but complementary to) already-existing PFTs. A similar analysis incorporating observational data could form the basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with rigorous testing of analytical results; using point measurements and realistic forcing variables to constrain process-based models; and planning by the global modeling community for decoupling decomposition from fixed site data. Lastly, these are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR.« less

Human Health Effects of Trichloroethylene: Key Findings and Scientific Issues

PubMed Central

Jinot, Jennifer; Scott, Cheryl Siegel; Makris, Susan L.; Cooper, Glinda S.; Dzubow, Rebecca C.; Bale, Ambuja S.; Evans, Marina V.; Guyton, Kathryn Z.; Keshava, Nagalakshmi; Lipscomb, John C.; Barone, Stanley; Fox, John F.; Gwinn, Maureen R.; Schaum, John; Caldwell, Jane C.

2012-01-01

Background: In support of the Integrated Risk Information System (IRIS), the U.S. Environmental Protection Agency (EPA) completed a toxicological review of trichloroethylene (TCE) in September 2011, which was the result of an effort spanning > 20 years. Objectives: We summarized the key findings and scientific issues regarding the human health effects of TCE in the U.S. EPA’s toxicological review. Methods: In this assessment we synthesized and characterized thousands of epidemiologic, experimental animal, and mechanistic studies, and addressed several key scientific issues through modeling of TCE toxicokinetics, meta-analyses of epidemiologic studies, and analyses of mechanistic data. Discussion: Toxicokinetic modeling aided in characterizing the toxicological role of the complex metabolism and multiple metabolites of TCE. Meta-analyses of the epidemiologic data strongly supported the conclusions that TCE causes kidney cancer in humans and that TCE may also cause liver cancer and non-Hodgkin lymphoma. Mechanistic analyses support a key role for mutagenicity in TCE-induced kidney carcinogenicity. Recent evidence from studies in both humans and experimental animals point to the involvement of TCE exposure in autoimmune disease and hypersensitivity. Recent avian and in vitro mechanistic studies provided biological plausibility that TCE plays a role in developmental cardiac toxicity, the subject of substantial debate due to mixed results from epidemiologic and rodent studies. Conclusions: TCE is carcinogenic to humans by all routes of exposure and poses a potential human health hazard for noncancer toxicity to the central nervous system, kidney, liver, immune system, male reproductive system, and the developing embryo/fetus. PMID:23249866

Inferring the Impact of Regulatory Mechanisms that Underpin CD8+ T Cell Control of B16 Tumor Growth In vivo Using Mechanistic Models and Simulation.

PubMed

Klinke, David J; Wang, Qing

2016-01-01

A major barrier for broadening the efficacy of immunotherapies for cancer is identifying key mechanisms that limit the efficacy of tumor infiltrating lymphocytes. Yet, identifying these mechanisms using human samples and mouse models for cancer remains a challenge. While interactions between cancer and the immune system are dynamic and non-linear, identifying the relative roles that biological components play in regulating anti-tumor immunity commonly relies on human intuition alone, which can be limited by cognitive biases. To assist natural intuition, modeling and simulation play an emerging role in identifying therapeutic mechanisms. To illustrate the approach, we developed a multi-scale mechanistic model to describe the control of tumor growth by a primary response of CD8+ T cells against defined tumor antigens using the B16 C57Bl/6 mouse model for malignant melanoma. The mechanistic model was calibrated to data obtained following adenovirus-based immunization and validated to data obtained following adoptive transfer of transgenic CD8+ T cells. More importantly, we use simulation to test whether the postulated network topology, that is the modeled biological components and their associated interactions, is sufficient to capture the observed anti-tumor immune response. Given the available data, the simulation results also provided a statistical basis for quantifying the relative importance of different mechanisms that underpin CD8+ T cell control of B16F10 growth. By identifying conditions where the postulated network topology is incomplete, we illustrate how this approach can be used as part of an iterative design-build-test cycle to expand the predictive power of the model.

Simple yet effective: Historical proximity variables improve the species distribution models for invasive giant hogweed (Heracleum mantegazzianum s.l.) in Poland.

PubMed

Mędrzycki, Piotr; Jarzyna, Ingeborga; Obidziński, Artur; Tokarska-Guzik, Barbara; Sotek, Zofia; Pabjanek, Piotr; Pytlarczyk, Adam; Sachajdakiewicz, Izabela

2017-01-01

Species distribution models are scarcely applicable to invasive species because of their breaking of the models' assumptions. So far, few mechanistic, semi-mechanistic or statistical solutions like dispersal constraints or propagule limitation have been applied. We evaluated a novel quasi-semi-mechanistic approach for regional scale models, using historical proximity variables (HPV) representing a state of the population in a given moment in the past. Our aim was to test the effects of addition of HPV sets of different minimal recentness, information capacity and the total number of variables on the quality of the species distribution model for Heracleum mantegazzianum on 116000 km2 in Poland. As environmental predictors, we used fragments of 103 1×1 km, world- wide, free-access rasters from WorldGrids.org. Single and ensemble models were computed using BIOMOD2 package 3.1.47 working in R environment 3.1.0. The addition of HPV improved the quality of single and ensemble models from poor to good and excellent. The quality was the highest for the variants with HPVs based on the distance from the most recent past occurrences. It was mostly affected by the algorithm type, but all HPV traits (minimal recentness, information capacity, model type or the number of the time periods) were significantly important determinants. The addition of HPVs improved the quality of current projections, raising the occurrence probability in regions where the species had occurred before. We conclude that HPV addition enables semi-realistic estimation of the rate of spread and can be applied to the short-term forecasting of invasive or declining species, which also break equal-dispersal probability assumptions.

Mechanistic elucidation of the antitumor properties of withaferin A in breast cancer

PubMed Central

Nagalingam, Arumugam; Kuppusamy, Panjamurthy; Singh, Shivendra V.; Sharma, Dipali; Saxena, Neeraj K.

2014-01-01

Withaferin A (WFA) is a steroidal lactone with antitumor effects manifested at multiple levels which are mechanistically obscure. Using a phospho-kinase screening array, we discovered that WFA activated phosphorylation of the S6 kinase RSK in breast cancer cells. Pursuing this observation, we defined activation of ERK-RSK and Elk1-CHOP kinase pathways in upregulating transcription of the death receptor DR5. Through this route, WFA acted as an effective DR5 activator capable of potentiating the biological effects of celecoxib, etoposide and TRAIL. Accordingly, WFA treatment inhibited breast tumor formation in xenograft and MMTV-neu mouse models in a manner associated with activation of the ERK/RSK axis, DR5 upregulation and elevated nuclear accumulation of Elk1 and CHOP. Together, our results offer mechanistic insight into how WFA inhibits breast tumor growth. PMID:24732433

An ecophysiological perspective on likely giant panda habitat responses to climate change.

PubMed

Zhang, Yuke; Mathewson, Paul D; Zhang, Qiongyue; Porter, Warren P; Ran, Jianghong

2018-04-01

Threatened and endangered species are more vulnerable to climate change due to small population and specific geographical distribution. Therefore, identifying and incorporating the biological processes underlying a species' adaptation to its environment are important for determining whether they can persist in situ. Correlative models are widely used to predict species' distribution changes, but generally fail to capture the buffering capacity of organisms. Giant pandas (Ailuropoda melanoleuca) live in topographically complex mountains and are known to avoid heat stress. Although many studies have found that climate change will lead to severe habitat loss and threaten previous conservation efforts, the mechanisms underlying panda's responses to climate change have not been explored. Here, we present a case study in Daxiangling Mountains, one of the six Mountain Systems that giant panda distributes. We used a mechanistic model, Niche Mapper, to explore what are likely panda habitat response to climate change taking physiological, behavioral and ecological responses into account, through which we map panda's climatic suitable activity area (SAA) for the first time. We combined SAA with bamboo forest distribution to yield highly suitable habitat (HSH) and seasonal suitable habitat (SSH), and their temporal dynamics under climate change were predicted. In general, SAA in the hottest month (July) would reduce 11.7%-52.2% by 2070, which is more moderate than predicted bamboo habitat loss (45.6%-86.9%). Limited by the availability of bamboo and forest, panda's suitable habitat loss increases, and only 15.5%-68.8% of current HSH would remain in 2070. Our method of mechanistic modeling can help to distinguish whether habitat loss is caused by thermal environmental deterioration or food loss under climate change. Furthermore, mechanistic models can produce robust predictions by incorporating ecophysiological feedbacks and minimizing extrapolation into novel environments. We suggest that a mechanistic approach should be incorporated into distribution predictions and conservation planning. © 2017 John Wiley & Sons Ltd.

Serial Femtosecond Crystallography and Ultrafast Absorption Spectroscopy of the Photoswitchable Fluorescent Protein IrisFP.

PubMed

Colletier, Jacques-Philippe; Sliwa, Michel; Gallat, François-Xavier; Sugahara, Michihiro; Guillon, Virginia; Schirò, Giorgio; Coquelle, Nicolas; Woodhouse, Joyce; Roux, Laure; Gotthard, Guillaume; Royant, Antoine; Uriarte, Lucas Martinez; Ruckebusch, Cyril; Joti, Yasumasa; Byrdin, Martin; Mizohata, Eiichi; Nango, Eriko; Tanaka, Tomoyuki; Tono, Kensuke; Yabashi, Makina; Adam, Virgile; Cammarata, Marco; Schlichting, Ilme; Bourgeois, Dominique; Weik, Martin

2016-03-03

Reversibly photoswitchable fluorescent proteins find growing applications in cell biology, yet mechanistic details, in particular on the ultrafast photochemical time scale, remain unknown. We employed time-resolved pump-probe absorption spectroscopy on the reversibly photoswitchable fluorescent protein IrisFP in solution to study photoswitching from the nonfluorescent (off) to the fluorescent (on) state. Evidence is provided for the existence of several intermediate states on the pico- and microsecond time scales that are attributed to chromophore isomerization and proton transfer, respectively. Kinetic modeling favors a sequential mechanism with the existence of two excited state intermediates with lifetimes of 2 and 15 ps, the second of which controls the photoswitching quantum yield. In order to support that IrisFP is suited for time-resolved experiments aiming at a structural characterization of these ps intermediates, we used serial femtosecond crystallography at an X-ray free electron laser and solved the structure of IrisFP in its on state. Sample consumption was minimized by embedding crystals in mineral grease, in which they remain photoswitchable. Our spectroscopic and structural results pave the way for time-resolved serial femtosecond crystallography aiming at characterizing the structure of ultrafast intermediates in reversibly photoswitchable fluorescent proteins.

Mutual Dependence Between Sedimentary Organic Carbon and Infaunal Macrobenthos Resolved by Mechanistic Modeling

NASA Astrophysics Data System (ADS)

Zhang, Wenyan; Wirtz, Kai

2017-10-01

The mutual dependence between sedimentary total organic carbon (TOC) and infaunal macrobenthos is here quantified by a mechanistic model. The model describes (i) the vertical distribution of infaunal macrobenthic biomass resulting from a trade-off between nutritional benefit (quantity and quality of TOC) and the costs of burial (respiration) and mortality, and (ii) the variable vertical distribution of TOC being in turn shaped by bioturbation of local macrobenthos. In contrast to conventional approaches, our model emphasizes variations of bioturbation both spatially and temporally depending on local food resources and macrobenthic biomass. Our implementation of the dynamic interaction between TOC and infaunal macrobenthos is able to capture a temporal benthic response to both depositional and erosional environments and provides improved estimates of the material exchange flux at the sediment-water interface. Applications to literature data for the North Sea demonstrate the robustness and accuracy of the model and its potential as an analysis tool for the status of TOC and macrobenthos in marine sediments. Results indicate that the vertical distribution of infaunal biomass is shaped by both the quantity and the quality of OC, while the community structure is determined only by the quality of OC. Bioturbation intensity may differ by 1 order of magnitude over different seasons owing to variations in the OC input, resulting in a significant modulation on the distribution of OC. Our relatively simple implementation may further improve models of early diagenesis and marine food web dynamics by mechanistically connecting the vertical distribution of both TOC and macrobenthic biomass.

Semi-mechanistic autoinduction model of midazolam in critically ill patients: population pharmacokinetic analysis.

PubMed

Aoyama, T; Hirata, K; Yamamoto, Y; Yokota, H; Hayashi, H; Aoyama, Y; Matsumoto, Y

2016-08-01

Midazolam (MDZ) is commonly used for sedating critically ill patients. The daily dose required for adequate sedation increases in increments over 100 h after administration. The objectives of this study were to characterize the MDZ pharmacokinetics in critically ill patients and to describe the phenomenon of increasing daily dose by means of population pharmacokinetic analysis. Data were obtained from 30 patients treated in an intensive care unit. The patients received MDZ intravenously as a combination of bolus and continuous infusion. Serum MDZ concentration was assayed by high-performance liquid chromatography. Population pharmacokinetic analysis was performed using the NONMEM software package. The alteration of clearance unexplained by demographic factors and clinical laboratory data was described as an autoinduction of MDZ clearance using a semi-mechanistic pharmacokinetic-enzyme turnover model. The final population pharmacokinetic model was a one-compartment model estimated by incorporating a semi-mechanistic pharmacokinetic-enzyme turnover model for clearance, taking autoinduction into account. A significant covariate for MDZ clearance was total bilirubin. An increase in total bilirubin indicated a reduction in MDZ clearance. From simulation using the population pharmacokinetic parameters obtained in this study, MDZ clearance increased 2·3 times compared with pre-induced clearance 100 h after the start of 12·5 mg/h continuous infusion. Autoinduction and total bilirubin were significant predictors of the clearance of MDZ in this population. Step-by-step dosage adjustment using this population pharmacokinetic model may be useful for establishing a MDZ dosage regimen in critically ill patients. © 2016 John Wiley & Sons Ltd.

Mechanistic modeling of microbial interactions at pore to profile scale resolve methane emission dynamics from permafrost soil

NASA Astrophysics Data System (ADS)

Ebrahimi, Ali; Or, Dani

2017-05-01

The sensitivity of polar regions to raising global temperatures is reflected in rapidly changing hydrological processes associated with pronounced seasonal thawing of permafrost soil and increased biological activity. Of particular concern is the potential release of large amounts of soil carbon and stimulation of other soil-borne greenhouse gas emissions such as methane. Soil methanotrophic and methanogenic microbial communities rapidly adjust their activity and spatial organization in response to permafrost thawing and other environmental factors. Soil structural elements such as aggregates and layering affect oxygen and nutrient diffusion processes thereby contributing to methanogenic activity within temporal anoxic niches (hot spots). We developed a mechanistic individual-based model to quantify microbial activity dynamics in soil pore networks considering transport processes and enzymatic activity associated with methane production in soil. The model was upscaled from single aggregates to the soil profile where freezing/thawing provides macroscopic boundary conditions for microbial activity at different soil depths. The model distinguishes microbial activity in aerate bulk soil from aggregates (or submerged profile) for resolving methane production and oxidation rates. Methane transport pathways by diffusion and ebullition of bubbles vary with hydration dynamics. The model links seasonal thermal and hydrologic dynamics with evolution of microbial community composition and function affecting net methane emissions in good agreement with experimental data. The mechanistic model enables systematic evaluation of key controlling factors in thawing permafrost and microbial response (e.g., nutrient availability and enzyme activity) on long-term methane emissions and carbon decomposition rates in the rapidly changing polar regions.

A mathematical model of marine bacteriophage evolution.

PubMed

Pagliarini, Silvia; Korobeinikov, Andrei

2018-03-01

To explore how particularities of a host cell-virus system, and in particular host cell replication, affect viral evolution, in this paper we formulate a mathematical model of marine bacteriophage evolution. The intrinsic simplicity of real-life phage-bacteria systems, and in particular aquatic systems, for which the assumption of homogeneous mixing is well justified, allows for a reasonably simple model. The model constructed in this paper is based upon the Beretta-Kuang model of bacteria-phage interaction in an aquatic environment (Beretta & Kuang 1998 Math. Biosci. 149 , 57-76. (doi:10.1016/S0025-5564(97)10015-3)). Compared to the original Beretta-Kuang model, the model assumes the existence of a multitude of viral variants which correspond to continuously distributed phenotypes. It is noteworthy that the model is mechanistic (at least as far as the Beretta-Kuang model is mechanistic). Moreover, this model does not include any explicit law or mechanism of evolution; instead it is assumed, in agreement with the principles of Darwinian evolution, that evolution in this system can occur as a result of random mutations and natural selection. Simulations with a simplistic linear fitness landscape (which is chosen for the convenience of demonstration only and is not related to any real-life system) show that a pulse-type travelling wave moving towards increasing Darwinian fitness appears in the phenotype space. This implies that the overall fitness of a viral quasi-species steadily increases with time. That is, the simulations demonstrate that for an uneven fitness landscape random mutations combined with a mechanism of natural selection (for this particular system this is given by the conspecific competition for the resource) lead to the Darwinian evolution. It is noteworthy that in this system the speed of propagation of this wave (and hence the rate of evolution) is not constant but varies, depending on the current viral fitness and the abundance of susceptible bacteria. A specific feature of the original Beretta-Kuang model is that this model exhibits a supercritical Hopf bifurcation, leading to the loss of stability and the rise of self-sustained oscillations in the system. This phenomenon corresponds to the paradox of enrichment in the system. It is remarkable that under the conditions that ensure the bifurcation in the Beretta-Kuang model, the viral evolution model formulated in this paper also exhibits a rise in self-sustained oscillations of the abundance of all interacting populations. The propagation of the travelling wave, however, remains stable under these conditions. The only visible impact of the oscillations on viral evolution is a lower speed of the evolution.

A functional-structural kiwifruit vine model integrating architecture, carbon dynamics and effects of the environment.

PubMed

Cieslak, Mikolaj; Seleznyova, Alla N; Hanan, Jim

2011-04-01

Functional-structural modelling can be used to increase our understanding of how different aspects of plant structure and function interact, identify knowledge gaps and guide priorities for future experimentation. By integrating existing knowledge of the different aspects of the kiwifruit (Actinidia deliciosa) vine's architecture and physiology, our aim is to develop conceptual and mathematical hypotheses on several of the vine's features: (a) plasticity of the vine's architecture; (b) effects of organ position within the canopy on its size; (c) effects of environment and horticultural management on shoot growth, light distribution and organ size; and (d) role of carbon reserves in early shoot growth. Using the L-system modelling platform, a functional-structural plant model of a kiwifruit vine was created that integrates architectural development, mechanistic modelling of carbon transport and allocation, and environmental and management effects on vine and fruit growth. The branching pattern was captured at the individual shoot level by modelling axillary shoot development using a discrete-time Markov chain. An existing carbon transport resistance model was extended to account for several source/sink components of individual plant elements. A quasi-Monte Carlo path-tracing algorithm was used to estimate the absorbed irradiance of each leaf. Several simulations were performed to illustrate the model's potential to reproduce the major features of the vine's behaviour. The model simulated vine growth responses that were qualitatively similar to those observed in experiments, including the plastic response of shoot growth to local carbon supply, the branching patterns of two Actinidia species, the effect of carbon limitation and topological distance on fruit size and the complex behaviour of sink competition for carbon. The model is able to reproduce differences in vine and fruit growth arising from various experimental treatments. This implies it will be a valuable tool for refining our understanding of kiwifruit growth and for identifying strategies to improve production.

Informing Mechanistic Toxicology with Computational Molecular Models

EPA Science Inventory

Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo effo...

Modeling Environment for Total Risk-4M

EPA Science Inventory

MENTOR-4M uses an integrated, mechanistically consistent, source-to-dose modeling framework to quantify simultaneous exposures and doses of individuals and populations to multiple contaminants. It is an implementation of the MENTOR system for exposures to Multiple contaminants fr...

MODELING CRYPTOSPORIDIUM PARVUM OOCYST INACTIVATION AND BROMATE IN A FLOW-THROUGH OZONE CONTACTOR TREATING NATURAL WATER

EPA Science Inventory

A reactive transport model was developed to simultaneously predict Cryptosporidium parvum oocyst inactivation and bromate formation during ozonation of natural water. A mechanistic model previously established to predict bromate formation in organic-free synthetic waters w...

INTEGRATED CHEMICAL INFORMATION TECHNOLOGIES ...

EPA Pesticide Factsheets

A central regulatory mandate of the Environmental Protection Agency, spanning many Program Offices and issues, is to assess the potential health and environmental risks of large numbers of chemicals released into the environment, often in the absence of relevant test data. Models for predicting potential adverse effects of chemicals based primarily on chemical structure play a central role in prioritization and screening strategies yet are highly dependent and conditional upon the data used for developing such models. Hence, limits on data quantity, quality, and availability are considered by many to be the largest hurdles to improving prediction models in diverse areas of toxicology. Generation of new toxicity data for additional chemicals and endpoints, development of new high-throughput, mechanistically relevant bioassays, and increased generation of genomics and proteomics data that can clarify relevant mechanisms will all play important roles in improving future SAR prediction models. The potential for much greater immediate gains, across large domains of chemical and toxicity space, comes from maximizing the ability to mine and model useful information from existing toxicity data, data that represent huge past investment in research and testing expenditures. In addition, the ability to place newer “omics” data, data that potentially span many possible domains of toxicological effects, in the broader context of historical data is the means for opti

There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task

PubMed Central

Miconi, Thomas; Groomes, Laura; Kreiman, Gabriel

2016-01-01

When searching for an object in a scene, how does the brain decide where to look next? Visual search theories suggest the existence of a global “priority map” that integrates bottom-up visual information with top-down, target-specific signals. We propose a mechanistic model of visual search that is consistent with recent neurophysiological evidence, can localize targets in cluttered images, and predicts single-trial behavior in a search task. This model posits that a high-level retinotopic area selective for shape features receives global, target-specific modulation and implements local normalization through divisive inhibition. The normalization step is critical to prevent highly salient bottom-up features from monopolizing attention. The resulting activity pattern constitues a priority map that tracks the correlation between local input and target features. The maximum of this priority map is selected as the locus of attention. The visual input is then spatially enhanced around the selected location, allowing object-selective visual areas to determine whether the target is present at this location. This model can localize objects both in array images and when objects are pasted in natural scenes. The model can also predict single-trial human fixations, including those in error and target-absent trials, in a search task involving complex objects. PMID:26092221

Engineering properties of resin modified pavement (RMP) for mechanistic design

NASA Astrophysics Data System (ADS)

Anderton, Gary Lee

1997-11-01

The research study described in this report focuses on determining the engineering properties of the resin modified pavement (RMP) material relating to pavement performance, and then developing a rational mechanistic design procedure to replace the current empirical design procedure. A detailed description of RMP is provided, including a review of the available literature on this relatively new pavement technology. Field evaluations of four existing and two new RMP project sites were made to assess critical failure modes and to obtain pavement samples for subsequent laboratory testing. Various engineering properties of laboratory-produced and field-recovered samples of RMP were measured and analyzed. The engineering properties evaluated included those relating to the material's stiffness, strength, thermal properties, and traffic-related properties. Comparisons of these data to typical values for asphalt concrete and portland cement concrete were made to relate the physical nature of RMP to more common pavement surfacing materials. A mechanistic design procedure was developed to determine appropriate thickness profiles of RMP, using stiffness and fatigue properties determined by this study. The design procedure is based on the U.S. Army Corps of Engineers layered elastic method for airfield flexible pavements. The WESPAVE computer program was used to demonstrate the new design procedure for a hypothetical airfield apron design. The results of the study indicated that RMP is a relatively stiff, viscoelastic pavement surfacing material with many of its strength and stiffness properties falling between those of typical asphalt concrete and portland cement concrete. The RMP's thermal and traffic-related properties indicated favorable field performance. The layered elastic design approach appeared to be a reasonable and practical method for RMP mechanistic pavement design, and this design procedure was recommended for future use and development.

USE OF OUTPUT FROM THE NEW ENGLAND SPARROW MODEL TO ESTIMATE CONCENTRATIONS OF TOTAL NITROGEN IN ESTUARIES

EPA Science Inventory

The USGS=s SPARROW Model is a statistical model with mechanistic features that has been used to calculate annual nutrient fluxes in nontidal streams nationally on the basis of nitrogen sources, landscape characteristics, and stream properties. This model has been useful for asses...

Biomarker Discovery and Mechanistic Studies of Prostate Cancer using Targeted Proteomic Approaches

DTIC Science & Technology

2012-07-01

basigin in Drosophila ) tightly regulates cytoskeleton rearrangement in Drosophila melanogaster [23]. Based on the present results and the existing...from OligoEngine according to the manufac- turer’s instruction. Plasmids were amplified in DH5a cell and confirmed by sequencing . Subconfluent cell...electrophoresis and the results are shown in Figure 1 (Panel C). The RT-PCR products were cloned and subjected to DNA sequenc - ing. The sequencing

Machinery of protein folding and unfolding.

PubMed

Zhang, Xiaodong; Beuron, Fabienne; Freemont, Paul S

2002-04-01

During the past two years, a large amount of biochemical, biophysical and low- to high-resolution structural data have provided mechanistic insights into the machinery of protein folding and unfolding. It has emerged that dual functionality in terms of folding and unfolding might exist for some systems. The majority of folding/unfolding machines adopt oligomeric ring structures in a cooperative fashion and utilise the conformational changes induced by ATP binding/hydrolysis for their specific functions.

Quantification of Cation Sorption to Engineered Barrier Materials Under Extreme Conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Powell, Brian; Schlautman, Mark; Rao, Linfeng

The objective of this research is to examine mechanisms and thermodynamics of actinide sorption to engineered barrier materials (iron (oxyhydr)oxides and bentonite clay) for nuclear waste repositories under high temperature and high ionic strength conditions using a suite of macroscopic and microscopic techniques which will be coupled with interfacial reaction models. Gaining a mechanistic understanding of interfacial processes governing the sorption/sequestration of actinides at mineral-water interfaces is fundamental for the accurate prediction of actinide behavior in waste repositories. Although macroscale sorption data and various spectroscopic techniques have provided valuable information regarding speciation of actinides at solid-water interfaces, significant knowledge gapsmore » still exist with respect to sorption mechanisms and the ability to quantify sorption, particularly at high temperatures and ionic strengths. This objective is addressed through three major tasks: (1) influence of oxidation state on actinide sorption to iron oxides and clay minerals at elevated temperatures and ionic strengths; (2) calorimetric titrations of actinide-mineral suspensions; (3) evaluation of bentonite performance under repository conditions. The results of the work will include a qualitative conceptual model and a quantitative thermodynamic speciation model describing actinide partitioning to minerals and sediments, which is based upon a mechanistic understanding of specific sorption processes as determined from both micro-scale and macroscale experimental techniques. The speciation model will be a thermodynamic aqueous and surface complexation model of actinide interactions with mineral surfaces that is self-consistent with macroscopic batch sorption data, calorimetric and potentiometric titrations, X-ray absorption Spectroscopy (XAS, mainly Extended X-ray Absorption Fine Structure (EXAFS)), and electron microscopy analyses. The novelty of the proposed work lies largely in the unique system conditions which will be examined (i.e. elevated temperature and ionic strength) and the manner in which the surface complexation model will be developed in terms of specific surface species identified using XAS. These experiments will thus provide a fundamental understanding of the chemical and physical processes occurring at the solid-solution interface under expected repository conditions. Additionally, the focus on thermodynamic treatment of actinide ion interactions with minerals as proposed will provide information on the driving forces involved and contribute to the overall understanding of the high affinity many actinide ions have for oxide surfaces. The utility of this model will be demonstrated in this work through a series of advective and diffusive flow experiments.« less

Development of Mechanistic Reasoning and Multilevel Explanations of Ecology in Third Grade Using Agent-Based Models

ERIC Educational Resources Information Center

Dickes, Amanda Catherine; Sengupta, Pratim; Farris, Amy Voss; Satabdi, Basu

2016-01-01

In this paper, we present a third-grade ecology learning environment that integrates two forms of modeling--embodied modeling and agent-based modeling (ABMs)--through the generation of mathematical representations that are common to both forms of modeling. The term "agent" in the context of ABMs indicates individual computational objects…

Application of PBPK modelling in drug discovery and development at Pfizer.

PubMed

Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B

2012-01-01

Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

A method to identify and analyze biological programs through automated reasoning

PubMed Central

Yordanov, Boyan; Dunn, Sara-Jane; Kugler, Hillel; Smith, Austin; Martello, Graziano; Emmott, Stephen

2016-01-01

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function. PMID:27668090

Simple yet effective: Historical proximity variables improve the species distribution models for invasive giant hogweed (Heracleum mantegazzianum s.l.) in Poland

PubMed Central

Jarzyna, Ingeborga; Obidziński, Artur; Tokarska-Guzik, Barbara; Sotek, Zofia; Pabjanek, Piotr; Pytlarczyk, Adam; Sachajdakiewicz, Izabela

2017-01-01

Species distribution models are scarcely applicable to invasive species because of their breaking of the models’ assumptions. So far, few mechanistic, semi-mechanistic or statistical solutions like dispersal constraints or propagule limitation have been applied. We evaluated a novel quasi-semi-mechanistic approach for regional scale models, using historical proximity variables (HPV) representing a state of the population in a given moment in the past. Our aim was to test the effects of addition of HPV sets of different minimal recentness, information capacity and the total number of variables on the quality of the species distribution model for Heracleum mantegazzianum on 116000 km2 in Poland. As environmental predictors, we used fragments of 103 1×1 km, world- wide, free-access rasters from WorldGrids.org. Single and ensemble models were computed using BIOMOD2 package 3.1.47 working in R environment 3.1.0. The addition of HPV improved the quality of single and ensemble models from poor to good and excellent. The quality was the highest for the variants with HPVs based on the distance from the most recent past occurrences. It was mostly affected by the algorithm type, but all HPV traits (minimal recentness, information capacity, model type or the number of the time periods) were significantly important determinants. The addition of HPVs improved the quality of current projections, raising the occurrence probability in regions where the species had occurred before. We conclude that HPV addition enables semi-realistic estimation of the rate of spread and can be applied to the short-term forecasting of invasive or declining species, which also break equal-dispersal probability assumptions. PMID:28926580

Observational and Modeling Studies of Radiative, Chemical, and Dynamical Interactions in the Earth''s Atmosphere

NASA Technical Reports Server (NTRS)

Salby, Murry

1998-01-01

A 3-dimensional model was developed to support mechanistic studies. The model solves the global primitive equations in isentropic coordinates, which directly characterize diabatic processes forcing the Brewer-Dobson circulation of the middle atmosphere. It's numerical formulation is based on Hough harmonics, which partition horizontal motion into its rotational and divergent components. These computational features, along with others, enable 3D integrations to be performed practically on RISC computer architecture, on which they can be iterated to support mechanistic studies. The model conserves potential vorticity quite accurately under adiabatic conditions. Forced by observed tropospheric structure, in which integrations are anchored, the model generates a diabatic circulation that is consistent with satellite observations of tracer behavior and diabatic cooling rates. The model includes a basic but fairly complete treatment of gas-phase photochemistry that represents some 20 chemical species and 50 governing reactions with diurnally-varying shortwave absorption. The model thus provides a reliable framework to study transport and underlying diabatic processes, which can then be compared against chemical and dynamical structure observed and in GCM integrations. Integrations with the Langley GCM were performed to diagnose feedback between simulated convection and the tropical circulation. These were studied in relation to tropospheric properties controlling moisture convergence and environmental conditions supporting deep convection, for comparison against mechanistic integrations of wave CISK that successfully reproduce the Madden-Julian Oscillation (MJO) of the tropical circulation. These comparisons were aimed at identifying and ultimately improving aspects of the convective simulation, with the objective of recovering a successful simulation of the MJO in the Langley GCM, behavior that should be important to budgets of upper-tropospheric water vapor and chemical species.

Simulating the effects of climate change on the distribution of an invasive plant, using a high resolution, local scale, mechanistic approach: challenges and insights.

PubMed

Fennell, Mark; Murphy, James E; Gallagher, Tommy; Osborne, Bruce

2013-04-01

The growing economic and ecological damage associated with biological invasions, which will likely be exacerbated by climate change, necessitates improved projections of invasive spread. Generally, potential changes in species distribution are investigated using climate envelope models; however, the reliability of such models has been questioned and they are not suitable for use at local scales. At this scale, mechanistic models are more appropriate. This paper discusses some key requirements for mechanistic models and utilises a newly developed model (PSS[gt]) that incorporates the influence of habitat type and related features (e.g., roads and rivers), as well as demographic processes and propagule dispersal dynamics, to model climate induced changes in the distribution of an invasive plant (Gunnera tinctoria) at a local scale. A new methodology is introduced, dynamic baseline benchmarking, which distinguishes climate-induced alterations in species distributions from other potential drivers of change. Using this approach, it was concluded that climate change, based on IPCC and C4i projections, has the potential to increase the spread-rate and intensity of G. tinctoria invasions. Increases in the number of individuals were primarily due to intensification of invasion in areas already invaded or in areas projected to be invaded in the dynamic baseline scenario. Temperature had the largest influence on changes in plant distributions. Water availability also had a large influence and introduced the most uncertainty in the projections. Additionally, due to the difficulties of parameterising models such as this, the process has been streamlined by utilising methods for estimating unknown variables and selecting only essential parameters. © 2012 Blackwell Publishing Ltd.

Body Fineness Ratio as a Predictor of Maximum Prolonged-Swimming Speed in Coral Reef Fishes

PubMed Central

Walker, Jeffrey A.; Alfaro, Michael E.; Noble, Mae M.; Fulton, Christopher J.

2013-01-01

The ability to sustain high swimming speeds is believed to be an important factor affecting resource acquisition in fishes. While we have gained insights into how fin morphology and motion influences swimming performance in coral reef fishes, the role of other traits, such as body shape, remains poorly understood. We explore the ability of two mechanistic models of the causal relationship between body fineness ratio and endurance swimming-performance to predict maximum prolonged-swimming speed (Umax) among 84 fish species from the Great Barrier Reef, Australia. A drag model, based on semi-empirical data on the drag of rigid, submerged bodies of revolution, was applied to species that employ pectoral-fin propulsion with a rigid body at U max. An alternative model, based on the results of computer simulations of optimal shape in self-propelled undulating bodies, was applied to the species that swim by body-caudal-fin propulsion at Umax. For pectoral-fin swimmers, Umax increased with fineness, and the rate of increase decreased with fineness, as predicted by the drag model. While the mechanistic and statistical models of the relationship between fineness and Umax were very similar, the mechanistic (and statistical) model explained only a small fraction of the variance in Umax. For body-caudal-fin swimmers, we found a non-linear relationship between fineness and Umax, which was largely negative over most of the range of fineness. This pattern fails to support either predictions from the computational models or standard functional interpretations of body shape variation in fishes. Our results suggest that the widespread hypothesis that a more optimal fineness increases endurance-swimming performance via reduced drag should be limited to fishes that swim with rigid bodies. PMID:24204575

(Q)SARs to predict environmental toxicities: current status and future needs.

PubMed

Cronin, Mark T D

2017-03-22

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

Left Ventricular Gene Expression Profile of Healthy and Cardiovascular Compromised Rat Models Used in Air Pollution Studies

EPA Science Inventory

The link between pollutant exposure and cardiovascular disease (CVD) has prompted mechanistic research with animal models of CVD. We hypothesized that the cardiac gene expression patterns of healthy and genetically compromised, CVD-prone rat models, with or without metabolic impa...

Dynamical properties of maps fitted to data in the noise-free limit

PubMed Central

Lindström, Torsten

2013-01-01

We argue that any attempt to classify dynamical properties from nonlinear finite time-series data requires a mechanistic model fitting the data better than piecewise linear models according to standard model selection criteria. Such a procedure seems necessary but still not sufficient. PMID:23768079

The mechanistic model, GoMDOM: Development , calibration and sensitivity analysis

EPA Science Inventory

This presentation will be in a series of Gulf Hypoxia modeling presentations which will be used to: 1) aid NOAA in informing scientific directions and funding decisions for their cooperators and 2) a Technical Review of all models will be provided to the Mississippi River Nutrie...

In silico models for development of insect repellents

USDA-ARS?s Scientific Manuscript database

In silico modeling a common term to describe computer-assisted molecular modeling has been used to make remarkable advances in mechanistic drug design and in the discovery of new potential bioactive chemical entities in recent years. The goal of this chapter will be to focus on new, next-generation ...

Fathead Minnow Steroidogenesis: In Silico Analyses Reveals Tradeoffs Between Nominal Target Efficacy and Robustness to Cross-talk

EPA Science Inventory

This paper presents the formulation and evaluation of a mechanistic mathematical model of fathead minnow ovarian steroidogenesis. The model presented in the present study was adpated from other models developed as part of an integrated, multi-disciplinary computational toxicolog...

A mathematical model of sentimental dynamics accounting for marital dissolution.

PubMed

Rey, José-Manuel

2010-03-31

Marital dissolution is ubiquitous in western societies. It poses major scientific and sociological problems both in theoretical and therapeutic terms. Scholars and therapists agree on the existence of a sort of second law of thermodynamics for sentimental relationships. Effort is required to sustain them. Love is not enough. Building on a simple version of the second law we use optimal control theory as a novel approach to model sentimental dynamics. Our analysis is consistent with sociological data. We show that, when both partners have similar emotional attributes, there is an optimal effort policy yielding a durable happy union. This policy is prey to structural destabilization resulting from a combination of two factors: there is an effort gap because the optimal policy always entails discomfort and there is a tendency to lower effort to non-sustaining levels due to the instability of the dynamics. These mathematical facts implied by the model unveil an underlying mechanism that may explain couple disruption in real scenarios. Within this framework the apparent paradox that a union consistently planned to last forever will probably break up is explained as a mechanistic consequence of the second law.

Evolution of optimal Hill coefficients in nonlinear public goods games.

PubMed

Archetti, Marco; Scheuring, István

2016-10-07

In evolutionary game theory, the effect of public goods like diffusible molecules has been modelled using linear, concave, sigmoid and step functions. The observation that biological systems are often sigmoid input-output functions, as described by the Hill equation, suggests that a sigmoid function is more realistic. The Michaelis-Menten model of enzyme kinetics, however, predicts a concave function, and while mechanistic explanations of sigmoid kinetics exist, we lack an adaptive explanation: what is the evolutionary advantage of a sigmoid benefit function? We analyse public goods games in which the shape of the benefit function can evolve, in order to determine the optimal and evolutionarily stable Hill coefficients. We find that, while the dynamics depends on whether output is controlled at the level of the individual or the population, intermediate or high Hill coefficients often evolve, leading to sigmoid input-output functions that for some parameters are so steep to resemble a step function (an on-off switch). Our results suggest that, even when the shape of the benefit function is unknown, biological public goods should be modelled using a sigmoid or step function rather than a linear or concave function. Copyright © 2016 Elsevier Ltd. All rights reserved.

What is orgasm? A model of sexual trance and climax via rhythmic entrainment

PubMed Central

Safron, Adam

2016-01-01

Orgasm is one of the most intense pleasures attainable to an organism, yet its underlying mechanisms remain poorly understood. On the basis of existing literatures, this article introduces a novel mechanistic model of sexual stimulation and orgasm. In doing so, it characterizes the neurophenomenology of sexual trance and climax, describes parallels in dynamics between orgasms and seizures, speculates on possible evolutionary origins of sex differences in orgasmic responding, and proposes avenues for future experimentation. Here, a model is introduced wherein sexual stimulation induces entrainment of coupling mechanical and neuronal oscillatory systems, thus creating synchronized functional networks within which multiple positive feedback processes intersect synergistically to contribute to sexual experience. These processes generate states of deepening sensory absorption and trance, potentially culminating in climax if critical thresholds are surpassed. The centrality of rhythmic stimulation (and its modulation by salience) for surpassing these thresholds suggests ways in which differential orgasmic responding between individuals—or with different partners—may serve as a mechanism for ensuring adaptive mate choice. Because the production of rhythmic stimulation combines honest indicators of fitness with cues relating to potential for investment, differential orgasmic response may serve to influence the probability of continued sexual encounters with specific mates. PMID:27799079

A Mathematical Model of Sentimental Dynamics Accounting for Marital Dissolution

PubMed Central

Rey, José-Manuel

2010-01-01

Background Marital dissolution is ubiquitous in western societies. It poses major scientific and sociological problems both in theoretical and therapeutic terms. Scholars and therapists agree on the existence of a sort of second law of thermodynamics for sentimental relationships. Effort is required to sustain them. Love is not enough. Methodology/Principal Findings Building on a simple version of the second law we use optimal control theory as a novel approach to model sentimental dynamics. Our analysis is consistent with sociological data. We show that, when both partners have similar emotional attributes, there is an optimal effort policy yielding a durable happy union. This policy is prey to structural destabilization resulting from a combination of two factors: there is an effort gap because the optimal policy always entails discomfort and there is a tendency to lower effort to non-sustaining levels due to the instability of the dynamics. Conclusions/Significance These mathematical facts implied by the model unveil an underlying mechanism that may explain couple disruption in real scenarios. Within this framework the apparent paradox that a union consistently planned to last forever will probably break up is explained as a mechanistic consequence of the second law. PMID:20360987

Some Surprising Findings on the Involvement of the Parietal Lobe in Human Memory

PubMed Central

Olson, Ingrid R.; Berryhill, Marian

2009-01-01

The posterior parietal lobe is known to play some role in a far-flung list of mental processes: linking vision to action (saccadic eye movements, reaching, grasping), attending to visual space, numerical calculation, and mental rotation. Here we review findings from humans and monkeys that illuminate an untraditional function of this region: memory. Our review draws on neuroimaging findings that have repeatedly identified parietal lobe activations associated with short-term or working memory and episodic memory. We also discuss recent neuropsychological findings showing that individuals with parietal lobe damage exhibit both working memory and long-term memory deficits. These deficits are not ubiquitous; they are only evident under certain retrieval demands. Our review elaborates on these findings and evaluates various theories about the mechanistic role of the posterior parietal lobe in memory. The available data point towards the conclusion that the posterior parietal lobe plays an important role in memory retrieval irrespective of elapsed time. The two models that are best supported by existing data are the Attention to Memory Model and the Subjective Memory Model. We conclude by formalizing several open questions that are intended to encourage future research. PMID:18848635

Public Databases Supporting Computational Toxicology

EPA Science Inventory

A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, resea...

Modeling Environment for Total Risk-1A

EPA Science Inventory

MENTOR-1A uses an integrated, mechanistically consistent source-to-dose modeling framework to quantify inhalation exposure and dose for individuals and/or populations due to co-occurring air pollutants. It uses the "One Atmosphere" concept to characterize simultaneous exposures t...

Verification of mechanistic-empirical design models for flexible pavements through accelerated pavement testing : technical summary.

DOT National Transportation Integrated Search

2014-08-01

Midwest States Accelerated Pavement Testing Pooled-Fund Program, financed by the : highway departments of Kansas, Iowa, and Missouri, has supported an accelerated : pavement testing (APT) project to validate several models incorporated in the NCHRP :...

Modeling Total Suspended Solids (TSS) Concentrations in Narragansett Bay.

EPA Science Inventory

This work covers mechanistic modeling of suspended particulates in estuarine systems with an application to Narragansett Bay, RI. Suspended particles directly affect water clarity and attenuate light in the water column. Water clarity affects both phytoplankton and submerged aqua...

Verification of mechanistic-empirical design models for flexible pavements through accelerated pavement testing.

DOT National Transportation Integrated Search

2014-08-01

The Midwest States Accelerated Pavement Testing Pooled Fund Program, financed by the highway : departments of Kansas, Iowa, and Missouri, has supported an accelerated pavement testing (APT) project to : validate several models incorporated in the NCH...

20180129 - Computational Embryology: Translational Tools for Modeling in vitro Data (Toxicology Forum)

EPA Science Inventory

Integrative models are needed to "decode the toxicological blueprint of active substances that interact with living systems" (Systems toxicology). Computational biology is uniquely positioned to capture this connectivity and help shift decision-making to mechanistic pre...

Perception of the Body in Space: Mechanisms

NASA Technical Reports Server (NTRS)

Young, Laurence R.

1991-01-01

The principal topic is the perception of body orientation and motion in space and the extent to which these perceptual abstraction can be related directly to the knowledge of sensory mechanisms, particularly for the vestibular apparatus. Spatial orientation is firmly based on the underlying sensory mechanisms and their central integration. For some of the simplest situations, like rotation about a vertical axis in darkness, the dynamic response of the semicircular canals furnishes almost enough information to explain the sensations of turning and stopping. For more complex conditions involving multiple sensory systems and possible conflicts among their messages, a mechanistic response requires significant speculative assumptions. The models that exist for multisensory spatial orientation are still largely of the non-rational parameter variety. They are capable of predicting relationships among input motions and output perceptions of motion, but they involve computational functions that do not now and perhaps never will have their counterpart in central nervous system machinery. The challenge continues to be in the iterative process of testing models by experiment, correcting them where necessary, and testing them again.

Environmental drivers defining linkages among life-history traits: mechanistic insights from a semiterrestrial amphipod subjected to macroscale gradients.

PubMed

Gómez, Julio; Barboza, Francisco R; Defeo, Omar

2013-10-01

Determining the existence of interconnected responses among life-history traits and identifying underlying environmental drivers are recognized as key goals for understanding the basis of phenotypic variability. We studied potentially interconnected responses among senescence, fecundity, embryos size, weight of brooding females, size at maturity and sex ratio in a semiterrestrial amphipod affected by macroscale gradients in beach morphodynamics and salinity. To this end, multiple modelling processes based on generalized additive mixed models were used to deal with the spatio-temporal structure of the data obtained at 10 beaches during 22 months. Salinity was the only nexus among life-history traits, suggesting that this physiological stressor influences the energy balance of organisms. Different salinity scenarios determined shifts in the weight of brooding females and size at maturity, having consequences in the number and size of embryos which in turn affected sex determination and sex ratio at the population level. Our work highlights the importance of analysing field data to find the variables and potential mechanisms that define concerted responses among traits, therefore defining life-history strategies.

Annual variation of gravity-wave activity at middle and high latitudes in a high-resolution GCM

NASA Astrophysics Data System (ADS)

Becker, E.

2017-12-01

A high-resolution version of the Kuehlungsborn Mechanistic general Circulation Model (KMCM) with resolved gravity waves (GWs) is employed to analyze the annual variation of GW activity in both hemispheres at middle and high latitudes. The geographical distributions of GW hotspots in the winter stratosphere are consistent with existing satellite data. Vertical profiles up to the lower thermosphere agree with ground-based measurements for both season. The model confirms the semi-annual variation of GW energy in the upper mesosphere that was found previously in radar-measurements in the northern hemisphere Furthermore, the GW potential energy per unit mass during winter shows two maxima, one around 50-70 km and one around 80-100 km. We interpret the upper maximum as a result of secondary GWs that are generated in the stratopause region due to the intermittent body forces of primary GWs. In a recent study we showed that these secondary GWs produce a significant eastward drag in the mesopause region during austral winter. This mechanism is found to be important in the northern winter as well.

The Role of Multiple Neuromodulators in Reinforcement Learning That Is Based on Competition between Eligibility Traces.

PubMed

Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z

2016-01-01

The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule). To address the first problem synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although, these were mere theoretical constructs, recent experiments have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward nucleus cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP) and one for long-term depression (LTD), each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although, the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different neuromodulators for expressing the LTP and LTD traces? Here we expand on our previous model to include several neuromodulators, and illustrate through various examples how different these contribute to learning reward-timing within a wide set of training paradigms and propose further roles that multiple neuromodulators can play in encoding additional information of the rewarding signal.

Understanding cancer development processes after HZE-particle exposure: roles of ROS, DNA damage repair and inflammation.

PubMed

Sridharan, D M; Asaithamby, A; Bailey, S M; Costes, S V; Doetsch, P W; Dynan, W S; Kronenberg, A; Rithidech, K N; Saha, J; Snijders, A M; Werner, E; Wiese, C; Cucinotta, F A; Pluth, J M

2015-01-01

During space travel astronauts are exposed to a variety of radiations, including galactic cosmic rays composed of high-energy protons and high-energy charged (HZE) nuclei, and solar particle events containing low- to medium-energy protons. Risks from these exposures include carcinogenesis, central nervous system damage and degenerative tissue effects. Currently, career radiation limits are based on estimates of fatal cancer risks calculated using a model that incorporates human epidemiological data from exposed populations, estimates of relative biological effectiveness and dose-response data from relevant mammalian experimental models. A major goal of space radiation risk assessment is to link mechanistic data from biological studies at NASA Space Radiation Laboratory and other particle accelerators with risk models. Early phenotypes of HZE exposure, such as the induction of reactive oxygen species, DNA damage signaling and inflammation, are sensitive to HZE damage complexity. This review summarizes our current understanding of critical areas within the DNA damage and oxidative stress arena and provides insight into their mechanistic interdependence and their usefulness in accurately modeling cancer and other risks in astronauts exposed to space radiation. Our ultimate goals are to examine potential links and crosstalk between early response modules activated by charged particle exposure, to identify critical areas that require further research and to use these data to reduced uncertainties in modeling cancer risk for astronauts. A clearer understanding of the links between early mechanistic aspects of high-LET response and later surrogate cancer end points could reveal key nodes that can be therapeutically targeted to mitigate the health effects from charged particle exposures.

In-air and pressurized water reactor environment fatigue experiments of 316 stainless steel to study the effect of environment on cyclic hardening

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mohanty, Subhasish; Soppet, William K.; Majumdar, Saurindranath

Argonne National Laboratory (ANL), under the sponsorship of Department of Energy’s Light Water Reactor Sustainability (LWRS) program, is trying to develop a mechanistic approach for more accurate life estimation of LWR components. In this context, ANL has conducted many fatigue experiments under different test and environment conditions on type 316 stainless steel (316SS) material which is widely used in the US reactors. Contrary to the conventional S~N curve based empirical fatigue life estimation approach, the aim of the present DOE sponsored work is to develop an understanding of the material ageing issues more mechanistically (e.g. time dependent hardening and softening)more » under different test and environmental conditions. Better mechanistic understanding will help develop computer-based advanced modeling tools to better extrapolate stress-strain evolution of reactor components under multi-axial stress states and hence help predict their fatigue life more accurately. In this paper (part-I) the fatigue experiments under different test and environment conditions and related stress-strain results for 316 SS are discussed. In a second paper (part-II) the related evolutionary cyclic plasticity material modeling techniques and results are discussed.« less

Comparison of mechanistic transport cycle models of ABC exporters.

PubMed

Szöllősi, Dániel; Rose-Sperling, Dania; Hellmich, Ute A; Stockner, Thomas

2018-04-01

ABC (ATP binding cassette) transporters, ubiquitous in all kingdoms of life, carry out essential substrate transport reactions across cell membranes. Their transmembrane domains bind and translocate substrates and are connected to a pair of nucleotide binding domains, which bind and hydrolyze ATP to energize import or export of substrates. Over four decades of investigations into ABC transporters have revealed numerous details from atomic-level structural insights to their functional and physiological roles. Despite all these advances, a comprehensive understanding of the mechanistic principles of ABC transporter function remains elusive. The human multidrug resistance transporter ABCB1, also referred to as P-glycoprotein (P-gp), is one of the most intensively studied ABC exporters. Using ABCB1 as the reference point, we aim to compare the dominating mechanistic models of substrate transport and ATP hydrolysis for ABC exporters and to highlight the experimental and computational evidence in their support. In particular, we point out in silico studies that enhance and complement available biochemical data. "This article is part of a Special Issue entitled: Beyond the Structure-Function Horizon of Membrane Proteins edited by Ute Hellmich, Rupak Doshi and Benjamin McIlwain." Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Work values among Lebanese workers.

PubMed

Sidani, Y M; Gardner, W L

2000-10-01

On the basis of a review of the existing literature, the authors tested 4 hypotheses to determine the applicability of work values in Arab societies to employees in Lebanese organizations. Only 1 hypothesis was supported: Organizational policies that ran counter to the worker's religious values had an adverse effect on job satisfaction. There was no support for the hypotheses (a) that workers' religiosity in inversely related to positive attitudes toward women's involvement at work, (b) that employee satisfaction is related to a mechanistic organizational design, or (c) that workers with an internal locus of control experience higher job satisfaction. The Lebanese workers, thus, did not appear to share some of the attributes claimed to exist in Arab societies.

Evidence for the timing of sea-level events during MIS 3

NASA Astrophysics Data System (ADS)

Siddall, M.

2005-12-01

Four large sea-level peaks of millennial-scale duration occur during MIS 3. In addition smaller peaks may exist close to the sensitivity of existing methods to derive sea level during these periods. Millennial-scale changes in temperature during MIS 3 are well documented across much of the planet and are linked in some unknown, yet fundamental way to changes in ice volume / sea level. It is therefore highly likely that the timing of the sea level events during MIS 3 will prove to be a `Rosetta Stone' for understanding millennial scale climate variability. I will review observational and mechanistic arguments for the variation of sea level on Antarctic, Greenland and absolute time scales.

The Evolution of Land Plants and the Silicate Weathering Feedback

NASA Astrophysics Data System (ADS)

Ibarra, D. E.; Caves Rugenstein, J. K.; Bachan, A.; Baresch, A.; Lau, K. V.; Thomas, D.; Lee, J. E.; Boyce, C. K.; Chamberlain, C. P.

2017-12-01

It has long been recognized that the advent of vascular plants in the Paleozoic must have changed silicate weathering and fundamentally altered the long-term carbon cycle. Efforts to quantify these effects have been formulated in carbon cycle models that are, in part, calibrated by weathering studies of modern plant communities. In models of the long-term carbon cycle, plants play a key role in controlling atmospheric CO2, particularly in the late Paleozoic. We test the impact of some established and recent theories regarding plant-enhanced weathering by coupling a one-dimensional vapor transport model to a reactive transport model of silicate weathering. In this coupled model, we evaluate consequences of plant evolutionary innovation that have not been mechanistically incorporated into most existing models: 1) the role of evolutionary shifts in plant transpiration in enhancing silicate weathering by increasing downwind transport and recycling of water vapor to continental interiors; 2) the importance of deeply-rooted plants and their associated microbial communities in increasing soil CO2 and weathering zone length scales; and, 3) the cumulative effect of these processes. Our modeling approach is framed by energy/supply constraints calibrated for minimally vegetated-, vascular plant forested-, and angiosperm-worlds. We find that the emergence of widespread transpiration and associated inland vapor recycling approximately doubles weathering solute concentrations when deep-rooted vascular plants (Devonian-Carboniferous) fully replace a minimally vegetated (pre-Devonian) world. The later evolution of angiosperms (Cretaceous and Cenozoic) and subsequent increase in transpiration fluxes increase weathering solute concentrations by approximately an additional 20%. Our estimates of the changes in weatherability caused by land plant evolution are of a similar magnitude, but explained with new process-based mechanisms, than those used in existing carbon cycle models. We suggest a feedback where the increase in solute concentrations is compensated by a decrease in runoff and temperature, permitting lower steady-state atmospheric pCO2. Consequently, plants have increased the strength of the climatic feedback on silicate weathering since the late Paleozoic.

Modeling tools for the assessment of microbiological risks during floods: a review

NASA Astrophysics Data System (ADS)

Collender, Philip; Yang, Wen; Stieglitz, Marc; Remais, Justin

2015-04-01

Floods are a major, recurring source of harm to global economies and public health. Projected increases in the frequency and intensity of heavy precipitation events under future climate change, coupled with continued urbanization in areas with high risk of floods, may exacerbate future impacts of flooding. Improved flood risk management is essential to support global development, poverty reduction and public health, and is likely to be a crucial aspect of climate change adaptation. Importantly, floods can facilitate the transmission of waterborne pathogens by changing social conditions (overcrowding among displaced populations, interruption of public health services), imposing physical challenges to infrastructure (sewerage overflow, reduced capacity to treat drinking water), and altering fate and transport of pathogens (transport into waterways from overland flow, resuspension of settled contaminants) during and after flood conditions. Hydrological and hydrodynamic models are capable of generating quantitative characterizations of microbiological risks associated with flooding, while accounting for these diverse and at times competing physical and biological processes. Despite a few applications of such models to the quantification of microbiological risks associated with floods, there exists limited guidance as to the relative capabilities, and limitations, of existing modeling platforms when used for this purpose. Here, we review 17 commonly used flood and water quality modeling tools that have demonstrated or implicit capabilities of mechanistically representing and quantifying microbial risk during flood conditions. We compare models with respect to their capabilities of generating outputs that describe physical and microbial conditions during floods, such as concentration or load of non-cohesive sediments or pathogens, and the dynamics of high flow conditions. Recommendations are presented for the application of specific modeling tools for assessing particular flood-related microbial risks, and model improvements are suggested that may better characterize key microbial risks during flood events. The state of current tools are assessed in the context of a changing climate where the frequency, intensity and duration of flooding are shifting in some areas.

Pulmonary Transcriptional Response to Ozone in Healthy and Cardiovascular Compromised Rat Models

EPA Science Inventory

The genetic cardiovascular disease (CVD) and associated metabolic impairments can influence the lung injury from inhaled pollutants. We hypothesized that comparative assessment of global pulmonary expression profile of healthy and CVD-prone rat models will provide mechanistic ins...

A Mechanistic Design Approach for Graphite Nanoplatelet (GNP) Reinforced Asphalt Mixtures for Low-Temperature Applications

DOT National Transportation Integrated Search

2018-01-01

This report explores the application of a discrete computational model for predicting the fracture behavior of asphalt mixtures at low temperatures based on the results of simple laboratory experiments. In this discrete element model, coarse aggregat...

Adaptive Response in Female Modeling of the Hypothalamic-pituitary-gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course ...

The role of prostaglandins E1 and E2, dinoprostone, and misoprostol in cervical ripening and the induction of labor: a mechanistic approach.

PubMed

Bakker, Ronan; Pierce, Stephanie; Myers, Dean

2017-08-01

Prostaglandins play a critical role in cervical ripening by increasing inflammatory mediators in the cervix and inducing cervical remodeling. Prostaglandin E1 (PGE1) and prostaglandin E2 (PGE2) exert different effects on these processes and on myometrial contractility. These mechanistic differences may affect outcomes in women treated with dinoprostone, a formulation identical to endogenous PGE2, compared with misoprostol, a PGE1 analog. The objective of this review is to evaluate existing evidence regarding mechanistic differences between PGE1 and PGE2, and consider the clinical implications of these differences in patients requiring cervical ripening for labor induction. We conducted a critical narrative review of peer-reviewed articles identified using PubMed and other online databases. While both dinoprostone and misoprostol are effective in cervical ripening and labor induction, they differ in their clinical and pharmacological profiles. PGE2 has been shown to stimulate interleukin-8, an inflammatory cytokine that promotes the influx of neutrophils and induces remodeling of the cervical extracellular matrix, and to induce functional progesterone withdrawal. Misoprostol has been shown to elicit a dose-dependent effect on myometrial contractility, which may affect rates of uterine tachysystole in clinical practice. Differences in the mechanism of action between misoprostol and PGE2 may contribute to their variable effects in the cervix and myometrium, and should be considered to optimize outcomes.

CCR2 antagonism leads to marked reduction in proteinuria and glomerular injury in murine models of focal segmental glomerulosclerosis (FSGS)

PubMed Central

Miao, Zhenhua; Ertl, Linda S.; Newland, Dale; Zhao, Bin; Wang, Yu; Zang, Xiaoping; Campbell, James J.; Liu, Xiaoli; Dang, Ton; Miao, Shichang; Krasinski, Antoni; Punna, Sreenivas; Zeng, Yibin; McMahon, Jeffrey; Zhang, Penglie; Charo, Israel F.; Schall, Thomas J.

2018-01-01

Focal segmental glomerulosclerosis (FSGS) comprises a group of uncommon disorders that present with marked proteinuria, nephrotic syndrome, progressive renal failure and characteristic glomerular lesions on histopathology. The current standard of care for patients with FSGS include immunosuppressive drugs such as glucocorticoids followed by calcineurin inhibitors, if needed for intolerance or inadequate response to glucocorticoids. Renin-angiotensin-aldosterone (RAAS) blockers are also used to control proteinuria, an important signature of FSGS. Existing treatments, however, achieved only limited success. Despite best care, treatment failure is common and FSGS is causal in a significant proportion of end stage renal disease. Thus, an unmet need exists for novel disease modifying treatments for FSGS. We employed two widely-used murine models of FSGS to test the hypothesis that systemic inhibition of chemokine receptor CCR2 would have therapeutic benefit. Here we report that administration CCX872, a potent and selective small molecule antagonist of CCR2, achieved rapid and sustained attenuation of renal damage as determined by urine albumin excretion and improved histopathological outcome. Therapeutic benefit was present when CCX872 was used as a single therapy, and moreover, the combination of CCX872 and RAAS blockade was statistically more effective than RAAS blockade alone. In addition, the combination of CCR2 and RAAS blockade was equally as effective as endothelin receptor inhibition. We conclude that specific inhibition of CCR2 is effective in the Adriamycin-induced and 5/6 nephrectomy murine models of FSGS, and thus holds promise as a mechanistically distinct therapeutic addition to the treatment of human FSGS. PMID:29561839

Application of the adverse outcome pathway (AOP) concept to structure the available in vivo and in vitro mechanistic data for allergic sensitization to food proteins.

PubMed

van Bilsen, Jolanda H M; Sienkiewicz-Szłapka, Edyta; Lozano-Ojalvo, Daniel; Willemsen, Linette E M; Antunes, Celia M; Molina, Elena; Smit, Joost J; Wróblewska, Barbara; Wichers, Harry J; Knol, Edward F; Ladics, Gregory S; Pieters, Raymond H H; Denery-Papini, Sandra; Vissers, Yvonne M; Bavaro, Simona L; Larré, Colette; Verhoeckx, Kitty C M; Roggen, Erwin L

2017-01-01

The introduction of whole new foods in a population may lead to sensitization and food allergy. This constitutes a potential public health problem and a challenge to risk assessors and managers as the existing understanding of the pathophysiological processes and the currently available biological tools for prediction of the risk for food allergy development and the severity of the reaction are not sufficient. There is a substantial body of in vivo and in vitro data describing molecular and cellular events potentially involved in food sensitization. However, these events have not been organized in a sequence of related events that is plausible to result in sensitization, and useful to challenge current hypotheses. The aim of this manuscript was to collect and structure the current mechanistic understanding of sensitization induction to food proteins by applying the concept of adverse outcome pathway (AOP). The proposed AOP for food sensitization is based on information on molecular and cellular mechanisms and pathways evidenced to be involved in sensitization by food and food proteins and uses the AOPs for chemical skin sensitization and respiratory sensitization induction as templates. Available mechanistic data on protein respiratory sensitization were included to fill out gaps in the understanding of how proteins may affect cells, cell-cell interactions and tissue homeostasis. Analysis revealed several key events (KE) and biomarkers that may have potential use in testing and assessment of proteins for their sensitizing potential. The application of the AOP concept to structure mechanistic in vivo and in vitro knowledge has made it possible to identify a number of methods, each addressing a specific KE, that provide information about the food allergenic potential of new proteins. When applied in the context of an integrated strategy these methods may reduce, if not replace, current animal testing approaches. The proposed AOP will be shared at the www.aopwiki.org platform to expand the mechanistic data, improve the confidence in each of the proposed KE and key event relations (KERs), and allow for the identification of new, or refinement of established KE and KERs.

A mechanistic model for the evolution of multicellularity

NASA Astrophysics Data System (ADS)

Amado, André; Batista, Carlos; Campos, Paulo R. A.

2018-02-01

Through a mechanistic approach we investigate the formation of aggregates of variable sizes, accounting mechanisms of aggregation, dissociation, death and reproduction. In our model, cells can produce two metabolites, but the simultaneous production of both metabolites is costly in terms of fitness. Thus, the formation of larger groups can favor the aggregates to evolve to a configuration where division of labor arises. It is assumed that the states of the cells in a group are those that maximize organismal fitness. In the model it is considered that the groups can grow linearly, forming a chain, or compactly keeping a roughly spherical shape. Starting from a population consisting of single-celled organisms, we observe the formation of groups with variable sizes and usually much larger than two-cell aggregates. Natural selection can favor the formation of large groups, which allows the system to achieve new and larger fitness maxima.

Perception of mind and dehumanization: Human, animal, or machine?

PubMed

Morera, María D; Quiles, María N; Correa, Ana D; Delgado, Naira; Leyens, Jacques-Philippe

2016-08-02

Dehumanization is reached through several approaches, including the attribute-based model of mind perception and the metaphor-based model of dehumanization. We performed two studies to find different (de)humanized images for three targets: Professional people, Evil people, and Lowest of the low. In Study 1, we examined dimensions of mind, expecting the last two categories to be dehumanized through denial of agency (Lowest of the low) or experience (Evil people), compared with humanized targets (Professional people). Study 2 aimed to distinguish these targets using metaphors. We predicted that Evil and Lowest of the low targets would suffer mechanistic and animalistic dehumanization, respectively; our predictions were confirmed, but the metaphor-based model nuanced these results: animalistic and mechanistic dehumanization were shown as overlapping rather than independent. Evil persons were perceived as "killing machines" and "predators." Finally, Lowest of the low were not animalized but considered human beings. We discuss possible interpretations. © 2016 International Union of Psychological Science.

Modeling Study: Mechanism of Foam Propagation in Porous Media at Different Levels of Minimum Mobilization Pressure Gradient

NASA Astrophysics Data System (ADS)

Izadi, M.; Kam, S.

2017-12-01

Scope: Numerous laboratory and field tests revealed that foam can effectively control gas mobility and improve sweep efficiency in enhanced-oil-recovery and subsurface-remediation processes, if correctly designed. The objective of this study is to answer (i) how mechanistic foam model parameters can be determined by fitting lab experiments in a step-by-step manner; (ii) how different levels of mobilization pressure gradient for foam generation affects the fundamentals of foam propagation; and (iii) how foam propagation distance can be estimated in the subsurface. This study for the first time shows why, and by how much, supercritical CO2 foams are advantaged over other types of foams such as N2 foam. Methods: First of all, by borrowing experimental data existing in the literature, this study shows how to capture mechanistic foam model parameters. The model, then, is applied to a wide range of mobilization pressure gradient to represent different types of foams that have been applied in the field (Note that supercritical CO2 foams exhibit much lower mobilization pressure compared to other types of foams (N2, steam, air, etc.). Finally, the model and parameters are used to evaluate different types of foam injection scenarios in order to predict how far foams can propagate with what properties in the field condition. Results and Conclusions: The results show that (i) the presence of three different foam states (strong, weak, intermediate) as well as two different strong-foam flow regimes (high-quality and low-quality regimes) plays a key role in model fit and field-scale propagation prediction and (ii) the importance of complex non-Newtonian foam rheology should not be underestimated. More specifically, this study finds that (i) supercritical CO2 foams can propagate a few hundreds of feet easily, which is a few orders of magnitude higher than other foams such as N2 foams; (ii) for dry foams (or, strong foams in the high-quality regime), the higher gas fractions the less foams travel, while for wet foams (or, strong foams in the low-quality regime) the distance is not sensitive to gas fraction; and (iii) the higher injection rates (or pressures), the farther foams propagate (this effect is much more pronounced for dry foams).