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.

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.

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.

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.

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

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.

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.

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...

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

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.

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

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 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.

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

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

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

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.

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

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.

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.

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.

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

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 ...

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.

Modelling the mating system of polar bears: a mechanistic approach to the Allee effect.

PubMed

Molnár, Péter K; Derocher, Andrew E; Lewis, Mark A; Taylor, Mitchell K

2008-01-22

Allee effects may render exploited animal populations extinction prone, but empirical data are often lacking to describe the circumstances leading to an Allee effect. Arbitrary assumptions regarding Allee effects could lead to erroneous management decisions so that predictive modelling approaches are needed that identify the circumstances leading to an Allee effect before such a scenario occurs. We present a predictive approach of Allee effects for polar bears where low population densities, an unpredictable habitat and harvest-depleted male populations result in infrequent mating encounters. We develop a mechanistic model for the polar bear mating system that predicts the proportion of fertilized females at the end of the mating season given population density and operational sex ratio. The model is parametrized using pairing data from Lancaster Sound, Canada, and describes the observed pairing dynamics well. Female mating success is shown to be a nonlinear function of the operational sex ratio, so that a sudden and rapid reproductive collapse could occur if males are severely depleted. The operational sex ratio where an Allee effect is expected is dependent on population density. We focus on the prediction of Allee effects in polar bears but our approach is also applicable to other species.

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.

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.

A Unifying Mechanistic Model of Selective Attention in Spiking Neurons

PubMed Central

Bobier, Bruce; Stewart, Terrence C.; Eliasmith, Chris

2014-01-01

Visuospatial attention produces myriad effects on the activity and selectivity of cortical neurons. Spiking neuron models capable of reproducing a wide variety of these effects remain elusive. We present a model called the Attentional Routing Circuit (ARC) that provides a mechanistic description of selective attentional processing in cortex. The model is described mathematically and implemented at the level of individual spiking neurons, with the computations for performing selective attentional processing being mapped to specific neuron types and laminar circuitry. The model is used to simulate three studies of attention in macaque, and is shown to quantitatively match several observed forms of attentional modulation. Specifically, ARC demonstrates that with shifts of spatial attention, neurons may exhibit shifting and shrinking of receptive fields; increases in responses without changes in selectivity for non-spatial features (i.e. response gain), and; that the effect on contrast-response functions is better explained as a response-gain effect than as contrast-gain. Unlike past models, ARC embodies a single mechanism that unifies the above forms of attentional modulation, is consistent with a wide array of available data, and makes several specific and quantifiable predictions. PMID:24921249

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).

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.

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.

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.

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

Microvesicating effects of sulfur mustard on an in vitro human skin model.

PubMed

Hayden, Patrick J; Petrali, John P; Stolper, Gina; Hamilton, Tracey A; Jackson, George R; Wertz, Philip W; Ito, Susumu; Smith, William J; Klausner, Mitchell

2009-10-01

Bis-(beta-chloroethyl) sulfide (SM) is a potent skin vesicant previously used for chemical warfare. Progress in determination of the mechanistic basis of SM pathology, and development of prophylactic and/or therapeutic countermeasures to SM exposure has been hampered by lack of physiologically relevant models of human skin. The current work evaluated a newly developed tissue engineered full-thickness human skin model in a completely in vitro approach to investigation of SM-induced dermal pathology. The model was first characterized with regard to overall morphology, lipid composition, basement membrane (BM) composition and ultrastructural features that are important targets of SM pathologic activity. Well-developed BM ultrastructural features were observed at the dermal-epidermal junction (DEJ), thus demonstrating successful resolution of a primary deficiency of models previously evaluated for SM studies. Studies were then conducted to evaluate histopathological effects of SM on the model. Good replication of in vivo effects was observed, including apoptosis of basal keratinocytes (KC) and microblister formation at the DEJ. Tissue engineered skin models with well-developed basement membrane structures thus appear to be useful tools for in vitro mechanistic studies of SM vesicant activity and development of preventive/therapeutic approaches for SM pathology.

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.

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).

Simulating malaria transmission in the current and future climate of West Africa

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Bomblies, A.; Eltahir, E. A. B.

2015-12-01

Malaria transmission in West Africa is closely tied to climate, as rain fed water pools provide breeding habitat for the anopheles mosquito vector, and temperature affects the mosquito's ability to spread disease. We present results of a highly detailed, spatially explicit mechanistic modelling study exploring the relationships between the environment and malaria in the current and future climate of West Africa. A mechanistic model of human immunity was incorporated into an existing agent-based model of malaria transmission, allowing us to move beyond entomological measures such as mosquito density and vectorial capacity to analyzing the prevalence of the malaria parasite within human populations. The result is a novel modelling tool that mechanistically simulates all of the key processes linking environment to malaria transmission. Simulations were conducted across climate zones in West Africa, linking temperature and rainfall to entomological and epidemiological variables with a focus on nonlinearities due to threshold effects and interannual variability. Comparisons to observations from the region confirmed that the model provides a reasonable representation of the entomological and epidemiological conditions in this region. We used the predictions of future climate from the most credible CMIP5 climate models to predict the change in frequency and severity of malaria epidemics in West Africa as a result of climate change.

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

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...

Mechanistic modeling & effectiveness of buffer strips for pesticide regulatory frameworks

USDA-ARS?s Scientific Manuscript database

Vegetative Filter Strips (VFS) have been used as an effective conservation practice in agricultural areas for controlling and mitigate the effect of sediment, nutrients and pesticides loads into water bodies. In addition to the agricultural sector, another important use of VFS for controlling plague...

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.

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.

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

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.

Simulation of Plant Physiological Process Using Fuzzy Variables

Treesearch

Daniel L. Schmoldt

1991-01-01

Qualitative modelling can help us understand and project effects of multiple stresses on trees. It is not practical to collect and correlate empirical data for all combinations of plant/environments and human/climate stresses, especially for mature trees in natural settings. Therefore, a mechanistic model was developed to describe ecophysiological processes. This model...

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.

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

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.

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.

Fluid mechanics of Windkessel effect.

PubMed

Mei, C C; Zhang, J; Jing, H X

2018-01-08

We describe a mechanistic model of Windkessel phenomenon based on the linear dynamics of fluid-structure interactions. The phenomenon has its origin in an old-fashioned fire-fighting equipment where an air chamber serves to transform the intermittent influx from a pump to a more steady stream out of the hose. A similar mechanism exists in the cardiovascular system where blood injected intermittantly from the heart becomes rather smooth after passing through an elastic aorta. In existing haeodynamics literature, this mechanism is explained on the basis of electric circuit analogy with empirical impedances. We present a mechanistic theory based on the principles of fluid/structure interactions. Using a simple one-dimensional model, wave motion in the elastic aorta is coupled to the viscous flow in the rigid peripheral artery. Explicit formulas are derived that exhibit the role of material properties such as the blood density, viscosity, wall elasticity, and radii and lengths of the vessels. The current two-element model in haemodynamics is shown to be the limit of short aorta and low injection frequency and the impedance coefficients are derived theoretically. Numerical results for different aorta lengths and radii are discussed to demonstrate their effects on the time variations of blood pressure, wall shear stress, and discharge. Graphical Abstract A mechanistic analysis of Windkessel Effect is described which confirms theoretically the well-known feature that intermittent influx becomes continuous outflow. The theory depends only on the density and viscosity of the blood, the elasticity and dimensions of the vessel. Empirical impedence parameters are avoided.

Development of a Mechanistic-Based Healing Model for Self-Healing Glass Seals

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

Xu, Wei; Stephens, Elizabeth V.; Sun, Xin

Self-healing glass, a recent development of hermetic sealant materials, has the ability to effectively repair damage when heated to elevated temperatures; thus, able to extend its service life. Since crack healing morphological changes in the glass material are usually temperature and stress dependent, quantitative studies to determine the effects of thermo-mechanical conditions on the healing behavior of the self-healing glass sealants are extremely useful to accommodate the design and optimization of the sealing systems within SOFCs. The goal of this task is to develop a mechanistic-based healing model to quantify the stress and temperature dependent healing behavior. A two-step healingmore » mechanism was developed and implemented into finite element (FE) models through user-subroutines. Integrated experimental/kinetic Monte Carlo (kMC) simulation methodology was taken to calibrate the model parameters. The crack healing model is able to investigate the effects of various thermo-mechanical factors; therefore, able to determine the critical conditions under which the healing mechanism will be activated. Furthermore, the predicted results can be used to formulate the continuum damage-healing model and to assist the SOFC stack level simulations in predicting and evaluating the effectiveness and the performance of various engineering seal designs.« less

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.

Viscoelastic Emulsion Improved the Bioaccessibility and Oral Bioavailability of Crystalline Compound: A Mechanistic Study Using in Vitro and in Vivo Models.

PubMed

Ting, Yuwen; Jiang, Yike; Lan, Yaqi; Xia, Chunxin; Lin, Zhenyu; Rogers, Michael A; Huang, Qingrong

2015-07-06

The oral bioavailability of hydrophobic compound is usually limited by the poor aqueous solubility in the gastrointestinal (GI) tract. Various oral formulations were developed to enhance the systemic concentration of such molecules. Moreover, compounds with high melting temperature that appear as insoluble crystals imposed a great challenge to the development of oral vehicle. Polymethoxyflavone, an emerging category of bioactive compounds with potent therapeutic efficacies, were characterized as having a hydrophobic and highly crystalline chemical structure. To enhance the oral dosing efficiency of polymethoxyflavone, a viscoelastic emulsion system with a high static viscosity was developed and optimized using tangeretin, one of the most abundant polymethoxyflavones found in natural sources, as a modeling compound. In the present study, different in vitro and in vivo models were used to mechanistically evaluate the effect of emulsification on oral bioavailability of tangeretin. In vitro lipolysis revealed that emulsified tangeretin was digested and became bioaccessible much faster than unprocessed tangeretin oil suspension. By simulating the entire human GI tract, TNO's gastrointestinal model (TIM-1) is a valuable tool to mechanistically study the effect of emulsification on the digestion events that lead to a better oral bioavailability of tangeretin. TIM-1 result indicated that tangeretin was absorbed in the upper GI tract. Thus, a higher oral bioavailability can be expected if the compound becomes bioaccessible in the intestinal lumen soon after dosing. In vivo pharmacokinetics analysis on mice again confirmed that the oral bioavailability of tangeretin increased 2.3 fold when incorporated in the viscoelastic emulsion than unformulated oil suspension. By using the combination of in vitro and in vivo models introduced in this work, the mechanism that underlie the effect of viscoelastic emulsion on the oral bioavailability of tangeretin was well-elucidated.

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.

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.

Household water use and conservation models using Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Cahill, R.; Lund, J. R.; DeOreo, B.; Medellín-Azuara, J.

2013-10-01

The increased availability of end use measurement studies allows for mechanistic and detailed approaches to estimating household water demand and conservation potential. This study simulates water use in a single-family residential neighborhood using end-water-use parameter probability distributions generated from Monte Carlo sampling. This model represents existing water use conditions in 2010 and is calibrated to 2006-2011 metered data. A two-stage mixed integer optimization model is then developed to estimate the least-cost combination of long- and short-term conservation actions for each household. This least-cost conservation model provides an estimate of the upper bound of reasonable conservation potential for varying pricing and rebate conditions. The models were adapted from previous work in Jordan and are applied to a neighborhood in San Ramon, California in the eastern San Francisco Bay Area. The existing conditions model produces seasonal use results very close to the metered data. The least-cost conservation model suggests clothes washer rebates are among most cost-effective rebate programs for indoor uses. Retrofit of faucets and toilets is also cost-effective and holds the highest potential for water savings from indoor uses. This mechanistic modeling approach can improve understanding of water demand and estimate cost-effectiveness of water conservation programs.

Household water use and conservation models using Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Cahill, R.; Lund, J. R.; DeOreo, B.; Medellín-Azuara, J.

2013-04-01

The increased availability of water end use measurement studies allows for more mechanistic and detailed approaches to estimating household water demand and conservation potential. This study uses, probability distributions for parameters affecting water use estimated from end use studies and randomly sampled in Monte Carlo iterations to simulate water use in a single-family residential neighborhood. This model represents existing conditions and is calibrated to metered data. A two-stage mixed integer optimization model is then developed to estimate the least-cost combination of long- and short-term conservation actions for each household. This least-cost conservation model provides an estimate of the upper bound of reasonable conservation potential for varying pricing and rebate conditions. The models were adapted from previous work in Jordan and are applied to a neighborhood in San Ramon, California in eastern San Francisco Bay Area. The existing conditions model produces seasonal use results very close to the metered data. The least-cost conservation model suggests clothes washer rebates are among most cost-effective rebate programs for indoor uses. Retrofit of faucets and toilets is also cost effective and holds the highest potential for water savings from indoor uses. This mechanistic modeling approach can improve understanding of water demand and estimate cost-effectiveness of water conservation programs.

A Systems Biology Approach to Toxicology Research with Small Fish Models

EPA Science Inventory

Increasing use of mechanistically-based molecular and biochemical endpoints and in vitro assays is being advocated as a more efficient and cost-effective approach for generating chemical hazard data. However, development of effective assays and application of the resulting data i...

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.

Putting mechanisms into crop production models.

PubMed

Boote, Kenneth J; Jones, James W; White, Jeffrey W; Asseng, Senthold; Lizaso, Jon I

2013-09-01

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects. © 2013 John Wiley & Sons Ltd.

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.

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.

Theory of advection-driven long range biotic transport

USDA-ARS?s Scientific Manuscript database

We propose a simple mechanistic model to examine the effects of advective flow on the spread of fungal diseases spread by wind-blown spores. The model is defined by a set of two coupled non-linear partial differential equations for spore densities. One equation describes the long-distance advectiv...

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.

Developing the next generation of forest ecosystem models

Treesearch

Christopher R. Schwalm; Alan R. Ek

2002-01-01

Forest ecology and management are model-rich areas for research. Models are often cast as either empirical or mechanistic. With evolving climate change, hybrid models gain new relevance because of their ability to integrate existing mechanistic knowledge with empiricism based on causal thinking. The utility of hybrid platforms results in the combination of...

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...

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…

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

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.

Implications of mechanistic modeling of drought effects on growth and competition in forest landscape models

Treesearch

Eric J. Gustafson; Arjan M. G. De Bruijn; Brian R. Miranda; Brian R. Sturtevant; J. Thompson

2016-01-01

The incidence of drought is expected to increase worldwide as a factor structuring forested landscapes. Ecophysiological mechanisms are being added to Forest Landscape Models (FLMs) to increase their robustness to the novel environmental conditions of the future (including drought), but their behavior has not been evaluated for mixed temperate forests. We evaluated...

SIMULATION OF OZONE EFFECTS ON EIGHT TREE SPECIES AT SHENANDOAH NATIONAL PARK

EPA Science Inventory

As part of an assessment of potential effects of air pollutants on the vegetation of Shenandoah National Park (SHEN), we simulated the growth of eight important tree species using TREGRO, a mechanistic model of individual tree growth. Published TREGRO parameters for black cherry...

ATOMIC-SCALE DESIGN OF IRON FISCHER-TROPSCH CATALYSTS: A COMBINED COMPUTATIONAL CHEMISTRY, EXPERIMENTAL, AND MICROKINETIC MODELING APPROACH

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

Manos Mavrikakis; James A. Dumesic; Amit A. Gokhale

2005-03-22

Efforts during this first year focused on four areas: (1) searching/summarizing published FTS mechanistic and kinetic studies of FTS reactions on iron catalysts; (2) construction of mass spectrometer-TPD and Berty CSTR reactor systems; (3) preparation and characterization of unsupported iron and alumina-supported iron catalysts at various iron loadings (4) Determination of thermochemical parameters such as binding energies of reactive intermediates, heat of FTS elementary reaction steps, and kinetic parameters such as activation energies, and frequency factors of FTS elementary reaction steps on a number of model surfaces. Literature describing mechanistic and kinetic studies of Fischer-Tropsch synthesis on iron catalysts wasmore » compiled in a draft review. Construction of the mass spectrometer-TPD system is 90% complete and of a Berty CSTR reactor system 98% complete. Three unsupported iron catalysts and three alumina-supported iron catalysts were prepared by nonaqueous-evaporative deposition (NED) or aqueous impregnation (AI) and characterized by chemisorption, BET, extent-of-reduction, XRD, and TEM methods. These catalysts, covering a wide range of dispersions and metal loadings, are well-reduced and relatively thermally stable up to 500-600 C in H{sub 2}, thus ideal for kinetic and mechanistic studies. The alumina-supported iron catalysts will be used for kinetic and mechanistic studies. In the coming year, adsorption/desorption properties, rates of elementary steps, and global reaction rates will be measured for these catalysts, with and without promoters, providing a database for understanding effects of dispersion, metal loading, and support on elementary kinetic parameters and for validation of computational models that incorporate effects of surface structure and promoters. Furthermore, using state-of-the-art self-consistent Density Functional Theory (DFT) methods, we have extensively studied the thermochemistry and kinetics of various elementary steps on three different model surfaces: (1) Fe(110), (2) Fe(110) modified by subsurface C, and (3) Fe surface modified with Pt adatoms. These studies have yielded valuable insights into the reactivity of Fe surfaces for FTS, and provided accurate estimates for the effect of Fe modifiers such as subsurface C and surface Pt.« less

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.

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.

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

Pharmacokinetic/Pharmacodynamic Relationship of Gabapentin in a CFA-induced Inflammatory Hyperalgesia Rat Model.

PubMed

Larsen, Malte Selch; Keizer, Ron; Munro, Gordon; Mørk, Arne; Holm, René; Savic, Rada; Kreilgaard, Mads

2016-05-01

Gabapentin displays non-linear drug disposition, which complicates dosing for optimal therapeutic effect. Thus, the current study was performed to elucidate the pharmacokinetic/pharmacodynamic (PKPD) relationship of gabapentin's effect on mechanical hypersensitivity in a rat model of CFA-induced inflammatory hyperalgesia. A semi-mechanistic population-based PKPD model was developed using nonlinear mixed-effects modelling, based on gabapentin plasma and brain extracellular fluid (ECF) time-concentration data and measurements of CFA-evoked mechanical hyperalgesia following administration of a range of gabapentin doses (oral and intravenous). The plasma/brain ECF concentration-time profiles of gabapentin were adequately described with a two-compartment plasma model with saturable intestinal absorption rate (K m  = 44.1 mg/kg, V max  = 41.9 mg/h∙kg) and dose-dependent oral bioavailability linked to brain ECF concentration through a transit compartment. Brain ECF concentration was directly linked to a sigmoid E max function describing reversal of hyperalgesia (EC 50, plasma  = 16.7 μg/mL, EC 50, brain  = 3.3 μg/mL). The proposed semi-mechanistic population-based PKPD model provides further knowledge into the understanding of gabapentin's non-linear pharmacokinetics and the link between plasma/brain disposition and anti-hyperalgesic effects. The model suggests that intestinal absorption is the primary source of non-linearity and that the investigated rat model provides reasonable predictions of clinically effective plasma concentrations for gabapentin.

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

Computational Approach using Mouse Embryonic Stem Cells to Define a Mechanistic Applicability Domain for Prenatal Developmental Toxicity

EPA Science Inventory

Identification of mechanisms responsible for adverse developmental effects is the first step in creating predictive toxicity models. Identification of putative mechanisms was performed by co-analyzing three datasets for the effects of ToxCast phase Ia and II chemicals: 1.In vitro...

Effects of Repetition on Associative Recognition in Young and Older Adults: Item and Associative Strengthening

DTIC Science & Technology

2010-01-01

familiarity (e.g., Diana, Reder, Arndt, & Park, 2006; Jacoby, 1991; Mandler, 1980; Reder, Nhouvanisvong, Schunn, Ayers, Angstadt, & Hiraki , 2000...Schunn, C. D., Ayers, M. S., Angstadt, P., & Hiraki , K. (2000). A mechanistic account of the mirror effect for word frequency: A computational model of

The stochastic system approach for estimating dynamic treatments effect.

PubMed

Commenges, Daniel; Gégout-Petit, Anne

2015-10-01

The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system" approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results.

Sequential Exposure of Bortezomib and Vorinostat is Synergistic in Multiple Myeloma Cells

PubMed Central

Nanavati, Charvi; Mager, Donald E.

2018-01-01

Purpose To examine the combination of bortezomib and vorinostat in multiple myeloma cells (U266) and xenografts, and to assess the nature of their potential interactions with semi-mechanistic pharmacodynamic models and biomarkers. Methods U266 proliferation was examined for a range of bortezomib and vorinostat exposure times and concentrations (alone and in combination). A non-competitive interaction model was used with interaction parameters that reflect the nature of drug interactions after simultaneous and sequential exposures. p21 and cleaved PARP were measured using immunoblotting to assess critical biomarker dynamics. For xenografts, data were extracted from literature and modeled with a PK/PD model with an interaction parameter. Results Estimated model parameters for simultaneous in vitro and xenograft treatments suggested additive drug effects. The sequence of bortezomib preincubation for 24 hours, followed by vorinostat for 24 hours, resulted in an estimated interaction term significantly less than 1, suggesting synergistic effects. p21 and cleaved PARP were also up-regulated the most in this sequence. Conclusions Semi-mechanistic pharmacodynamic modeling suggests synergistic pharmacodynamic interactions for the sequential administration of bortezomib followed by vorinostat. Increased p21 and cleaved PARP expression can potentially explain mechanisms of their enhanced effects, which require further PK/PD systems analysis to suggest an optimal dosing regimen. PMID:28101809

Fisher's geometrical model emerges as a property of complex integrated phenotypic networks.

PubMed

Martin, Guillaume

2014-05-01

Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts.

Animal models of contraception: utility and limitations

PubMed Central

Liechty, Emma R; Bergin, Ingrid L; Bell, Jason D

2015-01-01

Appropriate animal modeling is vital for the successful development of novel contraceptive devices. Advances in reproductive biology have identified novel pathways for contraceptive intervention. Here we review species-specific anatomic and physiologic considerations impacting preclinical contraceptive testing, including efficacy testing, mechanistic studies, device design, and modeling off-target effects. Emphasis is placed on the use of nonhuman primate models in contraceptive device development. PMID:29386922

Computational Model of the Fathead Minnow Hypothalamic-Pituitary-Gonadal Axis: Incorporating Protein Synthesis in Improving Predictability of Responses to Endocrine Active Chemicals

EPA Science Inventory

There is international concern about chemicals that alter endocrine system function in humans and/or wildlife and subsequently cause adverse effects. We previously developed a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minno...

A Mechanistic-Based Healing Model for Self-Healing Glass Seals Used in Solid Oxide Fuel Cells

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

Xu, Wei; Sun, Xin; Stephens, Elizabeth V.

The usage of self-healing glass as hermetic seals is a recent advancement in sealing technology development for the planar solid oxide fuel cells (SOFCs). Because of its capability of restoring the mechanical properties at elevated temperatures, the self-healing glass seal is expected to provide high reliability in maintaining the long-term structural integrity and functionality of SOFCs. In order to accommodate the design and to evaluate the effectiveness of such engineering seals under various thermo-mechanical operating conditions, computational modeling framework needs to be developed to accurately capture and predict the healing behavior of the glass material. In the present work, amore » mechanistic-based two-stage model was developed to study the stress and temperature-dependent crack healing of the self-healing glass materials. The model was first calibrated by experimental measurements combined with the kinetic Monte Carlo (kMC) simulation results and then implemented into the finite element analysis (FEA). The effects of various factors, i.e. stress, temperature, crack morphology, on the healing behavior of the glass were investigated and discussed.« less

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...

Application of the coastal generalized ecosystem model (CGEM) to assess the impacts of a potential future climate scenario on northern Gulf of Mexico hypoxia

EPA Science Inventory

Mechanistic hypoxia models for the northern Gulf of Mexico are being used to guide policy goals for Mississippi River nutrient loading reductions. However, to date, these models have not examined the effects of both nutrient loads and future climate. Here, we simulate a future c...

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

Transporter-Enzyme Interplay: Deconvoluting Effects of Hepatic Transporters and Enzymes on Drug Disposition Using Static and Dynamic Mechanistic Models.

PubMed

Varma, Manthena V; El-Kattan, Ayman F

2016-07-01

A large body of evidence suggests hepatic uptake transporters, organic anion-transporting polypeptides (OATPs), are of high clinical relevance in determining the pharmacokinetics of substrate drugs, based on which recent regulatory guidances to industry recommend appropriate assessment of investigational drugs for the potential drug interactions. We recently proposed an extended clearance classification system (ECCS) framework in which the systemic clearance of class 1B and 3B drugs is likely determined by hepatic uptake. The ECCS framework therefore predicts the possibility of drug-drug interactions (DDIs) involving OATPs and the effects of genetic variants of SLCO1B1 early in the discovery and facilitates decision making in the candidate selection and progression. Although OATP-mediated uptake is often the rate-determining process in the hepatic clearance of substrate drugs, metabolic and/or biliary components also contribute to the overall hepatic disposition and, more importantly, to liver exposure. Clinical evidence suggests that alteration in biliary efflux transport or metabolic enzymes associated with genetic polymorphism leads to change in the pharmacodynamic response of statins, for which the pharmacological target resides in the liver. Perpetrator drugs may show inhibitory and/or induction effects on transporters and enzymes simultaneously. It is therefore important to adopt models that frame these multiple processes in a mechanistic sense for quantitative DDI predictions and to deconvolute the effects of individual processes on the plasma and hepatic exposure. In vitro data-informed mechanistic static and physiologically based pharmacokinetic models are proven useful in rationalizing and predicting transporter-mediated DDIs and the complex DDIs involving transporter-enzyme interplay. © 2016, The American College of Clinical Pharmacology.

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.

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 ...

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

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.

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

Effect of space flight on interferon production - mechanistic studies

NASA Technical Reports Server (NTRS)

Sonnenfeld, Gerald

1991-01-01

Ground-based models were studied for the effects of space flight on immune responses. Most time was spent on the model for the antiorthostatic, hypokinetic, hypodynamic suspension model for rats. Results indicate that suspension is useful for modeling the effects of spaceflight on functional immune responses, such as interferon and interleukin production. It does not appear to be useful for modeling shifts in leukocyte sub-populations. Calcium and 1,25-dihydroxyvitamin D sub 3 appear to play a pivitol role in regulating shifts in immune responses due to suspension. The macrophage appears to be an important target cell for the effects of suspension on immune responses.

A series RCL circuit theory for analyzing non-steady-state water uptake of maize plants.

PubMed

Zhuang, Jie; Yu, Gui-Rui; Nakayama, Keiichi

2014-10-22

Understanding water uptake and transport through the soil-plant continuum is vital for ecosystem management and agricultural water use. Plant water uptake under natural conditions is a non-steady transient flow controlled by root distribution, plant configuration, soil hydraulics, and climatic conditions. Despite significant progress in model development, a mechanistic description of transient water uptake has not been developed or remains incomplete. Here, based on advanced electrical network theory (RLC circuit theory), we developed a non-steady state biophysical model to mechanistically analyze the fluctuations of uptake rates in response to water stress. We found that the non-steady-state model captures the nature of instantaneity and hysteresis of plant water uptake due to the considerations of water storage in plant xylem and coarse roots (capacitance effect), hydraulic architecture of leaf system (inductance effect), and soil-root contact (fuse effect). The model provides insights into the important role of plant configuration and hydraulic heterogeneity in helping plants survive an adverse environment. Our tests against field data suggest that the non-steady-state model has great potential for being used to interpret the smart water strategy of plants, which is intrinsically determined by stem size, leaf size/thickness and distribution, root system architecture, and the ratio of fine-to-coarse root lengths.

A semi-mechanistic model of CP-690,550-induced reduction in neutrophil counts in patients with rheumatoid arthritis.

PubMed

Gupta, Pankaj; Friberg, Lena E; Karlsson, Mats O; Krishnaswami, Sriram; French, Jonathan

2010-06-01

CP-690,550, a selective inhibitor of the Janus kinase family, is being developed as an oral disease-modifying antirheumatic drug for the treatment of rheumatoid arthritis (RA). A semi-mechanistic model was developed to characterize the time course of drug-induced absolute neutrophil count (ANC) reduction in a phase 2a study. Data from 264 RA patients receiving 6-week treatment (placebo, 5, 15, 30 mg bid) followed by a 6-week off-treatment period were analyzed. The model included a progenitor cell pool, a maturation chain comprising transit compartments, a circulation pool, and a feedback mechanism. The model was adequately described by system parameters (BASE(h), ktr(h), gamma, and k(circ)), disease effect parameters (DIS), and drug effect parameters (k(off) and k(D)). The disease manifested as an increase in baseline ANC and reduced maturation time due to increased demand from the inflammation site. The drug restored the perturbed system parameters to their normal values via an indirect mechanism. ANC reduction due to a direct myelosuppressive drug effect was not supported. The final model successfully described the dose- and time-dependent changes in ANC and predicted the incidence of neutropenia at different doses reasonably well.

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.

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.

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

Collection of materials and performance data for Texas flexible pavements and overlays : project summary.

DOT National Transportation Integrated Search

2015-08-31

Proper calibration of mechanistic-empirical : (M-E) design and rehabilitation performance : models to meet Texas conditions is essential : for cost-effective flexible pavement designs. : Such a calibration effort would require a : reliable source of ...

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, 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...

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

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.

Boreal soil carbon dynamics under a changing climate: a model inversion approach

Treesearch

Zhaosheng Fan; Jason C. Neff; Jennifer W. Harden; Kimberly P. Wickland

2008-01-01

Several fundamental but important factors controlling the feedback of boreal organic carbon (OC) to climate change were examined using a mechanistic model of soil OC dynamics, including the combined effects of temperature and moisture on the decomposition of OC and the factors controlling carbon quality and decomposition with depth. To estimate decomposition rates and...

EVALUATION OF THE MODEL ANTI-ANDROGEN FLUTAMIDE FOR ASSESSING THE MECHANISTIC BASIS OF RESPONSES TO AN ANDROGEN IN THE FATHEAD MINNOW (JOURNAL ARTICLE)

EPA Science Inventory

In this study we characterized the effects of flutamide, a model mammalian androgen receptor (AR) antagonist, on endocrine function in the fathead minnow (Pimephales promelas), a small fish species which is widely used for testing endocrine-disrupting chemicals (EDCs). Binding a...

Modelling algae-duckweed interaction under chemical pressure within a laboratory microcosm.

PubMed

Lamonica, Dominique; Clément, Bernard; Charles, Sandrine; Lopes, Christelle

2016-06-01

Contaminant effects on species are generally assessed with single-species bioassays. As a consequence, interactions between species that occur in ecosystems are not taken into account. To investigate the effects of contaminants on interacting species dynamics, our study describes the functioning of a 2-L laboratory microcosm with two species, the duckweed Lemna minor and the microalgae Pseudokirchneriella subcapitata, exposed to cadmium contamination. We modelled the dynamics of both species and their interactions using a mechanistic model based on coupled ordinary differential equations. The main processes occurring in this two-species microcosm were thus formalised, including growth and settling of algae, growth of duckweeds, interspecific competition between the two species and cadmium effects. We estimated model parameters by Bayesian inference, using simultaneously all the data issued from multiple laboratory experiments specifically conducted for this study. Cadmium concentrations ranged between 0 and 50 μg·L(-1). For all parameters of our model, we obtained biologically realistic values and reasonable uncertainties. Only duckweed dynamics was affected by interspecific competition, while algal dynamics was not impaired. Growth rate of both species decreased with cadmium concentration, as well as competition intensity showing that the interspecific competition pressure on duckweed decreased with cadmium concentration. This innovative combination of mechanistic modelling and model-guided experiments was successful to understand the algae-duckweed microcosm functioning without and with contaminant. This approach appears promising to include interactions between species when studying contaminant effects on ecosystem functioning. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

Modeling of hydrogen-air diffusion flame

NASA Technical Reports Server (NTRS)

Isaac, Kakkattukuzhy

1988-01-01

The present research objective is to determine the effects of contaminants on extinction limits of simple, well defined, counterflow Hydrogen 2-air diffusion flames, with combustion at 1 atmosphere. Results of extinction studies and other flame characterizations, with appropriate mechanistic modeling (presently underway), will be used to rationalize the observed effects of contamination over a reasonably wide range of diffusion flame conditions. The knowledge gained should help efforts to anticipate the effects of contaminants on combustion processes in Hydrogen 2-fueled scramjets.

THE EFFECTS OF NITROGEN LOADING AND FRESHWATER RESIDENCE TIME ON THE ESTUARINE ECOSYSTEM

EPA Science Inventory

A simple mechanistic model, designed to predict annual average concentrations of total nitrogen (TN) concentrations from nitrogen inputs and freshwater residence time in estuaries, was applied to data for several North American estuaries from previously published literature. The ...

SYMPOSIUM SESSION PROPOSAL: INCORPORATION OF MODE OF ACTION INTO MECHANISTICALLY-BASED QUANTITATIVE MODELS

EPA Science Inventory

The biological processes by which environmental pollutants induce adverse health effects is most likely regulated by complex interactions dependent upon the route of exposure, dose, kinetics of distribution, and multiple cellular responses. To further complicate deciphering thes...

20180312 - Mechanistic Modeling of Developmental Defects through Computational Embryology (SOT)

EPA Science Inventory

Significant advances in the genome sciences, in automated high-throughput screening (HTS), and in alternative methods for testing enable rapid profiling of chemical libraries for quantitative effects on diverse cellular activities. While a surfeit of HTS data and information is n...

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

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.

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.

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.

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.

Local variability mediates vulnerability of trout populations to land use and climate change

Treesearch

Brooke E. Penaluna; Jason B. Dunham; Steve F. Railsback; Ivan Arismendi; Sherri L. Johnson; Robert E. Bilby; Mohammad Safeeq; Arne E. Skaugset; James P. Meador

2015-01-01

Land use and climate change occur simultaneously around the globe. Fully understanding their separate and combined effects requires a mechanistic understanding at the local scale where their effects are ultimately realized. Here we applied an individual-based model of fish population dynamics to evaluate the role of local stream variability in modifying responses of...

On the use of satellite-based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa

NASA Astrophysics Data System (ADS)

Yamana, Teresa K.; Eltahir, Elfatih A. B.

2011-02-01

This paper describes the use of satellite-based estimates of rainfall to force the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a hydrology-based mechanistic model of malaria transmission. We first examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 h resolution. We then investigated whether HYDREMATS could be effectively forced by satellite-based estimates of rainfall instead of ground-based observations. The Climate Prediction Center morphing technique (CMORPH) precipitation estimates distributed by the National Oceanic and Atmospheric Administration are available at a 30 min temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results demonstrate that adjusted rainfall estimates from satellites can be used with a mechanistic model to accurately simulate the dynamics of mosquito populations.

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.

PREDICTING THE REGIONAL IMPACT OF OZONE AND PRECIPITATION ON THE GROWTH OF LOBLOLLY PINE AND YELLOW-POPLAR USING LINKED TREGRO AND ZELIG MODELS

EPA Science Inventory

To simulate the long-term effects of ozone on forests in the US, we linked TREGRO, a mechanistic model of an individual tree, to ZELIG, a forest stand model, to examine the response of forests to 5 ozone exposure regimes (0 to 100 ppm-hr SUM06 per year) in 100 year simulations. ...

Some Thoughts on Treasure-Keeping.

ERIC Educational Resources Information Center

O'Brien, Thomas C.

1989-01-01

Instead of studying children's knowing, American educators have applied policies and procedures from factories and assembly lines of the early 1900s. Three factory-oriented themes are paramount: mass production, cost effectiveness, and efficiency. This article suggests a Piagetian alternative to the present mechanistic model. Includes seven…

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.

Effect of water flow and chemical environment on microbiota growth and composition in the human colon.

PubMed

Cremer, Jonas; Arnoldini, Markus; Hwa, Terence

2017-06-20

The human gut harbors a dynamic microbial community whose composition bears great importance for the health of the host. Here, we investigate how colonic physiology impacts bacterial growth, which ultimately dictates microbiota composition. Combining measurements of bacterial physiology with analysis of published data on human physiology into a quantitative, comprehensive modeling framework, we show how water flow in the colon, in concert with other physiological factors, determine the abundances of the major bacterial phyla. Mechanistically, our model shows that local pH values in the lumen, which differentially affect the growth of different bacteria, drive changes in microbiota composition. It identifies key factors influencing the delicate regulation of colonic pH, including epithelial water absorption, nutrient inflow, and luminal buffering capacity, and generates testable predictions on their effects. Our findings show that a predictive and mechanistic understanding of microbial ecology in the gut is possible. Such predictive understanding is needed for the rational design of intervention strategies to actively control the microbiota.

Effect of water flow and chemical environment on microbiota growth and composition in the human colon

PubMed Central

Cremer, Jonas; Arnoldini, Markus; Hwa, Terence

2017-01-01

The human gut harbors a dynamic microbial community whose composition bears great importance for the health of the host. Here, we investigate how colonic physiology impacts bacterial growth, which ultimately dictates microbiota composition. Combining measurements of bacterial physiology with analysis of published data on human physiology into a quantitative, comprehensive modeling framework, we show how water flow in the colon, in concert with other physiological factors, determine the abundances of the major bacterial phyla. Mechanistically, our model shows that local pH values in the lumen, which differentially affect the growth of different bacteria, drive changes in microbiota composition. It identifies key factors influencing the delicate regulation of colonic pH, including epithelial water absorption, nutrient inflow, and luminal buffering capacity, and generates testable predictions on their effects. Our findings show that a predictive and mechanistic understanding of microbial ecology in the gut is possible. Such predictive understanding is needed for the rational design of intervention strategies to actively control the microbiota. PMID:28588144

Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Treesearch

Russell A. Parsons; William E. Mell; Peter McCauley

2011-01-01

Crownfire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (...

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

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

Effect of ingested lipids on drug dissolution and release with concurrent digestion: a modeling approach

PubMed Central

Buyukozturk, Fulden; Di Maio, Selena; Budil, David E.; Carrier, Rebecca L.

2014-01-01

Purpose To mechanistically study and model the effect of lipids, either from food or self-emulsifying drug delivery systems (SEDDS), on drug transport in the intestinal lumen. Methods Simultaneous lipid digestion, dissolution/release, and drug partitioning were experimentally studied and modeled for two dosing scenarios: solid drug with a food-associated lipid (soybean oil) and drug solubilized in a model SEDDS (soybean oil and Tween 80 at 1:1 ratio). Rate constants for digestion, permeability of emulsion droplets, and partition coefficients in micellar and oil phases were measured, and used to numerically solve the developed model. Results Strong influence of lipid digestion on drug release from SEDDS and solid drug dissolution into food-associated lipid emulsion were observed and predicted by the developed model. 90 minutes after introduction of SEDDS, there was 9% and 70% drug release in the absence and presence of digestion, respectively. However, overall drug dissolution in the presence of food-associated lipids occurred over a longer period than without digestion. Conclusion A systems-based mechanistic model incorporating simultaneous dynamic processes occurring upon dosing of drug with lipids enabled prediction of aqueous drug concentration profile. This model, once incorporated with a pharmacokinetic model considering processes of drug absorption and drug lymphatic transport in the presence of lipids, could be highly useful for quantitative prediction of impact of lipids on bioavailability of drugs. PMID:24234918

Experimental and modelling studies for the validation of the mechanistic basis of the Local Effect Model

NASA Astrophysics Data System (ADS)

Tommasino, F.

2016-03-01

This review will summarize results obtained in the recent years applying the Local Effect Model (LEM) approach to the study of basic radiobiological aspects, as for instance DNA damage induction and repair, and charged particle track structure. The promising results obtained using different experimental techniques and looking at different biological end points, support the relevance of the LEM approach for the description of radiation effects induced by both low- and high-LET radiation. Furthermore, they suggest that nowadays the appropriate combination of experimental and modelling tools can lead to advances in the understanding of several open issues in the field of radiation biology.

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…

Effect of Zn2+ binding and enzyme active site on the transition state for RNA 2′-O-transphosphorylation interpreted through kinetic isotope effects

PubMed Central

Chen, Haoyuan; Piccirilli, Joseph A.; Harris, Michael E.; York, Darrin M.

2016-01-01

Divalent metal ions, due to their ability to stabilize high concentrations of negative charge, are important for RNA folding and catalysis. Detailed models derived from the structures and kinetics of enzymes and from computational simulations have been developed. However, in most cases the specific catalytic modes involving metal ions and their mechanistic roles and effects on transition state structures remains controversial. Valuable information about the nature of the transition state is provided by measurement of kinetic isotope effects (KIEs). However, KIEs reflect changes in all bond vibrational modes that differ between the ground state and transition state. QM calculations are therefore essential for developing structural models of the transition state and evaluating mechanistic alternatives. Herein, we present computational models for Zn2+ binding to RNA 2′O-transphosphorylation reaction models that aid in the interpretation of KIE experiments. Different Zn2+ binding modes produce distinct KIE signatures, and one binding mode involving two zinc ions is in close agreement with KIEs measured for non-enzymatic catalysis by Zn2+ aquo ions alone. Interestingly, the KIE signatures in this specific model are also very close to those in RNase A catalysis. These results allow a quantitative connection to be made between experimental KIE measurements and transition state structure and bonding, and provide insight into RNA 2′O-transphosphorylation reactions catalyzed by metal ions and enzymes. PMID:25812974

Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.

PubMed

de Witte, Wilhelmus E A; Rottschäfer, Vivi; Danhof, Meindert; van der Graaf, Piet H; Peletier, Lambertus A; de Lange, Elizabeth C M

2018-05-18

Drug-target binding kinetics (as determined by association and dissociation rate constants, k on and k off ) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug-target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment-target binding kinetics (EC-TB) model were tested on either plasma (EC PL , TB PL and EC-TB PL ) or brain extracellular fluid (ECF) (EC ECF , TB ECF and EC-TB ECF ) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (Tmax TO ) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in Tmax TO (∆Tmax TO ) as a result of increasing dose revealed that ∆Tmax TO is decreasing towards zero if the k off is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆Tmax TO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of k on and k off , a target binding model should be considered more often for mechanistic modelling purposes.

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

Modeling approaches in avian conservation and the role of field biologists

USGS Publications Warehouse

Beissinger, Steven R.; Walters, J.R.; Catanzaro, D.G.; Smith, Kimberly G.; Dunning, J.B.; Haig, Susan M.; Noon, Barry; Stith, Bradley M.

2006-01-01

This review grew out of our realization that models play an increasingly important role in conservation but are rarely used in the research of most avian biologists. Modelers are creating models that are more complex and mechanistic and that can incorporate more of the knowledge acquired by field biologists. Such models require field biologists to provide more specific information, larger sample sizes, and sometimes new kinds of data, such as habitat-specific demography and dispersal information. Field biologists need to support model development by testing key model assumptions and validating models. The best conservation decisions will occur where cooperative interaction enables field biologists, modelers, statisticians, and managers to contribute effectively. We begin by discussing the general form of ecological models—heuristic or mechanistic, "scientific" or statistical—and then highlight the structure, strengths, weaknesses, and applications of six types of models commonly used in avian conservation: (1) deterministic single-population matrix models, (2) stochastic population viability analysis (PVA) models for single populations, (3) metapopulation models, (4) spatially explicit models, (5) genetic models, and (6) species distribution models. We end by considering their unique attributes, determining whether the assumptions that underlie the structure are valid, and testing the ability of the model to predict the future correctly.

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.

The effect of level of feeding, genetic merit, body condition score and age on biological parameters of a mammary gland model.

PubMed

Bryant, J R; Lopez-Villalobos, N; Holmes, C W; Pryce, J E; Pitman, G D; Davis, S R

2007-03-01

An evolutionary algorithm was applied to a mechanistic model of the mammary gland to find the parameter values that minimised the difference between predicted and actual lactation curves of milk yields in New Zealand Jersey cattle managed at different feeding levels. The effect of feeding level, genetic merit, body condition score at parturition and age on total lactation yields of milk, fat and protein, days in milk, live weight and evolutionary algorithm derived mammary gland parameters was then determined using a multiple regression model. The mechanistic model of the mammary gland was able to fit lactation curves that corresponded to actual lactation curves with a high degree of accuracy. The senescence rate of quiescent (inactive) alveoli was highest at the very low feeding level. The active alveoli population at peak lactation was highest at very low feeding levels, but lower nutritional status at this feeding level prevented high milk yields from being achieved. Genetic merit had a significant linear effect on the active alveoli population at peak and mid to late lactation, with higher values in animals, which had higher breeding values for milk yields. A type of genetic merit × feeding level scaling effect was observed for total yields of milk and fat, and total number of alveoli produced from conception until the end of lactation with the benefits of increases in genetic merit being greater at high feeding levels. A genetic merit × age scaling effect was observed for total lactation protein yields. Initial rates of differentiation of progenitor cells declined with age. Production levels of alveoli from conception to the end of lactation were lowest in 5- to 8-year-old animals; however, in these older animals, quiescent alveoli were reactivated more frequently. The active alveoli population at peak lactation and rates of active alveoli proceeding to quiescence were highest in animals of intermediate body condition scores of 4.0 to 5.0. The results illustrate the potential uses of a mechanistic model of the mammary gland to fit a lactation curve and to quantify the effects of feeding level, genetic merit, body condition score, and age on mammary gland dynamics throughout lactation.

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.

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.

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...

White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host

USGS Publications Warehouse

Verant, Michelle L.; Meteyer, Carol U.; Speakman, John R.; Cryan, Paul M.; Lorch, Jeffrey M.; Blehert, David S.

2014-01-01

Integrating these novel findings on the physiological changes that occur in early-stage WNS with those previously documented in late-stage infections, we propose a multi-stage disease progression model that mechanistically describes the pathologic and physiologic effects underlying mortality of WNS in hibernating bats. This model identifies testable hypotheses for better understanding this disease, knowledge that will be critical for defining effective disease mitigation strategies aimed at reducing morbidity and mortality that results from WNS.

Fast charging technique for high power LiFePO4 batteries: A mechanistic analysis of aging

NASA Astrophysics Data System (ADS)

Anseán, D.; Dubarry, M.; Devie, A.; Liaw, B. Y.; García, V. M.; Viera, J. C.; González, M.

2016-07-01

One of the major issues hampering the acceptance of electric vehicles (EVs) is the anxiety associated with long charging time. Hence, the ability to fast charging lithium-ion battery (LIB) systems is gaining notable interest. However, fast charging is not tolerated by all LIB chemistries because it affects battery functionality and accelerates its aging processes. Here, we investigate the long-term effects of multistage fast charging on a commercial high power LiFePO4-based cell and compare it to another cell tested under standard charging. Coupling incremental capacity (IC) and IC peak area analysis together with mechanistic model simulations ('Alawa' toolbox with harvested half-cell data), we quantify the degradation modes that cause aging of the tested cells. The results show that the proposed fast charging technique caused similar aging effects as standard charging. The degradation is caused by a linear loss of lithium inventory, coupled with a less degree of linear loss of active material on the negative electrode. This study validates fast charging as a feasible mean of operation for this particular LIB chemistry and cell architecture. It also illustrates the benefits of a mechanistic approach to understand cell degradation on commercial cells.

BYSTANDER EFFECTS, GENOMIC INSTABILITY, ADAPTIVE RESPONSE AND CANCER RISK ASSESSMENT FOR RADIATION AND CHEMICAL EXPOSURES

EPA Science Inventory

There is an increased interest in utilizing mechanistic data in support of the cancer risk assessment process for ionizing radiation and environmental chemical exposures. In this regard the use of biologically based dose-response models is particularly advocated. The aim is to pr...

Simulating Limb Formation in the U.S. EPA Virtual Embryo - Risk Assessment Project

EPA Science Inventory

The U.S. EPA’s Virtual Embryo project (v-Embryo™) is a computer model simulation of morphogenesis that integrates cell and molecular level data from mechanistic and in vitro assays with knowledge about normal development processes to assess in silico the effects of chemicals on d...

Modeling Rabbit Responses to Single and Multiple Aerosol ...

EPA Pesticide Factsheets

Journal Article Survival models are developed here to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple dose dataset to predict the probability of death through specifying dose-response functions and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) has an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed employ different underlying dose-response functions and use the assumption that, in a multiple dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this paper. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit datasets. More accurate survival models depend upon future development of dose-response datasets specifically designed to assess potential multiple dose effects on response and time-to-response. The process used in this paper to dev

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.

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.

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.

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

Substituent Effects in the Pyridinium Catalyzed Reduction of CO 2 to Methanol: Further Mechanistic Insights

DOE PAGES

Barton Cole, Emily E.; Baruch, Maor F.; L’Esperance, Robert P.; ...

2014-11-15

A series of substituted pyridiniums were examined for their catalytic ability to electrochemically reduce carbon dioxide to methanol. It is found that in general increased basicity of the nitrogen of the amine and higher LUMO energy of the pyridinium correlate with enhanced carbon dioxide reduction. The highest faradaic yield for methanol production at a platinum electrode was 39 ± 4 % for 4-aminopyridine compared to 22 ± 2 % for pyridine. However, 4-tertbutyl and 4-dimethylamino pyridine showed decreased catalytic behavior, contrary to the enhanced activity associated with the increased basicity and LUMO energy, and suggesting that steric effects also playmore » a significant role in the behavior of pyridinium electrocatalysts. As a result, mechanistic models for the the reaction of the pyridinium with carbon dioxide are considered.« 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

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.

The status and future of acupuncture mechanism research.

PubMed

Napadow, Vitaly; Ahn, Andrew; Longhurst, John; Lao, Lixing; Stener-Victorin, Elisabet; Harris, Richard; Langevin, Helene M

2008-09-01

On November 8-9, 2007, the Society for Acupuncture Research (SAR) hosted an international conference to mark the tenth anniversary of the landmark NIH [National Institutes of Health] Consensus Development Conference on Acupuncture. More than 300 acupuncture researchers, practitioners, students, funding agency personnel, and health policy analysts from 20 countries attended the SAR meeting held at the University of Maryland School of Medicine, Baltimore, MD. This paper summarizes important invited lectures in the area of basic and translational acupuncture research. Specific areas include the scientific assessment of acupuncture points and meridians, the neural mechanisms of cardiovascular regulation by acupuncture, mechanisms for electroacupuncture applied to persistent inflammation and pain, basic and translational research on acupuncture in gynecologic applications, the application of functional neuroimaging to acupuncture research with specific application to carpal-tunnel syndrome and fibromyalgia, and the association of the connective tissue system to acupuncture research. In summary, mechanistic models for acupuncture effects that have been investigated experimentally have focused on the effects of acupuncture needle stimulation on the nervous system, muscles, and connective tissue. These mechanistic models are not mutually exclusive. Iterative testing, expanding, and perhaps merging of such models will potentially lead to an incremental understanding of the effects of manual and electrical stimulation of acupuncture needles that is solidly rooted in physiology.

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

Rotary ultrasonic machining of CFRP: a mechanistic predictive model for cutting force.

PubMed

Cong, W L; Pei, Z J; Sun, X; Zhang, C L

2014-02-01

Cutting force is one of the most important output variables in rotary ultrasonic machining (RUM) of carbon fiber reinforced plastic (CFRP) composites. Many experimental investigations on cutting force in RUM of CFRP have been reported. However, in the literature, there are no cutting force models for RUM of CFRP. This paper develops a mechanistic predictive model for cutting force in RUM of CFRP. The material removal mechanism of CFRP in RUM has been analyzed first. The model is based on the assumption that brittle fracture is the dominant mode of material removal. CFRP micromechanical analysis has been conducted to represent CFRP as an equivalent homogeneous material to obtain the mechanical properties of CFRP from its components. Based on this model, relationships between input variables (including ultrasonic vibration amplitude, tool rotation speed, feedrate, abrasive size, and abrasive concentration) and cutting force can be predicted. The relationships between input variables and important intermediate variables (indentation depth, effective contact time, and maximum impact force of single abrasive grain) have been investigated to explain predicted trends of cutting force. Experiments are conducted to verify the model, and experimental results agree well with predicted trends from this model. Copyright © 2013 Elsevier B.V. All rights reserved.

On the closed form mechanistic modeling of milling: Specific cutting energy, torque, and power

NASA Astrophysics Data System (ADS)

Bayoumi, A. E.; Yücesan, G.; Hutton, D. V.

1994-02-01

Specific energy in metal cutting, defined as the energy expended in removing a unit volume of workpiece material, is formulated and determined using a previously developed closed form mechanistic force model for milling operations. Cutting power is computed from the cutting torque, cutting force, kinematics of the cutter, and the volumetric material removal rate. Closed form expressions for specific cutting energy were formulated and found to be functions of the process parameters: pressure and friction for both rake and flank surfaces and chip flow angle at the rake face of the tool. Friction is found to play a very important role in cutting torque and power. Experiments were carried out to determine the effects of feedrate, cutting speed, workpiece material, and flank wear land width on specific cutting energy. It was found that the specific cutting energy increases with a decrease in the chip thickness and with an increase in flank wear land.

The Effects of Lactobacillus acidophilus on the Intestinal Smooth Muscle Contraction through PKC/MLCK/MLC Signaling Pathway in TBI Mouse Model

PubMed Central

Fang, Huan; Zhu, Lina; Gao, Ning; Zhu, Jingci

2015-01-01

Clinical studies have shown that probiotics influence gastrointestinal motility. However, the molecular mechanisms by which probiotic Lactobacillus modulates intestinal motility in traumatic brain injury (TBI) mouse model have not been explored. In the present study, we provided evidence showing that treatment of TBI mice with Lactobacillus acidophilus significantly improved the terminal ileum villus morphology, restored the impaired interstitial cells of Cajal (ICC) and the disrupted ICC networks after TBI, and prevented TBI-mediated inhibition of contractile activity in intestinal smooth muscle. Mechanistically, the decreased concentration of MLCK, phospho-MLC20 and phospho-MYPT1 and increased concentration of MLCP and PKC were observed after TBI, and these events mediated by TBI were efficiently prevented by Lactobacillus acidophilus application. These findings may provide a novel mechanistic basis for the application of Lactobacillus acidophilus in the treatment of TBI. PMID:26030918

The Effects of Lactobacillus acidophilus on the Intestinal Smooth Muscle Contraction through PKC/MLCK/MLC Signaling Pathway in TBI Mouse Model.

PubMed

Sun, Bo; Hu, Chen; Fang, Huan; Zhu, Lina; Gao, Ning; Zhu, Jingci

2015-01-01

Clinical studies have shown that probiotics influence gastrointestinal motility. However, the molecular mechanisms by which probiotic Lactobacillus modulates intestinal motility in traumatic brain injury (TBI) mouse model have not been explored. In the present study, we provided evidence showing that treatment of TBI mice with Lactobacillus acidophilus significantly improved the terminal ileum villus morphology, restored the impaired interstitial cells of Cajal (ICC) and the disrupted ICC networks after TBI, and prevented TBI-mediated inhibition of contractile activity in intestinal smooth muscle. Mechanistically, the decreased concentration of MLCK, phospho-MLC20 and phospho-MYPT1 and increased concentration of MLCP and PKC were observed after TBI, and these events mediated by TBI were efficiently prevented by Lactobacillus acidophilus application. These findings may provide a novel mechanistic basis for the application of Lactobacillus acidophilus in the treatment of TBI.

On the evolution of specialization with a mechanistic underpinning in structured metapopulations.

PubMed

Nurmi, Tuomas; Parvinen, Kalle

2008-03-01

We analyze the evolution of specialization in resource utilization in a discrete-time metapopulation model using the adaptive dynamics approach. The local dynamics in the metapopulation are based on the Beverton-Holt model with mechanistic underpinnings. The consumer faces a trade-off in the abilities to consume two resources that are spatially heterogeneously distributed to patches that are prone to local catastrophes. We explore the factors favoring the spread of generalist or specialist strategies. Increasing fecundity or decreasing catastrophe probability favors the spread of the generalist strategy and increasing environmental heterogeneity enlarges the parameter domain where the evolutionary branching is possible. When there are no catastrophes, increasing emigration diminishes the parameter domain where the evolutionary branching may occur. Otherwise, the effect of emigration on evolutionary dynamics is non-monotonous: both small and large values of emigration probability favor the spread of the specialist strategies whereas the parameter domain where evolutionary branching may occur is largest when the emigration probability has intermediate values. We compare how different forms of spatial heterogeneity and different models of local growth affect the evolutionary dynamics. We show that even small changes in the resource dynamics may have outstanding evolutionary effects to the consumers.

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.

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...

Do rising temperatures always increase forest productivity? Interacting effects of temperature, precipitation, cloudiness and soil texture on tree species growth and competition

Treesearch

Eric J. Gustafson; Brian R. Miranda; Arjan M.G. De Bruijn; Brian R. Sturtevant; Mark E. Kubiske

2017-01-01

Forest landscape models (FLM) are increasingly used to project the effects of climate change on forested landscapes, yet most use phenomenological approaches with untested assumptions about future forest dynamics. We used a FLM that relies on first principles to mechanistically simulate growth (LANDIS-II with PnET-Succession) to systematically explore how landscapes...

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)...

Role of Thermochemical Decomposition in Energetic Material Initiation Sensitivity and Explosive Performance

DTIC Science & Technology

2007-02-05

Electronic excitation has been suggested as one contributing mechanistic step in a multiprocess detonation model [18], and such electronic...and, (b) Dick, J. J., Orientation Dependence of the Shock Initiation Sensitivity of PETN: A Steric Hindrance Model , Workshop on Desensitization of...Explosives and Propellants, Rijswijk, The Netherlands, 11-13 Nov 1991. [15] Piermarini, G. J., Block, S., Miller , P. J., Effects of Pressure on

Intraventricular flow alterations due to dyssynchronous wall motion

NASA Astrophysics Data System (ADS)

Pope, Audrey M.; Lai, Hong Kuan; Samaee, Milad; Santhanakrishnan, Arvind

2015-11-01

Roughly 30% of patients with systolic heart failure suffer from left ventricular dyssynchrony (LVD), in which mechanical discoordination of the ventricle walls leads to poor hemodynamics and suboptimal cardiac function. There is currently no clear mechanistic understanding of how abnormalities in septal-lateral (SL) wall motion affects left ventricle (LV) function, which is needed to improve the treatment of LVD using cardiac resynchronization therapy. We use an experimental flow phantom with an LV physical model to study mechanistic effects of SL wall motion delay on LV function. To simulate mechanical LVD, two rigid shafts were coupled to two segments (apical and mid sections) along the septal wall of the LV model. Flow through the LV model was driven using a piston pump, and stepper motors coupled to the above shafts were used to locally perturb the septal wall segments relative to the pump motion. 2D PIV was used to examine the intraventricular flow through the LV physical model. Alterations to SL delay results in a reduction in the kinetic energy (KE) of the flow field compared to synchronous SL motion. The effect of varying SL motion delay from 0% (synchronous) to 100% (out-of-phase) on KE and viscous dissipation will be presented. This research was supported by the Oklahoma Center for Advancement of Science and Technology (HR14-022).

REDUCING UNCERTAINTY IN RISK ASSESSMENT USING MECHANISTIC DATA: ENHANCING THE U.S. EPA DEVELOPMENTAL NEUROTOXICITY TESTING GUIDELINES

EPA Science Inventory

SUMMARY: Mechanistic data should provide the Agency with a more accurate basis to estimate risk than do the Agency’s default assumptions (10x uncertainty factors, etc.), thereby improving risk assessment decisions. NTD is providing mechanistic data for toxicant effects on two maj...

A preliminary study of mechanistic approach in pavement design to accommodate climate change effects

NASA Astrophysics Data System (ADS)

Harnaeni, S. R.; Pramesti, F. P.; Budiarto, A.; Setyawan, A.

2018-03-01

Road damage is caused by some factors, including climate changes, overload, and inappropriate procedure for material and development process. Meanwhile, climate change is a phenomenon which cannot be avoided. The effects observed include air temperature rise, sea level rise, rainfall changes, and the intensity of extreme weather phenomena. Previous studies had shown the impacts of climate changes on road damage. Therefore, several measures to anticipate the damage should be considered during the planning and construction in order to reduce the cost of road maintenance. There are three approaches generally applied in the design of flexible pavement thickness, namely mechanistic approach, mechanistic-empirical (ME) approach and empirical approach. The advantages of applying mechanistic approach or mechanistic-empirical (ME) approaches are its efficiency and reliability in the design of flexible pavement thickness as well as its capacity to accommodate climate changes in compared to empirical approach. However, generally, the design of flexible pavement thickness in Indonesia still applies empirical approach. This preliminary study aimed to emphasize the importance of the shifting towards a mechanistic approach in the design of flexible pavement thickness.

An Illness of Power: Gender and the Social Causes of Depression.

PubMed

Neitzke, Alex B

2016-03-01

There is considerable discourse surrounding the disproportionate diagnosis of women with depression as compared to men, often times cited at a rate around 2:1. While this disparity clearly draws attention to gender, a focus on gender tends to fall away in the study and treatment of depression in neuroscience and psychiatry, which largely understand its workings in mechanistic terms of brain chemistry and neurological processes. I first consider how this brain-centered biological model for depression came about. I then argue that the authoritative scientific models for disorder have serious consequences for those diagnosed. Finally, I argue that mechanistic biological models of depression have the effect of silencing women and marginalizing or preventing the examination of social-structural causes of depression, like gender oppression, and therein contribute to the ideological reproduction of oppressive social relations. I argue that depression is best understood in terms of systems of power, including gender, and where a given individual is situated within such social relations. The result is a model of depression that accounts for the influence of biological, psychological, and social factors.

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.

Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

NASA Astrophysics Data System (ADS)

Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.

2017-12-01

Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream measurements.

Combining Solvent Isotope Effects with Substrate Isotope Effects in Mechanistic Studies of Alcohol and Amine Oxidation by Enzymes*

PubMed Central

Fitzpatrick, Paul F.

2014-01-01

Oxidation of alcohols and amines is catalyzed by multiple families of flavin-and pyridine nucleotide-dependent enzymes. Measurement of solvent isotope effects provides a unique mechanistic probe of the timing of the cleavage of the OH and NH bonds, necessary information for a complete description of the catalytic mechanism. The inherent ambiguities in interpretation of solvent isotope effects can be significantly decreased if isotope effects arising from isotopically labeled substrates are measured in combination with solvent isotope effects. The application of combined solvent and substrate (mainly deuterium) isotope effects to multiple enzymes is described here to illustrate the range of mechanistic insights that such an approach can provide. PMID:25448013

Effect of Zn2+ binding and enzyme active site on the transition state for RNA 2'-O-transphosphorylation interpreted through kinetic isotope effects.

PubMed

Chen, Haoyuan; Piccirilli, Joseph A; Harris, Michael E; York, Darrin M

2015-11-01

Divalent metal ions, due to their ability to stabilize high concentrations of negative charge, are important for RNA folding and catalysis. Detailed models derived from the structures and kinetics of enzymes and from computational simulations have been developed. However, in most cases the specific catalytic modes involving metal ions and their mechanistic roles and effects on transition state structures remain controversial. Valuable information about the nature of the transition state is provided by measurement of kinetic isotope effects (KIEs). However, KIEs reflect changes in all bond vibrational modes that differ between the ground state and transition state. QM calculations are therefore essential for developing structural models of the transition state and evaluating mechanistic alternatives. Herein, we present computational models for Zn2+ binding to RNA 2'O-transphosphorylation reaction models that aid in the interpretation of KIE experiments. Different Zn2+ binding modes produce distinct KIE signatures, and one binding mode involving two zinc ions is in close agreement with KIEs measured for non-enzymatic catalysis by Zn2+ aquo ions alone. Interestingly, the KIE signatures in this specific model are also very close to those in RNase A catalysis. These results allow a quantitative connection to be made between experimental KIE measurements and transition state structure and bonding, and provide insight into RNA 2'O-ransphosphorylation reactions catalyzed by metal ions and enzymes. This article is part of a Special Issue entitled: Enzyme Transition States from Theory and Experiment. Copyright © 2015. Published by Elsevier B.V.

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

Introductory Biology Students’ Conceptual Models and Explanations of the Origin of Variation

PubMed Central

Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess understanding of the origin of variation. By midterm, only a small percentage of students articulated complete and accurate representations of the origin of variation in their models. Targeted feedback was offered through activities requiring students to critically evaluate peers’ models. At semester's end, a substantial proportion of students significantly improved their representation of how variation arises (though one-third still did not include mutation in their models). Students’ written explanations of the origin of variation were mostly consistent with their models, although less effective than models in conveying mechanistic reasoning. This study contributes evidence that articulating the genetic origin of variation is particularly challenging for learners and may require multiple cycles of instruction, assessment, and feedback. To support meaningful learning of the origin of variation, we advocate instruction that explicitly integrates multiple scales of biological organization, assessment that promotes and reveals mechanistic and causal reasoning, and practice with explanatory models with formative feedback. PMID:25185235

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...

Modeling Physiological Processes That Relate Toxicant Exposure and Bacterial Population Dynamics

PubMed Central

Klanjscek, Tin; Nisbet, Roger M.; Priester, John H.; Holden, Patricia A.

2012-01-01

Quantifying effects of toxicant exposure on metabolic processes is crucial to predicting microbial growth patterns in different environments. Mechanistic models, such as those based on Dynamic Energy Budget (DEB) theory, can link physiological processes to microbial growth. Here we expand the DEB framework to include explicit consideration of the role of reactive oxygen species (ROS). Extensions considered are: (i) additional terms in the equation for the “hazard rate” that quantifies mortality risk; (ii) a variable representing environmental degradation; (iii) a mechanistic description of toxic effects linked to increase in ROS production and aging acceleration, and to non-competitive inhibition of transport channels; (iv) a new representation of the “lag time” based on energy required for acclimation. We estimate model parameters using calibrated Pseudomonas aeruginosa optical density growth data for seven levels of cadmium exposure. The model reproduces growth patterns for all treatments with a single common parameter set, and bacterial growth for treatments of up to 150 mg(Cd)/L can be predicted reasonably well using parameters estimated from cadmium treatments of 20 mg(Cd)/L and lower. Our approach is an important step towards connecting levels of biological organization in ecotoxicology. The presented model reveals possible connections between processes that are not obvious from purely empirical considerations, enables validation and hypothesis testing by creating testable predictions, and identifies research required to further develop the theory. PMID:22328915

Mathematical modeling of drug release from lipid dosage forms.

PubMed

Siepmann, J; Siepmann, F

2011-10-10

Lipid dosage forms provide an interesting potential for controlled drug delivery. In contrast to frequently used poly(ester) based devices for parenteral administration, they do not lead to acidification upon degradation and potential drug inactivation, especially in the case of protein drugs and other acid-labile active agents. The aim of this article is to give an overview on the current state of the art of mathematical modeling of drug release from this type of advanced drug delivery systems. Empirical and semi-empirical models are described as well as mechanistic theories, considering diffusional mass transport, potentially limited drug solubility and the leaching of other, water-soluble excipients into the surrounding bulk fluid. Various practical examples are given, including lipid microparticles, beads and implants, which can successfully be used to control the release of an incorporated drug during periods ranging from a few hours up to several years. The great benefit of mechanistic mathematical theories is the possibility to quantitatively predict the effects of different formulation parameters and device dimensions on the resulting drug release kinetics. Thus, in silico simulations can significantly speed up product optimization. This is particularly useful if long release periods (e.g., several months) are targeted, since experimental trial-and-error studies are highly time-consuming in these cases. In the future it would be highly desirable to combine mechanistic theories with the quantitative description of the drug fate in vivo, ideally including the pharmacodynamic efficacy of the treatments. Copyright © 2011 Elsevier B.V. All rights reserved.

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

PubMed Central

Guis, Helene; Caminade, Cyril; Calvete, Carlos; Morse, Andrew P.; Tran, Annelise; Baylis, Matthew

2012-01-01

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases. PMID:21697167

FXR activation by obeticholic acid or nonsteroidal agonists induces a human-like lipoprotein cholesterol change in mice with humanized chimeric liver.

PubMed

Papazyan, Romeo; Liu, Xueqing; Liu, Jingwen; Dong, Bin; Plummer, Emily M; Lewis, Ronald D; Roth, Jonathan D; Young, Mark A

2018-06-01

Obeticholic acid (OCA) is a selective farnesoid X receptor (FXR) agonist that regulates bile acid and lipid metabolism. FXR activation induces distinct changes in circulating cholesterol among animal models and humans. The mechanistic basis of these effects has been elusive because of difficulties in studying lipoprotein homeostasis in mice, which predominantly package circulating cholesterol in HDLs. Here, we tested the effects of OCA in chimeric mice whose livers are mostly composed (≥80%) of human hepatocytes. Chimeric mice exhibited a human-like ratio of serum LDL cholesterol (LDL-C) to HDL cholesterol (HDL-C) at baseline. OCA treatment in chimeric mice increased circulating LDL-C and decreased circulating HDL-C levels, demonstrating that these mice closely model the cholesterol effects of FXR activation in humans. Mechanistically, OCA treatment increased hepatic cholesterol in chimeric mice but not in control mice. This increase correlated with decreased SREBP-2 activity and target gene expression, including a significant reduction in LDL receptor protein. Cotreatment with atorvastatin reduced total cholesterol, rescued LDL receptor protein levels, and normalized serum LDL-C. Treatment with two clinically relevant nonsteroidal FXR agonists elicited similar lipoprotein and hepatic changes in chimeric mice, suggesting that the increase in circulating LDL-C is a class effect of FXR activation.

Minoxidil is a potential neuroprotective drug for paclitaxel-induced peripheral neuropathy

PubMed Central

Chen, Yi-Fan; Chen, Li-Hsien; Yeh, Yu-Min; Wu, Pei-Ying; Chen, Yih-Fung; Chang, Lian-Yun; Chang, Jang-Yang; Shen, Meng-Ru

2017-01-01

Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of cancer treatment. No medication has been shown to be effective in the treatment of CIPN. This study aims to integrate the image-based high-content screening, mouse behavior models and mechanistic cell-based assays to discover potential neuroprotective drugs. Among screened compounds, minoxidil showed the most potent neuroprotective effect against paclitaxel, with regard to neurite outgrowth of dorsal root ganglia (DRG). Minoxidil protected mice from thermal insensitivity and alleviated mechanical allodynia in paclitaxel-treated mice. The ultrastructure and quantified G-ratio of myelin integrity of sciatic nerve tissues supported the observations in mouse behavioral tests. The mechanistic study on DRG neurons suggested that minoxidil suppressed neuroinflammation and remodeled the dysregulation of intracellular calcium homeostasis provoked by paclitaxel. Importantly, minoxidil showed a synergistic anti-tumor effect with paclitaxel both in tumor xenograft models of cervical and breast cancer. Interestingly, the quantitative assays on hair length and hair growth both exhibited that minoxidil significantly improved the hair quality after chemotherapy. Since minoxidil is a drug approved by the Food and Drug Administration (FDA), the safety and biocompatibility are well documented. The immediate next step is to launch an early-stage clinical trial intending to prevent CIPN by minoxidil. PMID:28349969

Electroacupuncture in conscious free-moving mice reduces pain by ameliorating peripheral and central nociceptive mechanisms

PubMed Central

Wang, Ying; Lei, Jianxun; Gupta, Mihir; Peng, Fei; Lam, Sarah; Jha, Ritu; Raduenz, Ellis; Beitz, Al J.; Gupta, Kalpna

2016-01-01

Integrative approaches such as electroacupuncture, devoid of drug effects are gaining prominence for treating pain. Understanding the mechanisms of electroacupuncture induced analgesia would benefit chronic pain conditions such as sickle cell disease (SCD), for which patients may require opioid analgesics throughout life. Mouse models are instructive in developing a mechanistic understanding of pain, but the anesthesia/restraint required to administer electroacupuncture may alter the underlying mechanisms. To overcome these limitations, we developed a method to perform electroacupuncture in conscious, freely moving, unrestrained mice. Using this technique we demonstrate a significant analgesic effect in transgenic mouse models of SCD and cancer as well as complete Freund’s adjuvant-induced pain. We demonstrate a comprehensive antinociceptive effect on mechanical, cold and deep tissue hyperalagesia in both genders. Interestingly, individual mice showed a variable response to electroacupuncture, categorized into high-, moderate-, and non-responders. Mechanistically, electroacupuncture significantly ameliorated inflammatory and nociceptive mediators both peripherally and centrally in sickle mice correlative to the antinociceptive response. Application of sub-optimal doses of morphine in electroacupuncture-treated moderate-responders produced equivalent antinociception as obtained in high-responders. Electroacupuncture in conscious freely moving mice offers an effective approach to develop a mechanism-based understanding of analgesia devoid of the influence of anesthetics or restraints. PMID:27687125

Electroacupuncture in conscious free-moving mice reduces pain by ameliorating peripheral and central nociceptive mechanisms.

PubMed

Wang, Ying; Lei, Jianxun; Gupta, Mihir; Peng, Fei; Lam, Sarah; Jha, Ritu; Raduenz, Ellis; Beitz, Al J; Gupta, Kalpna

2016-09-30

Integrative approaches such as electroacupuncture, devoid of drug effects are gaining prominence for treating pain. Understanding the mechanisms of electroacupuncture induced analgesia would benefit chronic pain conditions such as sickle cell disease (SCD), for which patients may require opioid analgesics throughout life. Mouse models are instructive in developing a mechanistic understanding of pain, but the anesthesia/restraint required to administer electroacupuncture may alter the underlying mechanisms. To overcome these limitations, we developed a method to perform electroacupuncture in conscious, freely moving, unrestrained mice. Using this technique we demonstrate a significant analgesic effect in transgenic mouse models of SCD and cancer as well as complete Freund's adjuvant-induced pain. We demonstrate a comprehensive antinociceptive effect on mechanical, cold and deep tissue hyperalagesia in both genders. Interestingly, individual mice showed a variable response to electroacupuncture, categorized into high-, moderate-, and non-responders. Mechanistically, electroacupuncture significantly ameliorated inflammatory and nociceptive mediators both peripherally and centrally in sickle mice correlative to the antinociceptive response. Application of sub-optimal doses of morphine in electroacupuncture-treated moderate-responders produced equivalent antinociception as obtained in high-responders. Electroacupuncture in conscious freely moving mice offers an effective approach to develop a mechanism-based understanding of analgesia devoid of the influence of anesthetics or restraints.

Wear-dependent specific coefficients in a mechanistic model for turning of nickel-based superalloy with ceramic tools

NASA Astrophysics Data System (ADS)

López de Lacalle, Luis Norberto; Urbicain Pelayo, Gorka; Fernández-Valdivielso, Asier; Alvarez, Alvaro; González, Haizea

2017-09-01

Difficult to cut materials such as nickel and titanium alloys are used in the aeronautical industry, the former alloys due to its heat-resistant behavior and the latter for the low weight - high strength ratio. Ceramic tools made out alumina with reinforce SiC whiskers are a choice in turning for roughing and semifinishing workpiece stages. Wear rate is high in the machining of these alloys, and consequently cutting forces tends to increase along one operation. This paper establishes the cutting force relation between work-piece and tool in the turning of such difficult-to-cut alloys by means of a mechanistic cutting force model that considers the tool wear effect. The cutting force model demonstrates the force sensitivity to the cutting engagement parameters (ap, f) when using ceramic inserts and wear is considered. Wear is introduced through a cutting time factor, being useful in real conditions taking into account that wear quickly appears in alloys machining. A good accuracy in the cutting force model coefficients is the key issue for an accurate prediction of turning forces, which could be used as criteria for tool replacement or as input for chatter or other models.

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.

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

PubMed Central

He, Xin; Samee, Md. Abul Hassan; Blatti, Charles; Sinha, Saurabh

2010-01-01

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences. PMID:20862354

Avian models for toxicity testing

USGS Publications Warehouse

Hill, E.F.; Hoffman, D.J.

1984-01-01

The use of birds as test models in experimental and environmental toxicology as related to health effects is reviewed, and an overview of descriptive tests routinely used in wildlife toxicology is provided. Toxicologic research on birds may be applicable to human health both directly by their use as models for mechanistic and descriptive studies and indirectly as monitors of environmental quality. Topics include the use of birds as models for study of teratogenesis and embryotoxicity, neurotoxicity, behavior, trends of environmental pollution, and for use in predictive wildlife toxicology. Uses of domestic and wild-captured birds are discussed.

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.

Models of regeneration, tree growth, and current and potential ranges of tree and mammal species in the Eastern U.S.

Treesearch

Elaine K. Sutherland; Louis R. Iverson; Daniel A. Yaussy; Charles T. Scott; Betsy J. Hale; Anantha Prasad; Mark Schwartz; Hope R. Barrett

1997-01-01

An environmentally responsive, mechanistic regeneration simulator should simulate important ecological relationships and disturbance effects. Development of such a regeneration simulator is complex because of the many attributes that characterize reproductive strategies and the importance of forest history and disturbance in determining the composition of the next...

High-fat, high-fructose, high-cholesterol feeding causes severe NASH and cecal microbiota dysbiosis in juvenile Ossabaw swine

USDA-ARS?s Scientific Manuscript database

Pediatric obesity and nonalcoholic steatohepatitis (NASH) are on the rise in industrialized countries, yet our ability to mechanistically examine this relationship is limited by the lack of a suitable higher animal models. Here, we examined the effects of high-fat, high-fructose corn syrup, high-cho...

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.

A Modeling and Experimental Investigation of the Effects of Antigen Density, Binding Affinity, and Antigen Expression Ratio on Bispecific Antibody Binding to Cell Surface Targets*

PubMed Central

Rhoden, John J.; Dyas, Gregory L.

2016-01-01

Despite the increasing number of multivalent antibodies, bispecific antibodies, fusion proteins, and targeted nanoparticles that have been generated and studied, the mechanism of multivalent binding to cell surface targets is not well understood. Here, we describe a conceptual and mathematical model of multivalent antibody binding to cell surface antigens. Our model predicts that properties beyond 1:1 antibody:antigen affinity to target antigens have a strong influence on multivalent binding. Predicted crucial properties include the structure and flexibility of the antibody construct, the target antigen(s) and binding epitope(s), and the density of antigens on the cell surface. For bispecific antibodies, the ratio of the expression levels of the two target antigens is predicted to be critical to target binding, particularly for the lower expressed of the antigens. Using bispecific antibodies of different valencies to cell surface antigens including MET and EGF receptor, we have experimentally validated our modeling approach and its predictions and observed several nonintuitive effects of avidity related to antigen density, target ratio, and antibody affinity. In some biological circumstances, the effect we have predicted and measured varied from the monovalent binding interaction by several orders of magnitude. Moreover, our mathematical framework affords us a mechanistic interpretation of our observations and suggests strategies to achieve the desired antibody-antigen binding goals. These mechanistic insights have implications in antibody engineering and structure/activity relationship determination in a variety of biological contexts. PMID:27022022

Mechanistic link between uptake of sulfonamides and bacteriostatic effect: model development and application to experimental data from two soil microorganisms.

PubMed

Focks, Andreas; Klasmeier, Jörg; Matthies, Michael

2010-07-01

Sulfonamides (SA) are antibiotic compounds that are widely used as human and veterinary pharmaceuticals. They are not rapidly biodegradable and have been detected in various environmental compartments. Effects of sulfonamides on microbial endpoints in soil have been reported from laboratory incubation studies. Sulfonamides inhibit the growth of sensitive microorganisms by competitive binding to the dihydropteroate-synthase (DHPS) enzyme of folic acid production. A mathematical model was developed that relates the extracellular SA concentration to the inhibition of the relative bacterial growth rate. Two factors--the anionic accumulation factor (AAF) and the cellular affinity factor (CAF)--determine the effective concentration of an SA. The AAF describes the SA uptake into bacterial cells and varies with both the extra- and intracellular pH values and with the acidic pKa value of an SA. The CAF subsumes relevant cellular and enzyme properties, and is directly proportional to the DHPS affinity constant for an SA. Based on the model, a mechanistic dose-response relationship is developed and evaluated against previously published data, where differences in the responses of Pseudomonas aeruginosa and Panthoea agglomerans toward changing medium pH values were found, most likely as a result of their diverse pH regulation. The derived dose-response relationship explains the pH and pKa dependency of mean effective concentration values (EC50) of eight SA and two soil bacteria based on AAF and CAF values. The mathematical model can be used to extrapolate sulfonamide effects to other pH values and to calculate the CAF as a pH-independent measure for the SA effects on microbial growth. Copyright (c) 2010 SETAC.

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.

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.

Constrained variability of modeled T:ET ratio across biomes

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Pappas, Christoforos

2017-07-01

A large variability (35-90%) in the ratio of transpiration to total evapotranspiration (referred here as T:ET) across biomes or even at the global scale has been documented by a number of studies carried out with different methodologies. Previous empirical results also suggest that T:ET does not covary with mean precipitation and has a positive dependence on leaf area index (LAI). Here we use a mechanistic ecohydrological model, with a refined process-based description of evaporation from the soil surface, to investigate the variability of T:ET across biomes. Numerical results reveal a more constrained range and higher mean of T:ET (70 ± 9%, mean ± standard deviation) when compared to observation-based estimates. T:ET is confirmed to be independent from mean precipitation, while it is found to be correlated with LAI seasonally but uncorrelated across multiple sites. Larger LAI increases evaporation from interception but diminishes ground evaporation with the two effects largely compensating each other. These results offer mechanistic model-based evidence to the ongoing research about the patterns of T:ET and the factors influencing its magnitude across biomes.

Modelling insights on the partition of evapotranspiration components across biomes

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Pappas, Christoforos

2017-04-01

Recent studies using various methodologies have found a large variability (from 35 to 90%) in the ratio of transpiration to total evapotranspiration (denoted as T:ET) across biomes or even at the global scale. Concurrently, previous results suggest that T:ET is independent of mean precipitation and has a positive correlation with Leaf Area Index (LAI). We used the mechanistic ecohydrological model, T&C, with a refined process-based description of soil resistance and a detailed treatment of canopy biophysics and ecophysiology, to investigate T:ET across multiple biomes. Contrary to observation-based estimates, simulation results highlight a well-constrained range of mean T:ET across biomes that is also robust to perturbations of the most sensitive parameters. Simulated T:ET was confirmed to be independent of average precipitation, while it was found to be uncorrelated with LAI across biomes. Higher values of LAI increase evaporation from interception but suppress ground evaporation with the two effects largely cancelling each other in many sites. These results offer mechanistic, model-based, evidence to the ongoing research about the range of T:ET and the factors affecting its magnitude across biomes.

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.

Development of Novel Antibiotic Lysocin E Identified by Silkworm Infection Model.

PubMed

Hamamoto, Hiroshi; Sekimizu, Kazuhisa

2017-01-01

In this symposium, we reported the identification and mechanistic analysis of a novel antibiotic named lysocin E. Lysocin E was identified by screening for therapeutic effectiveness in a silkworm Staphylococcus aureus infection model. The advantages of the silkworm infection model for screening and purification of antibiotics from the culture supernatant of soil bacteria are: 1) low cost; 2) no ethical issues; 3) convenient for evaluation of the therapeutic effectiveness of antibiotics; and 4) pharmacokinetics similar to those of mammals. Lysocin E has remarkable features compared with known antibiotics such as a novel mechanism of action and target. Here, we summarize our reports presented in this symposium.

The importance of mechanisms for the evolution of cooperation

PubMed Central

van den Berg, Pieter; Weissing, Franz J.

2015-01-01

Studies aimed at explaining the evolution of phenotypic traits have often solely focused on fitness considerations, ignoring underlying mechanisms. In recent years, there has been an increasing call for integrating mechanistic perspectives in evolutionary considerations, but it is not clear whether and how mechanisms affect the course and outcome of evolution. To study this, we compare four mechanistic implementations of two well-studied models for the evolution of cooperation, the Iterated Prisoner's Dilemma (IPD) game and the Iterated Snowdrift (ISD) game. Behavioural strategies are either implemented by a 1 : 1 genotype–phenotype mapping or by a simple neural network. Moreover, we consider two different scenarios for the effect of mutations. The same set of strategies is feasible in all four implementations, but the probability that a given strategy arises owing to mutation is largely dependent on the behavioural and genetic architecture. Our individual-based simulations show that this has major implications for the evolutionary outcome. In the ISD, different evolutionarily stable strategies are predominant in the four implementations, while in the IPD each implementation creates a characteristic dynamical pattern. As a consequence, the evolved average level of cooperation is also strongly dependent on the underlying mechanism. We argue that our findings are of general relevance for the evolution of social behaviour, pleading for the integration of a mechanistic perspective in models of social evolution. PMID:26246554

Life at the Common Denominator: Mechanistic and Quantitative Biology for the Earth and Space Sciences

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.

2010-01-01

The remarkable challenges and possibilities of the coming few decades will compel the biogeochemical and astrobiological sciences to characterize the interactions between biology and its environment in a fundamental, mechanistic, and quantitative fashion. The clear need for integrative and scalable biology-environment models is exemplified in the Earth sciences by the challenge of effectively addressing anthropogenic global change, and in the space sciences by the challenge of mounting a well-constrained yet sufficiently adaptive and inclusive search for life beyond Earth. Our understanding of the life-planet interaction is still, however, largely empirical. A variety of approaches seek to move from empirical to mechanistic descriptions. One approach focuses on the relationship between biology and energy, which is at once universal (all life requires energy), unique (life manages energy flow in a fashion not seen in abiotic systems), and amenable to characterization and quantification in thermodynamic terms. Simultaneously, a focus on energy flow addresses a critical point of interface between life and its geological, chemical, and physical environment. Characterizing and quantifying this relationship for life on Earth will support the development of integrative and predictive models for biology-environment dynamics. Understanding this relationship at its most fundamental level holds potential for developing concepts of habitability and biosignatures that can optimize astrobiological exploration strategies and are extensible to all life.

Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits.

PubMed

Pawar, Samraat; Dell, Anthony I; Savage, Van M; Knies, Jennifer L

2016-02-01

Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ~0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

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.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet.

PubMed

Helmlinger, Gabriel; Al-Huniti, Nidal; Aksenov, Sergey; Peskov, Kirill; Hallow, Karen M; Chu, Lulu; Boulton, David; Eriksson, Ulf; Hamrén, Bengt; Lambert, Craig; Masson, Eric; Tomkinson, Helen; Stanski, Donald

2017-11-15

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D. Copyright © 2017 Elsevier B.V. All rights reserved.

A mechanistic model for electricity consumption on dairy farms: definition, validation, and demonstration.

PubMed

Upton, J; Murphy, M; Shalloo, L; Groot Koerkamp, P W G; De Boer, I J M

2014-01-01

Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Optimal Chemotherapy for Leukemia: A Model-Based Strategy for Individualized Treatment

PubMed Central

Jayachandran, Devaraj; Rundell, Ann E.; Hannemann, Robert E.; Vik, Terry A.; Ramkrishna, Doraiswami

2014-01-01

Acute Lymphoblastic Leukemia, commonly known as ALL, is a predominant form of cancer during childhood. With the advent of modern healthcare support, the 5-year survival rate has been impressive in the recent past. However, long-term ALL survivors embattle several treatment-related medical and socio-economic complications due to excessive and inordinate chemotherapy doses received during treatment. In this work, we present a model-based approach to personalize 6-Mercaptopurine (6-MP) treatment for childhood ALL with a provision for incorporating the pharmacogenomic variations among patients. Semi-mechanistic mathematical models were developed and validated for i) 6-MP metabolism, ii) red blood cell mean corpuscular volume (MCV) dynamics, a surrogate marker for treatment efficacy, and iii) leukopenia, a major side-effect. With the constraint of getting limited data from clinics, a global sensitivity analysis based model reduction technique was employed to reduce the parameter space arising from semi-mechanistic models. The reduced, sensitive parameters were used to individualize the average patient model to a specific patient so as to minimize the model uncertainty. Models fit the data well and mimic diverse behavior observed among patients with minimum parameters. The model was validated with real patient data obtained from literature and Riley Hospital for Children in Indianapolis. Patient models were used to optimize the dose for an individual patient through nonlinear model predictive control. The implementation of our approach in clinical practice is realizable with routinely measured complete blood counts (CBC) and a few additional metabolite measurements. The proposed approach promises to achieve model-based individualized treatment to a specific patient, as opposed to a standard-dose-for-all, and to prescribe an optimal dose for a desired outcome with minimum side-effects. PMID:25310465

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.

Biomechanical assessment and clinical analysis of different intramedullary nailing systems for oblique fractures.

PubMed

Alierta, J A; Pérez, M A; Seral, B; García-Aznar, J M

2016-09-01

The aim of this study is to evaluate the fracture union or non-union for a specific patient that presented oblique fractures in tibia and fibula, using a mechanistic-based bone healing model. Normally, this kind of fractures can be treated through an intramedullary nail using two possible configurations that depends on the mechanical stabilisation: static and dynamic. Both cases are simulated under different fracture geometries in order to understand the effect of the mechanical stabilisation on the fracture healing outcome. The results of both simulations are in good agreement with previous clinical experience. From the results, it is demonstrated that the dynamization of the fracture improves healing in comparison with a static or rigid fixation of the fracture. This work shows the versatility and potential of a mechanistic-based bone healing model to predict the final outcome (union, non-union, delayed union) of realistic 3D fractures where even more than one bone is involved.

Identifiability, reducibility, and adaptability in allosteric macromolecules.

PubMed

Bohner, Gergő; Venkataraman, Gaurav

2017-05-01

The ability of macromolecules to transduce stimulus information at one site into conformational changes at a distant site, termed "allostery," is vital for cellular signaling. Here, we propose a link between the sensitivity of allosteric macromolecules to their underlying biophysical parameters, the interrelationships between these parameters, and macromolecular adaptability. We demonstrate that the parameters of a canonical model of the mSlo large-conductance Ca 2+ -activated K + (BK) ion channel are non-identifiable with respect to the equilibrium open probability-voltage relationship, a common functional assay. We construct a reduced model with emergent parameters that are identifiable and expressed as combinations of the original mechanistic parameters. These emergent parameters indicate which coordinated changes in mechanistic parameters can leave assay output unchanged. We predict that these coordinated changes are used by allosteric macromolecules to adapt, and we demonstrate how this prediction can be tested experimentally. We show that these predicted parameter compensations are used in the first reported allosteric phenomena: the Bohr effect, by which hemoglobin adapts to varying pH. © 2017 Bohner and Venkataraman.

Identifiability, reducibility, and adaptability in allosteric macromolecules

PubMed Central

Bohner, Gergő

2017-01-01

The ability of macromolecules to transduce stimulus information at one site into conformational changes at a distant site, termed “allostery,” is vital for cellular signaling. Here, we propose a link between the sensitivity of allosteric macromolecules to their underlying biophysical parameters, the interrelationships between these parameters, and macromolecular adaptability. We demonstrate that the parameters of a canonical model of the mSlo large-conductance Ca2+-activated K+ (BK) ion channel are non-identifiable with respect to the equilibrium open probability-voltage relationship, a common functional assay. We construct a reduced model with emergent parameters that are identifiable and expressed as combinations of the original mechanistic parameters. These emergent parameters indicate which coordinated changes in mechanistic parameters can leave assay output unchanged. We predict that these coordinated changes are used by allosteric macromolecules to adapt, and we demonstrate how this prediction can be tested experimentally. We show that these predicted parameter compensations are used in the first reported allosteric phenomena: the Bohr effect, by which hemoglobin adapts to varying pH. PMID:28416647

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...

Atrial arrhythmogenicity of KCNJ2 mutations in short QT syndrome: Insights from virtual human atria

PubMed Central

El Harchi, Aziza; Hancox, Jules C.

2017-01-01

Gain-of-function mutations in KCNJ2-encoded Kir2.1 channels underlie variant 3 (SQT3) of the short QT syndrome, which is associated with atrial fibrillation (AF). Using biophysically-detailed human atria computer models, this study investigated the mechanistic link between SQT3 mutations and atrial arrhythmogenesis, and potential ion channel targets for treatment of SQT3. A contemporary model of the human atrial action potential (AP) was modified to recapitulate functional changes in IK1 due to heterozygous and homozygous forms of the D172N and E299V Kir2.1 mutations. Wild-type (WT) and mutant formulations were incorporated into multi-scale homogeneous and heterogeneous tissue models. Effects of mutations on AP duration (APD), conduction velocity (CV), effective refractory period (ERP), tissue excitation threshold and their rate-dependence, as well as the wavelength of re-entry (WL) were quantified. The D172N and E299V Kir2.1 mutations produced distinct effects on IK1 and APD shortening. Both mutations decreased WL for re-entry through a reduction in ERP and CV. Stability of re-entrant excitation waves in 2D and 3D tissue models was mediated by changes to tissue excitability and dispersion of APD in mutation conditions. Combined block of IK1 and IKr was effective in terminating re-entry associated with heterozygous D172N conditions, whereas IKr block alone may be a safer alternative for the E299V mutation. Combined inhibition of IKr and IKur produced a synergistic anti-arrhythmic effect in both forms of SQT3. In conclusion, this study provides mechanistic insights into atrial proarrhythmia with SQT3 Kir2.1 mutations and highlights possible pharmacological strategies for management of SQT3-linked AF. PMID:28609477

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.

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

Application of a mechanistic model as a tool for on-line monitoring of pilot scale filamentous fungal fermentation processes-The importance of evaporation effects.

PubMed

Mears, Lisa; Stocks, Stuart M; Albaek, Mads O; Sin, Gürkan; Gernaey, Krist V

2017-03-01

A mechanistic model-based soft sensor is developed and validated for 550L filamentous fungus fermentations operated at Novozymes A/S. The soft sensor is comprised of a parameter estimation block based on a stoichiometric balance, coupled to a dynamic process model. The on-line parameter estimation block models the changing rates of formation of product, biomass, and water, and the rate of consumption of feed using standard, available on-line measurements. This parameter estimation block, is coupled to a mechanistic process model, which solves the current states of biomass, product, substrate, dissolved oxygen and mass, as well as other process parameters including k L a, viscosity and partial pressure of CO 2 . State estimation at this scale requires a robust mass model including evaporation, which is a factor not often considered at smaller scales of operation. The model is developed using a historical data set of 11 batches from the fermentation pilot plant (550L) at Novozymes A/S. The model is then implemented on-line in 550L fermentation processes operated at Novozymes A/S in order to validate the state estimator model on 14 new batches utilizing a new strain. The product concentration in the validation batches was predicted with an average root mean sum of squared error (RMSSE) of 16.6%. In addition, calculation of the Janus coefficient for the validation batches shows a suitably calibrated model. The robustness of the model prediction is assessed with respect to the accuracy of the input data. Parameter estimation uncertainty is also carried out. The application of this on-line state estimator allows for on-line monitoring of pilot scale batches, including real-time estimates of multiple parameters which are not able to be monitored on-line. With successful application of a soft sensor at this scale, this allows for improved process monitoring, as well as opening up further possibilities for on-line control algorithms, utilizing these on-line model outputs. Biotechnol. Bioeng. 2017;114: 589-599. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response

PubMed Central

Geerts, Hugo; Spiros, Athan; Roberts, Patrick; Twyman, Roy; Alphs, Larry; Grace, Anthony A.

2012-01-01

The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published ‘Quantitative Systems Pharmacology’ computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D2 antagonist and ocaperidone, a very high affinity dopamine D2 antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development. PMID:23251349

Root plasticity buffers competition among plants: theory meets experimental data.

PubMed

Schiffers, Katja; Tielbörger, Katja; Tietjen, Britta; Jeltsch, Florian

2011-03-01

Morphological plasticity is a striking characteristic of plants in natural communities. In the context of foraging behavior particularly, root plasticity has been documented for numerous species. Root plasticity is known to mitigate competitive interactions by reducing the overlap of the individuals' rhizospheres. But despite its obvious effect on resource acquisition, plasticity has been generally neglected in previous empirical and theoretical studies estimating interaction intensity among plants. In this study, we developed a semi-mechanistic model that addresses this shortcoming by introducing the idea of compensatory growth into the classical-zone-of influence (ZOI) and field-of-neighborhood (FON) approaches. The model parameters describing the belowground plastic sphere of influence (PSI) were parameterized using data from an accompanying field experiment. Measurements of the uptake of a stable nutrient analogue at distinct distances to the neighboring plants showed that the study species responded plastically to belowground competition by avoiding overlap of individuals' rhizospheres. An unexpected finding was that the sphere of influence of the study species Bromus hordeaceus could be best described by a unimodal function of distance to the plant's center and not with a continuously decreasing function as commonly assumed. We employed the parameterized model to investigate the interplay between plasticity and two other important factors determining the intensity of competitive interactions: overall plant density and the distribution of individuals in space. The simulation results confirm that the reduction of competition intensity due to morphological plasticity strongly depends on the spatial structure of the competitive environment. We advocate the use of semi-mechanistic simulations that explicitly consider morphological plasticity to improve our mechanistic understanding of plant interactions.

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.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

PubMed

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

2014-07-01

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.

Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming.

PubMed

Xue, Qingwan; Markkula, Gustav; Yan, Xuedong; Merat, Natasha

2018-06-18

Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ -1 , the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ -1 as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ -1 . Copyright © 2018 Elsevier Ltd. All rights reserved.

A discrete model of Drosophila eggshell patterning reveals cell-autonomous and juxtacrine effects.

PubMed

Fauré, Adrien; Vreede, Barbara M I; Sucena, Elio; Chaouiya, Claudine

2014-03-01

The Drosophila eggshell constitutes a remarkable system for the study of epithelial patterning, both experimentally and through computational modeling. Dorsal eggshell appendages arise from specific regions in the anterior follicular epithelium that covers the oocyte: two groups of cells expressing broad (roof cells) bordered by rhomboid expressing cells (floor cells). Despite the large number of genes known to participate in defining these domains and the important modeling efforts put into this developmental system, key patterning events still lack a proper mechanistic understanding and/or genetic basis, and the literature appears to conflict on some crucial points. We tackle these issues with an original, discrete framework that considers single-cell models that are integrated to construct epithelial models. We first build a phenomenological model that reproduces wild type follicular epithelial patterns, confirming EGF and BMP signaling input as sufficient to establish the major features of this patterning system within the anterior domain. Importantly, this simple model predicts an instructive juxtacrine signal linking the roof and floor domains. To explore this prediction, we define a mechanistic model that integrates the combined effects of cellular genetic networks, cell communication and network adjustment through developmental events. Moreover, we focus on the anterior competence region, and postulate that early BMP signaling participates with early EGF signaling in its specification. This model accurately simulates wild type pattern formation and is able to reproduce, with unprecedented level of precision and completeness, various published gain-of-function and loss-of-function experiments, including perturbations of the BMP pathway previously seen as conflicting results. The result is a coherent model built upon rules that may be generalized to other epithelia and developmental systems.

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.

Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology.

PubMed

Kirouac, D C; Onsum, M D

2013-09-04

If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e71; doi:10.1038/psp.2013.38; published online 4 September 2013.

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 (

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.

A Predictive Model for Satellite-Derived Phytoplankton Absorption Over the Louisiana Shelf Hypoxic Zone: Effects of Nutrients and Physical Forcing

DTIC Science & Technology

2008-06-06

biomass (Figures 5a and 6), as proposed by Biggs and ing causal mechanisms in the way that a mechanistic model Sanchez [1997]. In previous work...1987; L6pez-Veneroni and Cifuentes , 1994; Sahl et al., 1993; Chen et al., 2000]. Similarly, for our study years (2002- 2004), we observed...Hawaii, November. identified as important on the shelf, especially that of water Biggs, D. C., and L. L. Sanchez (1997), Nutrient enhanced primary

In vitro solubility, dissolution and permeability studies combined with semi-mechanistic modeling to investigate the intestinal absorption of desvenlafaxine from an immediate- and extended release formulation.

PubMed

Franek, F; Jarlfors, A; Larsen, F; Holm, P; Steffansen, B

2015-09-18

Desvenlafaxine is a biopharmaceutics classification system (BCS) class 1 (high solubility, high permeability) and biopharmaceutical drug disposition classification system (BDDCS) class 3, (high solubility, poor metabolism; implying low permeability) compound. Thus the rate-limiting step for desvenlafaxine absorption (i.e. intestinal dissolution or permeation) is not fully clarified. The aim of this study was to investigate whether dissolution and/or intestinal permeability rate-limit desvenlafaxine absorption from an immediate-release formulation (IRF) and Pristiq(®), an extended release formulation (ERF). Semi-mechanistic models of desvenlafaxine were built (using SimCyp(®)) by combining in vitro data on dissolution and permeation (mechanistic part of model) with clinical data (obtained from literature) on distribution and clearance (non-mechanistic part of model). The model predictions of desvenlafaxine pharmacokinetics after IRF and ERF administration were compared with published clinical data from 14 trials. Desvenlafaxine in vivo dissolution from the IRF and ERF was predicted from in vitro solubility studies and biorelevant dissolution studies (using the USP3 dissolution apparatus), respectively. Desvenlafaxine apparent permeability (Papp) at varying apical pH was investigated using the Caco-2 cell line and extrapolated to effective intestinal permeability (Peff) in human duodenum, jejunum, ileum and colon. Desvenlafaxine pKa-values and octanol-water partition coefficients (Do:w) were determined experimentally. Due to predicted rapid dissolution after IRF administration, desvenlafaxine was predicted to be available for permeation in the duodenum. Desvenlafaxine Do:w and Papp increased approximately 13-fold when increasing apical pH from 5.5 to 7.4. Desvenlafaxine Peff thus increased with pH down the small intestine. Consequently, desvenlafaxine absorption from an IRF appears rate-limited by low Peff in the upper small intestine, which "delays" the predicted time to the maximal plasma concentration (tmax), consistent with clinical data. Conversely, desvenlafaxine absorption from the ERF appears rate-limited by dissolution due to the formulation, which tends to negate the influence of pH-dependent permeability on absorption. We suggest that desvenlafaxine Peff is mainly driven by transcellular diffusion of the unionized form. In the case of desvenlafaxine, poor metabolism does not imply low intestinal permeability, as indicated by the BDDCS, merely low duodenal/jejunal permeability. Copyright © 2015 Elsevier B.V. All rights reserved.

Electrochemical processes and mechanistic aspects of field-effect sensors for biomolecules

PubMed Central

Huang, Weiguo; Diallo, Abdou Karim; Dailey, Jennifer L.; Besar, Kalpana

2017-01-01

Electronic biosensing is a leading technology for determining concentrations of biomolecules. In some cases, the presence of an analyte molecule induces a measured change in current flow, while in other cases, a new potential difference is established. In the particular case of a field effect biosensor, the potential difference is monitored as a change in conductance elsewhere in the device, such as across a film of an underlying semiconductor. Often, the mechanisms that lead to these responses are not specifically determined. Because improved understanding of these mechanisms will lead to improved performance, it is important to highlight those studies where various mechanistic possibilities are investigated. This review explores a range of possible mechanistic contributions to field-effect biosensor signals. First, we define the field-effect biosensor and the chemical interactions that lead to the field effect, followed by a section on theoretical and mechanistic background. We then discuss materials used in field-effect biosensors and approaches to improving signals from field-effect biosensors. We specifically cover the biomolecule interactions that produce local electric fields, structures and processes at interfaces between bioanalyte solutions and electronic materials, semiconductors used in biochemical sensors, dielectric layers used in top-gated sensors, and mechanisms for converting the surface voltage change to higher signal/noise outputs in circuits. PMID:29238595

Mechanistic explanation of time-dependent cross-phenomenon based on quorum sensing: A case study of the mixture of sulfonamide and quorum sensing inhibitor to bioluminescence of Aliivibrio fischeri.

PubMed

Sun, Haoyu; Pan, Yongzheng; Gu, Yue; Lin, Zhifen

2018-07-15

Cross-phenomenon in which the concentration-response curve (CRC) for a mixture crosses the CRC for the reference model has been identified in many studies, expressed as a heterogeneous pattern of joint toxic action. However, a mechanistic explanation of the cross-phenomenon has thus far been extremely insufficient. In this study, a time-dependent cross-phenomenon was observed, in which the cross-concentration range between the CRC for the mixture of sulfamethoxypyridazine (SMP) and (Z-)-4-Bromo-5-(bromomethylene)-2(5H)-furanone (C30) to the bioluminescence of Aliivibrio fischeri (A. fischeri) and the CRC for independent action model with 95% confidence bands varied from low-concentration to higher-concentration regions in a timely manner expressed the joint toxic action of the mixture changing with an increase of both concentration and time. Through investigating the time-dependent hormetic effects of SMP and C30 (by measuring the expression of protein mRNA, simulating the bioluminescent reaction and analyzing the toxic action), the underlying mechanism was as follows: SMP and C30 acted on the quorum sensing (QS) system of A. fischeri, which induced low-concentration stimulatory effects and high-concentration inhibitory effects; in the low-concentration region, the stimulatory effects of SMP and C30 made the mixture produce a synergistic stimulation on the bioluminescence; thus, the joint toxic action exhibited antagonism. In the high-concentration region, the inhibitory effects of SMP and C30 in the mixture caused a double block in the loop circuit of the QS system; thus, the joint toxic action exhibited synergism. With the increase of time, these stimulatory and inhibitory effects of SMP and C30 were changed by the variation of the QS system at different growth phases, resulting in the time-dependent cross-phenomenon. This study proposes an induced mechanism for time-dependent cross-phenomenon based on QS, which may provide new insight into the mechanistic investigation of time-dependent cross-phenomenon, benefitting the environmental risk assessment of mixtures. Copyright © 2018 Elsevier B.V. All rights reserved.

The use of mechanistic evidence in drug approval.

PubMed

Aronson, Jeffrey K; La Caze, Adam; Kelly, Michael P; Parkkinen, Veli-Pekka; Williamson, Jon

2018-06-11

The role of mechanistic evidence tends to be under-appreciated in current evidence-based medicine (EBM), which focusses on clinical studies, tending to restrict attention to randomized controlled studies (RCTs) when they are available. The EBM+ programme seeks to redress this imbalance, by suggesting methods for evaluating mechanistic studies alongside clinical studies. Drug approval is a problematic case for the view that mechanistic evidence should be taken into account, because RCTs are almost always available. Nevertheless, we argue that mechanistic evidence is central to all the key tasks in the drug approval process: in drug discovery and development; assessing pharmaceutical quality; devising dosage regimens; assessing efficacy, harms, external validity, and cost-effectiveness; evaluating adherence; and extending product licences. We recommend that, when preparing for meetings in which any aspect of drug approval is to be discussed, mechanistic evidence should be systematically analysed and presented to the committee members alongside analyses of clinical studies. © 2018 The Authors Journal of Evaluation in Clinical Practice Published by John Wiley & Sons Ltd.

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...

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.

Inferring Diffusion Dynamics from FCS in Heterogeneous Nuclear Environments

PubMed Central

Tsekouras, Konstantinos; Siegel, Amanda P.; Day, Richard N.; Pressé, Steve

2015-01-01

Fluorescence correlation spectroscopy (FCS) is a noninvasive technique that probes the diffusion dynamics of proteins down to single-molecule sensitivity in living cells. Critical mechanistic insight is often drawn from FCS experiments by fitting the resulting time-intensity correlation function, G(t), to known diffusion models. When simple models fail, the complex diffusion dynamics of proteins within heterogeneous cellular environments can be fit to anomalous diffusion models with adjustable anomalous exponents. Here, we take a different approach. We use the maximum entropy method to show—first using synthetic data—that a model for proteins diffusing while stochastically binding/unbinding to various affinity sites in living cells gives rise to a G(t) that could otherwise be equally well fit using anomalous diffusion models. We explain the mechanistic insight derived from our method. In particular, using real FCS data, we describe how the effects of cell crowding and binding to affinity sites manifest themselves in the behavior of G(t). Our focus is on the diffusive behavior of an engineered protein in 1) the heterochromatin region of the cell’s nucleus as well as 2) in the cell’s cytoplasm and 3) in solution. The protein consists of the basic region-leucine zipper (BZip) domain of the CCAAT/enhancer-binding protein (C/EBP) fused to fluorescent proteins. PMID:26153697

Mechanistic links between cellular trade-offs, gene expression, and growth.

PubMed

Weiße, Andrea Y; Oyarzún, Diego A; Danos, Vincent; Swain, Peter S

2015-03-03

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.

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.

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).

Mechanistic study of intertypic nucleoprotein complex formation and its inhibitory effect toward influenza A virus.

PubMed

Narkpuk, Jaraspim; Jaru-Ampornpan, Peera; Subali, Theressa; Bertulfo, Fatima Carla T; Wongthida, Phonphimon; Jongkaewwattana, Anan

2015-11-01

Co-infection of influenza A and B viruses (IAV and IBV) results in marked decreases in IAV replication. Multiple mechanisms have been proposed for this phenomenon. Recently, we reported that IBV nucleoprotein (BNP) alone can suppress IAV replication and proposed an inhibition model in which BNP binds IAV nucleoprotein (ANP) and disrupts IAV polymerase complexes. Here, using mutagenesis and co-immunoprecipitation, we determined the protein motifs mediating the intertypic ANP-BNP complex and showed that it specifically interferes with ANP׳s interaction with the PB2 subunit of the IAV polymerase but not with the other subunit PB1. We further demonstrated that BNP only suppresses growth of IAVs but not other RNA viruses. However, different IAV strains display varied sensitivity toward the BNP׳s inhibitory effect. Together, our data provide mechanistic insights into intertypic nucleoprotein complex formation and highlight the role of BNP as a potential broad-spectrum anti-IAV agent. Copyright © 2015 Elsevier Inc. All rights reserved.

At the interface of antioxidant signalling and cellular function: Key polyphenol effects

PubMed Central

Kerimi, Asimina

2016-01-01

The hypothesis that dietary (poly)phenols promote well‐being by improving chronic disease‐risk biomarkers, such as endothelial dysfunction, chronic inflammation and plasma uric acid, is the subject of intense current research, involving human interventions studies, animal models and in vitro mechanistic work. The original claim that benefits were due to the direct antioxidant properties of (poly)phenols has been mostly superseded by detailed mechanistic studies on specific molecular targets. Nevertheless, many proposed mechanisms in vivo and in vitro are due to modulation of oxidative processes, often involving binding to specific proteins and effects on cell signalling. We review the molecular mechanisms for 3 actions of (poly)phenols on oxidative processes where there is evidence in vivo from human intervention or animal studies. (1) Effects of (poly) phenols on pathways of chronic inflammation leading to prevention of some of the damaging effects associated with the metabolic syndrome. (2) Interaction of (poly)phenols with endothelial cells and smooth muscle cells, leading to effects on blood pressure and endothelial dysfunction, and consequent reduction in cardiovascular disease risk. (3) The inhibition of xanthine oxidoreductase leading to modulation of intracellular superoxide and plasma uric acid, a risk factor for developing type 2 diabetes. PMID:26887821

A Modeling and Experimental Investigation of the Effects of Antigen Density, Binding Affinity, and Antigen Expression Ratio on Bispecific Antibody Binding to Cell Surface Targets.

PubMed

Rhoden, John J; Dyas, Gregory L; Wroblewski, Victor J

2016-05-20

Despite the increasing number of multivalent antibodies, bispecific antibodies, fusion proteins, and targeted nanoparticles that have been generated and studied, the mechanism of multivalent binding to cell surface targets is not well understood. Here, we describe a conceptual and mathematical model of multivalent antibody binding to cell surface antigens. Our model predicts that properties beyond 1:1 antibody:antigen affinity to target antigens have a strong influence on multivalent binding. Predicted crucial properties include the structure and flexibility of the antibody construct, the target antigen(s) and binding epitope(s), and the density of antigens on the cell surface. For bispecific antibodies, the ratio of the expression levels of the two target antigens is predicted to be critical to target binding, particularly for the lower expressed of the antigens. Using bispecific antibodies of different valencies to cell surface antigens including MET and EGF receptor, we have experimentally validated our modeling approach and its predictions and observed several nonintuitive effects of avidity related to antigen density, target ratio, and antibody affinity. In some biological circumstances, the effect we have predicted and measured varied from the monovalent binding interaction by several orders of magnitude. Moreover, our mathematical framework affords us a mechanistic interpretation of our observations and suggests strategies to achieve the desired antibody-antigen binding goals. These mechanistic insights have implications in antibody engineering and structure/activity relationship determination in a variety of biological contexts. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Mechanistic kinetic modeling generates system-independent P-glycoprotein mediated transport elementary rate constants for inhibition and, in combination with 3D SIM microscopy, elucidates the importance of microvilli morphology on P-glycoprotein mediated efflux activity.

PubMed

Ellens, Harma; Meng, Zhou; Le Marchand, Sylvain J; Bentz, Joe

2018-06-01

In vitro transporter kinetics are typically analyzed by steady-state Michaelis-Menten approximations. However, no clear evidence exists that these approximations, applied to multiple transporters in biological membranes, yield system-independent mechanistic parameters needed for reliable in vivo hypothesis generation and testing. Areas covered: The classical mass action model has been developed for P-glycoprotein (P-gp) mediated transport across confluent polarized cell monolayers. Numerical integration of the mass action equations for transport using a stable global optimization program yields fitted elementary rate constants that are system-independent. The efflux active P-gp was defined by the rate at which P-gp delivers drugs to the apical chamber, since as much as 90% of drugs effluxed by P-gp partition back into nearby microvilli prior to reaching the apical chamber. The efflux active P-gp concentration was 10-fold smaller than the total expressed P-gp for Caco-2 cells, due to their microvilli membrane morphology. The mechanistic insights from this analysis are readily extrapolated to P-gp mediated transport in vivo. Expert opinion: In vitro system-independent elementary rate constants for transporters are essential for the generation and validation of robust mechanistic PBPK models. Our modeling approach and programs have broad application potential. They can be used for any drug transporter with minor adaptations.

Relationship of stream ecological conditions to simulated hydraulic metrics across a gradient of basin urbanization

USGS Publications Warehouse

Steuer, J.J.; Bales, J.D.; Giddings, E.M.P.

2009-01-01

The relationships among urbanization, stream hydraulics, and aquatic biology were investigated across a gradient of urbanization in 30 small basins in eastern Wisconsin, USA. Simulation of hydraulic metrics with 1-dimensional unsteady flow models was an effective means for mechanistically coupling the effects of urbanization with stream ecological conditions (i.e., algae, invertebrates, and fish). Urbanization, characterized by household, road, and urban land density, was positively correlated with the lowest shear stress for 2 adjacent transects in a reach for the low-flow summer (p < 0.001) and autumn (p < 0.01) periods. Urbanization also was positively correlated with Reynolds number and % exposed stream bed during months with moderate to low flows. Our study demonstrated the value of temporally and spatially explicit hydraulic models for providing mechanistic insight into the relationships between hydraulic variables and biological responses. For example, the positive correlation between filter-feeding invertebrate richness and minimum 2-transect shear stress observed in our study is consistent with a higher concentration of water-column particulates available for filtration. The strength of correlations between hydraulic and biological metrics is related to the time period (annual, seasonal, or monthly) considered. The hydraulic modeling approach, whether based on hourly or daily flow data, allowed documentation of the effects of a spatially variable response within a reach, and the results suggest that stream response to urbanization varies with hydraulic habitat type. ?? North American Benthological Society.

Phloretin-induced cytoprotective effects on mammalian cells: A mechanistic view and future directions.

PubMed

de Oliveira, Marcos Roberto

2016-01-01

Phloretin (C15 H14 O5 ), a dihydrochalcone flavonoid, is mainly found in fruit, leaves, and roots of apple tree. Phloretin exerts antioxidant, anti-inflammatory, and anti-tumor activities in mammalian cells through mechanisms that have been partially elucidated throughout the years. Phloretin bioavailability is well known in humans, but still remains to be better studied in experimental animals, such as mouse and rat. The focus of the present review is to gather information regarding the mechanisms involved in the phloretin-elicited effects in different in vitro and in vivo experimental models. Several manuscripts were analyzed and data raised by authors were described and discussed here in a mechanistic manner. Comparisons between the effects elicited by phloretin and phloridzin were made whenever possible, as well as with other polyphenols, clarifying questions about the use of phloretin as a potential therapeutic agent. Toxicological aspects associated to phloretin exposure were also discussed here. Furthermore, a special section containing future directions was created as a suggestive guide towards the elucidation of phloretin-related actions in mammalian cells and tissues. © 2016 International Union of Biochemistry and Molecular Biology.

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.

Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)

NASA Astrophysics Data System (ADS)

Stige, Leif Chr.; Langangen, Øystein; Yaragina, Natalia A.; Vikebø, Frode B.; Bogstad, Bjarte; Ottersen, Geir; Stenseth, Nils Chr.; Hjermann, Dag Ø.

2015-05-01

Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.

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.

Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling

NASA Astrophysics Data System (ADS)

Hong, Yoon-Seok; Rosen, Michael R.

2002-03-01

An urban fractured-rock aquifer system, where disposal of storm water is via 'soak holes' drilled directly into the top of fractured-rock basalt, has a highly dynamic nature where theories or knowledge to generate the model are still incomplete and insufficient. Therefore, formulating an accurate mechanistic model, usually based on first principles (physical and chemical laws, mass balance, and diffusion and transport, etc.), requires time- and money-consuming tasks. Instead of a human developing the mechanistic-based model, this paper presents an approach to automatic model evolution in genetic programming (GP) to model dynamic behaviour of groundwater level fluctuations affected by storm water infiltration. This GP evolves mathematical models automatically that have an understandable structure using function tree representation by methods of natural selection ('survival of the fittest') through genetic operators (reproduction, crossover, and mutation). The simulation results have shown that GP is not only capable of predicting the groundwater level fluctuation due to storm water infiltration but also provides insight into the dynamic behaviour of a partially known urban fractured-rock aquifer system by allowing knowledge extraction of the evolved models. Our results show that GP can work as a cost-effective modelling tool, enabling us to create prototype models quickly and inexpensively and assists us in developing accurate models in less time, even if we have limited experience and incomplete knowledge for an urban fractured-rock aquifer system affected by storm water infiltration.

Base course resilient modulus for the mechanistic-empirical pavement design guide.

DOT National Transportation Integrated Search

2011-11-01

The Mechanistic-Empirical Pavement Design Guidelines (MEPDG) recommend use of modulus in lieu of structural number for base layer thickness design. Modulus is nonlinear with respect to effective confinement stress, loading strain, and moisture. For d...

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.

Improvements In AF Ablation Outcome Will Be Based More On Technological Advancement Versus Mechanistic Understanding.

PubMed

Jiang Md, Chen-Yang; Jiang Ms, Ru-Hong

2014-01-01

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Catheter ablation has proven more effective than antiarrhythmic drugs in preventing clinical recurrence of AF, however long-term outcome remains unsatisfactory. Ablation strategies have evolved based on progress in mechanistic understanding, and technologies have advanced continuously. This article reviews current mechanistic concepts and technological advancements in AF treatment, and summarizes their impact on improvement of AF ablation outcome.

Recurrent sublethal warming reduces embryonic survival, inhibits juvenile growth, and alters species distribution projections under climate change.

PubMed

Carlo, Michael A; Riddell, Eric A; Levy, Ofir; Sears, Michael W

2018-01-01

The capacity to tolerate climate change often varies across ontogeny in organisms with complex life cycles. Recently developed species distribution models incorporate traits across life stages; however, these life-cycle models primarily evaluate effects of lethal change. Here, we examine impacts of recurrent sublethal warming on development and survival in ecological projections of climate change. We reared lizard embryos in the laboratory under temperature cycles that simulated contemporary conditions and warming scenarios. We also artificially warmed natural nests to mimic laboratory treatments. In both cases, recurrent sublethal warming decreased embryonic survival and hatchling sizes. Incorporating survivorship results into a mechanistic species distribution model reduced annual survival by up to 24% compared to models that did not incorporate sublethal warming. Contrary to models without sublethal effects, our model suggests that modest increases in developmental temperatures influence species ranges due to effects on survivorship. © 2017 John Wiley & Sons Ltd/CNRS.

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

PubMed

Du, Bin; Zielinski, Daniel C; Kavvas, Erol S; Dräger, Andreas; Tan, Justin; Zhang, Zhen; Ruggiero, Kayla E; Arzumanyan, Garri A; Palsson, Bernhard O

2016-06-06

The mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question. In this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations. Overall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches.

Global scale analysis and evaluation of an improved mechanistic representation of plant nitrogen and carbon dynamics in the Community Land Model (CLM)

NASA Astrophysics Data System (ADS)

Ghimire, B.; Riley, W. J.; Koven, C. D.; Randerson, J. T.; Mu, M.; Kattge, J.; Rogers, A.; Reich, P. B.

2014-12-01

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However mechanistic representation of nitrogen uptake linked to root traits, and functional nitrogen allocation among different leaf enzymes involved in respiration and photosynthesis is currently lacking in Earth System models. The linkage between nitrogen availability and plant productivity is simplistically represented by potential photosynthesis rates, and is subsequently downregulated depending on nitrogen supply and other nitrogen consumers in the model (e.g., nitrification). This type of potential photosynthesis rate calculation is problematic for several reasons. Firstly, plants do not photosynthesize at potential rates and then downregulate. Secondly, there is considerable subjectivity on the meaning of potential photosynthesis rates. Thirdly, there exists lack of understanding on modeling these potential photosynthesis rates in a changing climate. In addition to model structural issues in representing photosynthesis rates, the role of plant roots in nutrient acquisition have been largely ignored in Earth System models. For example, in CLM4.5, nitrogen uptake is linked to leaf level processes (e.g., primarily productivity) rather than root scale process involved in nitrogen uptake. We present a new plant model for CLM with an improved mechanistic presentation of plant nitrogen uptake based on root scale Michaelis Menten kinetics, and stronger linkages between leaf nitrogen and plant productivity by inferring relationships observed in global databases of plant traits (including the TRY database and several individual studies). We also incorporate improved representation of plant nitrogen leaf allocation, especially in tropical regions where significant over-prediction of plant growth and productivity in CLM4.5 simulations exist. We evaluate our improved global model simulations using the International Land Model Benchmarking (ILAMB) framework. We conclude that mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers leads to overall improvements in CLM4.5's global carbon cycling predictions.

A new model integrating short- and long-term aging of copper added to soils

PubMed Central

Zeng, Saiqi; Li, Jumei; Wei, Dongpu

2017-01-01

Aging refers to the processes by which the bioavailability/toxicity, isotopic exchangeability, and extractability of metals added to soils decline overtime. We studied the characteristics of the aging process in copper (Cu) added to soils and the factors that affect this process. Then we developed a semi-mechanistic model to predict the lability of Cu during the aging process with descriptions of the diffusion process using complementary error function. In the previous studies, two semi-mechanistic models to separately predict short-term and long-term aging of Cu added to soils were developed with individual descriptions of the diffusion process. In the short-term model, the diffusion process was linearly related to the square root of incubation time (t1/2), and in the long-term model, the diffusion process was linearly related to the natural logarithm of incubation time (lnt). Both models could predict short-term or long-term aging processes separately, but could not predict the short- and long-term aging processes by one model. By analyzing and combining the two models, we found that the short- and long-term behaviors of the diffusion process could be described adequately using the complementary error function. The effect of temperature on the diffusion process was obtained in this model as well. The model can predict the aging process continuously based on four factors—soil pH, incubation time, soil organic matter content and temperature. PMID:28820888

Stomatal control and hydraulic conductance, with special reference to tall trees.

PubMed

Franks, Peter J

2004-08-01

A better understanding of the mechanistic basis of stomatal control is necessary to understand why modes of stomatal response differ among individual trees, and to improve the theoretical foundation for predictive models and manipulative experiments. Current understanding of the mechanistic basis of stomatal control is reviewed here and discussed in relation to the plant hydraulic system. Analysis focused on: (1) the relative role of hydraulic conductance in the vicinity of the stomatal apparatus versus whole-plant hydraulic conductance; (2) the influence of guard cell inflation characteristics and the mechanical interaction between guard cells and epidermal cells; and (3) the system requirements for moderate versus dramatic reductions in stomatal conductance with increasing evaporation potential. Special consideration was given to the potential effect of changes in hydraulic properties as trees grow taller. Stomatal control of leaf gas exchange is coupled to the entire plant hydraulic system and the basis of this coupling is the interdependence of guard cell water potential and transpiration rate. This hydraulic feedback loop is always present, but its dynamic properties may be altered by growth or cavitation-induced changes in hydraulic conductance, and may vary with genetically related differences in hydraulic conductances. Mechanistic models should include this feedback loop. Plants vary in their ability to control transpiration rate sufficiently to maintain constant leaf water potential. Limited control may be achieved through the hydraulic feedback loop alone, but for tighter control, an additional element linking transpiration rate to guard cell osmotic pressure may be needed.

Modeling Creep Effects in Advanced SiC/SiC Composites

NASA Technical Reports Server (NTRS)

Lang, Jerry; DiCarlo, James

2006-01-01

Because advanced SiC/SiC composites are projected to be used for aerospace components with large thermal gradients at high temperatures, efforts are on-going at NASA Glenn to develop approaches for modeling the anticipated creep behavior of these materials and its subsequent effects on such key composite properties as internal residual stress, proportional limit stress, ultimate tensile strength, and rupture life. Based primarily on in-plane creep data for 2D panels, this presentation describes initial modeling progress at applied composite stresses below matrix cracking for some high performance SiC/SiC composite systems recently developed at NASA. Studies are described to develop creep and rupture models using empirical, mechanical analog, and mechanistic approaches, and to implement them into finite element codes for improved component design and life modeling

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.

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

Characterization of unbound materials (soils/aggregates) for mechanistic-empirical pavement design guide.

DOT National Transportation Integrated Search

2009-02-01

The resilient modulus (MR) input parameters in the Mechanistic-Empirical Pavement Design Guide (MEPDG) program have a significant effect on the projected pavement performance. The MEPDG program uses three different levels of inputs depending on the d...

Investigation of Dynamic Modulus and Flow Number Properties of Asphalt Mixtures In Washington State

DOT National Transportation Integrated Search

2011-11-11

Pavement design is now moving toward more mechanistic based design methodologies for the purpose of producing long : lasting and higher performance pavements in a cost-effective manner. The recent Mechanistic-Empirical pavement : design guide (MEPDG)...

Rosacea, Reactive Oxygen Species, and Azelaic Acid

PubMed Central

2009-01-01

Rosacea is a common skin condition thought to be primarily an inflammatory disorder. Neutrophils, in particular, have been implicated in the inflammation associated with rosacea and mediate many of their effects through the release of reactive oxygen species. Recently, the role of reactive oxygen species in the pathophysiology of rosacea has been recognized. Many effective agents for rosacea, including topical azelaic acid and topical metronidazole, have anti-inflammatory properties. in-vitro models have demonstrated the potent antioxidant effects of azelaic acid, providing a potential mechanistic explanation for its efficacy in the treatment of rosacea. PMID:20967185

Rosacea, reactive oxygen species, and azelaic Acid.

PubMed

Jones, David A

2009-01-01

Rosacea is a common skin condition thought to be primarily an inflammatory disorder. Neutrophils, in particular, have been implicated in the inflammation associated with rosacea and mediate many of their effects through the release of reactive oxygen species. Recently, the role of reactive oxygen species in the pathophysiology of rosacea has been recognized. Many effective agents for rosacea, including topical azelaic acid and topical metronidazole, have anti-inflammatory properties. in-vitro models have demonstrated the potent antioxidant effects of azelaic acid, providing a potential mechanistic explanation for its efficacy in the treatment of rosacea.

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.

Modeling behavioral thermoregulation in a climate change sentinel.

PubMed

Moyer-Horner, Lucas; Mathewson, Paul D; Jones, Gavin M; Kearney, Michael R; Porter, Warren P

2015-12-01

When possible, many species will shift in elevation or latitude in response to rising temperatures. However, before such shifts occur, individuals will first tolerate environmental change and then modify their behavior to maintain heat balance. Behavioral thermoregulation allows animals a range of climatic tolerances and makes predicting geographic responses under future warming scenarios challenging. Because behavioral modification may reduce an individual's fecundity by, for example, limiting foraging time and thus caloric intake, we must consider the range of behavioral options available for thermoregulation to accurately predict climate change impacts on individual species. To date, few studies have identified mechanistic links between an organism's daily activities and the need to thermoregulate. We used a biophysical model, Niche Mapper, to mechanistically model microclimate conditions and thermoregulatory behavior for a temperature-sensitive mammal, the American pika (Ochotona princeps). Niche Mapper accurately simulated microclimate conditions, as well as empirical metabolic chamber data for a range of fur properties, animal sizes, and environmental parameters. Niche Mapper predicted pikas would be behaviorally constrained because of the need to thermoregulate during the hottest times of the day. We also showed that pikas at low elevations could receive energetic benefits by being smaller in size and maintaining summer pelage during longer stretches of the active season under a future warming scenario. We observed pika behavior for 288 h in Glacier National Park, Montana, and thermally characterized their rocky, montane environment. We found that pikas were most active when temperatures were cooler, and at sites characterized by high elevations and north-facing slopes. Pikas became significantly less active across a suite of behaviors in the field when temperatures surpassed 20°C, which supported a metabolic threshold predicted by Niche Mapper. In general, mechanistic predictions and empirical observations were congruent. This research is unique in providing both an empirical and mechanistic description of the effects of temperature on a mammalian sentinel of climate change, the American pika. Our results suggest that previously underinvestigated characteristics, specifically fur properties and body size, may play critical roles in pika populations' response to climate change. We also demonstrate the potential importance of considering behavioral thermoregulation and microclimate variability when predicting animal responses to climate change.

Quantifying Direct and Indirect Effects of Elevated CO2 on Ecosystem Response

NASA Astrophysics Data System (ADS)

Fatichi, S.; Leuzinger, S.; Paschalis, A.; Donnellan-Barraclough, A.; Hovenden, M. J.; Langley, J. A.

2015-12-01

Increasing concentrations of atmospheric carbon dioxide are expected to affect carbon assimilation, evapotranspiration (ET) and ultimately plant growth. Direct leaf biochemical effects have been widely investigated, while indirect effects, although documented, are very difficult to quantify in experiments. We hypothesize that the interaction of direct and indirect effects is a possible reason for conflicting results concerning the magnitude of CO2 fertilization effects across different climates and ecosystems. A mechanistic ecohydrological model (Tethys-Chloris) is used to investigate the relative contribution of direct (through plant physiology) and indirect (via stomatal closure and thus soil moisture, and changes in Leaf Area Index, LAI) effects of elevated CO2 across a number of ecosystems. We specifically ask in which ecosystems and climate indirect effects are expected to be largest. Data and boundary conditions from flux-towers and free air CO2 enrichment (FACE) experiments are used to force the model and evaluate its performance. Numerical results suggest that indirect effects of elevated CO2, through water savings and increased LAI, are very significant and sometimes larger than direct effects. Indirect effects tend to be considerably larger in water-limited ecosystems, while direct effects correlate positively with mean air temperature. Increasing CO2 from 375 to 550 ppm causes a total effect on Net Primary Production in the order of 15 to 40% and on ET from 0 to -8%, depending on climate and ecosystem type. The total CO2 effect has a significant negative correlation with the wetness index and positive correlation with vapor pressure deficit. These results provide a more general mechanistic understanding of relatively short-term (less than 20 years) implications of elevated CO2 on ecosystem response and suggest plausible magnitudes for the expected changes.

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

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

USGS Publications Warehouse

Kocovsky, P.M.; Carline, R.F.

2006-01-01

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

Modeling the full length HIV-1 Gag polyprotein reveals the role of its p6 subunit in viral maturation and the effect of non-cleavage site mutations in protease drug resistance.

PubMed

Su, Chinh Tran-To; Kwoh, Chee-Keong; Verma, Chandra Shekhar; Gan, Samuel Ken-En

2017-12-27

HIV polyprotein Gag is increasingly found to contribute to protease inhibitor resistance. Despite its role in viral maturation and in developing drug resistance, there remain gaps in the knowledge of the role of certain Gag subunits (e.g. p6), and that of non-cleavage mutations in drug resistance. As p6 is flexible, it poses a problem for structural experiments, and is hence often omitted in experimental Gag structural studies. Nonetheless, as p6 is an indispensable component for viral assembly and maturation, we have modeled the full length Gag structure based on several experimentally determined constraints and studied its structural dynamics. Our findings suggest that p6 can mechanistically modulate Gag conformations. In addition, the full length Gag model reveals that allosteric communication between the non-cleavage site mutations and the first Gag cleavage site could possibly result in protease drug resistance, particularly in the absence of mutations in Gag cleavage sites. Our study provides a mechanistic understanding to the structural dynamics of HIV-1 Gag, and also proposes p6 as a possible drug target in anti-HIV therapy.

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.

Gaussian process regression for forecasting battery state of health

NASA Astrophysics Data System (ADS)

Richardson, Robert R.; Osborne, Michael A.; Howey, David A.

2017-07-01

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

The role of photorespiration during the evolution of C4 photosynthesis in the genus Flaveria.

PubMed

Mallmann, Julia; Heckmann, David; Bräutigam, Andrea; Lercher, Martin J; Weber, Andreas P M; Westhoff, Peter; Gowik, Udo

2014-06-16

C4 photosynthesis represents a most remarkable case of convergent evolution of a complex trait, which includes the reprogramming of the expression patterns of thousands of genes. Anatomical, physiological, and phylogenetic and analyses as well as computational modeling indicate that the establishment of a photorespiratory carbon pump (termed C2 photosynthesis) is a prerequisite for the evolution of C4. However, a mechanistic model explaining the tight connection between the evolution of C4 and C2 photosynthesis is currently lacking. Here we address this question through comparative transcriptomic and biochemical analyses of closely related C3, C3-C4, and C4 species, combined with Flux Balance Analysis constrained through a mechanistic model of carbon fixation. We show that C2 photosynthesis creates a misbalance in nitrogen metabolism between bundle sheath and mesophyll cells. Rebalancing nitrogen metabolism requires anaplerotic reactions that resemble at least parts of a basic C4 cycle. Our findings thus show how C2 photosynthesis represents a pre-adaptation for the C4 system, where the evolution of the C2 system establishes important C4 components as a side effect.

WE-H-BRA-07: Mechanistic Modelling of the Relative Biological Effectiveness of Heavy Charged Particles

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

McMahon, S; Queen’s University, Belfast, Belfast; McNamara, A

2016-06-15

Purpose Uncertainty in the Relative Biological Effectiveness (RBE) of heavy charged particles compared to photons remains one of the major uncertainties in particle therapy. As RBEs depend strongly on clinical variables such as tissue type, dose, and radiation quality, more accurate individualised models are needed to fully optimise treatments. MethodsWe have developed a model of DNA damage and repair following X-ray irradiation in a number of settings, incorporating mechanistic descriptions of DNA repair pathways, geometric effects on DNA repair, cell cycle effects and cell death. Our model has previously been shown to accurately predict a range of biological endpoints includingmore » chromosome aberrations, mutations, and cell death. This model was combined with nanodosimetric models of individual ion tracks to calculate the additional probability of lethal damage forming within a single track. These lethal damage probabilities can be used to predict survival and RBE for cells irradiated with ions of different Linear Energy Transfer (LET). ResultsBy combining the X-ray response model with nanodosimetry information, predictions of RBE can be made without cell-line specific fitting. The model’s RBE predictions were found to agree well with empirical proton RBE models (Mean absolute difference between models of 1.9% and 1.8% for cells with α/β ratios of 9 and 1.4, respectively, for LETs between 0 and 15 keV/µm). The model also accurately recovers the impact of high-LET carbon ion exposures, showing both the reduced efficacy of ions at extremely high LET, as well as the impact of defects in non-homologous end joining on RBE values in Chinese Hamster Ovary cells.ConclusionOur model is predicts RBE without the inclusion of empirical LET fitting parameters for a range of experimental conditions. This approach has the potential to deliver improved personalisation of particle therapy, with future developments allowing for the calculation of individualised RBEs. SJM is supported by a Marie Curie International Outgoing Fellowship from the European Commission’s FP7 program (EC FP7 MC-IOF-623630)« less

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

Population Dynamics of an Insect Herbivore over 32 Years are Driven by Precipitation and Host-Plant Effects: Testing Model Predictions.

PubMed

Price, Peter W; Hunter, Mark D

2015-06-01

The interaction between the arroyo willow, Salix lasiolepis Bentham, and its specialist herbivore, the arroyo willow stem-galling sawfly, Euura lasiolepis Smith (Hymenoptera: Tenthredinidae), was studied for 32 yr in Flagstaff, AZ, emphasizing a mechanistic understanding of insect population dynamics. Long-term weather records were evaluated to provide a climatic context for this study. Previously, predictive models of sawfly dynamics were developed from estimates of sawfly gall density made between 1981 and 2002; one model each for drier and wetter sites. Predictor variables in these models included winter precipitation and the Palmer Drought Severity Index, which impact the willow growth, with strong bottom-up effects on sawflies. We now evaluate original model predictions of sawfly population dynamics using new data (from 2003-2012). Additionally, willow resources were evaluated in 1986 and in 2012, using as criteria clone area, shoot density, and shoot length. The dry site model accounted for 40% of gall population density variation between 2003 and 2012 (69% over the 32 yr), providing strong support for the bottom-up, mechanistic hypothesis that water supply to willow hosts impacts sawfly populations. The current drying trend stressed willow clones: in drier sites, willow resources declined and gall density decreased by 98%. The wet site model accounted for 23% of variation in gall population density between 2003 and 2012 (48% over 30 yr), consistent with less water limitation. Nonetheless, gall populations were reduced by 72%. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

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.

Hybrid discrete-continuum modeling for transport, biofilm development and solid restructuring including electrostatic effects

NASA Astrophysics Data System (ADS)

Prechtel, Alexander; Ray, Nadja; Rupp, Andreas

2017-04-01

We want to present an approach for the mathematical, mechanistic modeling and numerical treatment of processes leading to the formation, stability, and turnover of soil micro-aggregates. This aims at deterministic aggregation models including detailed mechanistic pore-scale descriptions to account for the interplay of geochemistry and microbiology, and the link to soil functions as, e.g., the porosity. We therefore consider processes at the pore scale and the mesoscale (laboratory scale). At the pore scale transport by diffusion, advection, and drift emerging from electric forces can be taken into account, in addition to homogeneous and heterogeneous reactions of species. In the context of soil micro-aggregates the growth of biofilms or other glueing substances as EPS (extracellular polymeric substances) is important and affects the structure of the pore space in space and time. This model is upscaled mathematically in the framework of (periodic) homogenization to transfer it to the mesoscale resulting in effective coefficients/parameters there. This micro-macro model thus couples macroscopic equations that describe the transport and fluid flow at the scale of the porous medium (mesoscale) with averaged time- and space-dependent coefficient functions. These functions may be explicitly computed by means of auxiliary cell problems (microscale). Finally, the pore space in which the cell problems are defined is time and space dependent and its geometry inherits information from the transport equation's solutions. The microscale problems rely on versatile combinations of cellular automata and discontiuous Galerkin methods while on the mesoscale mixed finite elements are used. The numerical simulations allow to study the interplay between these processes.

Radiation-Induced Carcinogenesis: Mechanistically Based Differences between Gamma-Rays and Neutrons, and Interactions with DMBA

PubMed Central

Shuryak, Igor; Brenner, David J.; Ullrich, Robert L.

2011-01-01

Different types of ionizing radiation produce different dependences of cancer risk on radiation dose/dose rate. Sparsely ionizing radiation (e.g. γ-rays) generally produces linear or upwardly curving dose responses at low doses, and the risk decreases when the dose rate is reduced (direct dose rate effect). Densely ionizing radiation (e.g. neutrons) often produces downwardly curving dose responses, where the risk initially grows with dose, but eventually stabilizes or decreases. When the dose rate is reduced, the risk increases (inverse dose rate effect). These qualitative differences suggest qualitative differences in carcinogenesis mechanisms. We hypothesize that the dominant mechanism for induction of many solid cancers by sparsely ionizing radiation is initiation of stem cells to a pre-malignant state, but for densely ionizing radiation the dominant mechanism is radiation-bystander-effect mediated promotion of already pre-malignant cell clone growth. Here we present a mathematical model based on these assumptions and test it using data on the incidence of dysplastic growths and tumors in the mammary glands of mice exposed to high or low dose rates of γ-rays and neutrons, either with or without pre-treatment with the chemical carcinogen 7,12-dimethylbenz-alpha-anthracene (DMBA). The model provides a mechanistic and quantitative explanation which is consistent with the data and may provide useful insight into human carcinogenesis. PMID:22194850

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.

Big data, big knowledge: big data for personalized healthcare.

PubMed

Viceconti, Marco; Hunter, Peter; Hose, Rod

2015-07-01

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

Phenotypic screening in cancer drug discovery - past, present and future.

PubMed

Moffat, John G; Rudolph, Joachim; Bailey, David

2014-08-01

There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

Is timing the key to good fruit phenolics?: year 2

USDA-ARS?s Scientific Manuscript database

Despite a century of research, we still lack a concrete, mechanistic understanding of solar radiation and temperature effects on anthocyanin accumulation and composition, crucial for red wine grapes. Our aim was to elucidate the mechanistic response to microclimate of anthocyanin metabolism in Vitis...

Mechanistic Target of Rapamycin-Independent Antidepressant Effects of (R)-Ketamine in a Social Defeat Stress Model.

PubMed

Yang, Chun; Ren, Qian; Qu, Youge; Zhang, Ji-Chun; Ma, Min; Dong, Chao; Hashimoto, Kenji

2018-01-01

The role of the mechanistic target of rapamycin (mTOR) signaling in the antidepressant effects of ketamine is controversial. In addition to mTOR, extracellular signal-regulated kinase (ERK) is a key signaling molecule in prominent pathways that regulate protein synthesis. (R)-Ketamine has a greater potency and longer-lasting antidepressant effects than (S)-ketamine. Here we investigated whether mTOR signaling and ERK signaling play a role in the antidepressant effects of two enantiomers. The effects of mTOR inhibitors (rapamycin and AZD8055) and an ERK inhibitor (SL327) on the antidepressant effects of ketamine enantiomers in the chronic social defeat stress (CSDS) model (n = 7 or 8) and on those of ketamine enantiomers in these signaling pathways in mouse brain regions were examined. The intracerebroventricular infusion of rapamycin or AZD8055 blocked the antidepressant effects of (S)-ketamine, but not (R)-ketamine, in the CSDS model. Furthermore, (S)-ketamine, but not (R)-ketamine, significantly attenuated the decreased phosphorylation of mTOR and its downstream effector, ribosomal protein S6 kinase, in the prefrontal cortex of susceptible mice after CSDS. Pretreatment with SL327 blocked the antidepressant effects of (R)-ketamine but not (S)-ketamine. Moreover, (R)-ketamine, but not (S)-ketamine, significantly attenuated the decreased phosphorylation of ERK and its upstream effector, mitogen-activated protein kinase/ERK kinase, in the prefrontal cortex and hippocampal dentate gyrus of susceptible mice after CSDS. This study suggests that mTOR plays a role in the antidepressant effects of (S)-ketamine, but not (R)-ketamine, and that ERK plays a role in (R)-ketamine's antidepressant effects. Thus, it is unlikely that the activation of mTOR signaling is necessary for antidepressant actions of (R)-ketamine. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Extending positive CLASS results across multiple instructors and multiple classes of Modeling Instruction

NASA Astrophysics Data System (ADS)

Brewe, Eric; Traxler, Adrienne; de la Garza, Jorge; Kramer, Laird H.

2013-12-01

We report on a multiyear study of student attitudes measured with the Colorado Learning Attitudes about Science Survey in calculus-based introductory physics taught with the Modeling Instruction curriculum. We find that five of six instructors and eight of nine sections using Modeling Instruction showed significantly improved attitudes from pre- to postcourse. Cohen’s d effect sizes range from 0.08 to 0.95 for individual instructors. The average effect was d=0.45, with a 95% confidence interval of (0.26-0.64). These results build on previously published results showing positive shifts in attitudes from Modeling Instruction classes. We interpret these data in light of other published positive attitudinal shifts and explore mechanistic explanations for similarities and differences with other published positive shifts.

Ionizing Radiation: The issue of radiation quality

NASA Astrophysics Data System (ADS)

Prise, Kevin; Schettino, Giuseppe

Types of Ionising radiations are differentiated from each other by fundamental characteristics of their energy deposition patterns when they interact with biological materials. At the level of the DNA these non-random patterns drive differences in the yields and distributions of DNA damage patterns and specifically the production of clustered damage or complex lesions. The complex radiation fields found in space bring significant challenges for developing a mechanistic understanding of radiation effects from the perspective of radiation quality as these consist of a diverse range of particle and energy types unique to the space environment. Linear energy transfer, energy deposited per unit track length in units of keV per micron, has long been used as a comparator for different types of radiation but has limitations in that it is an average value. Difference in primary core ionizations relative to secondary delta ray ranges vary significantly with particle mass and energy leading to complex interrelationships with damage production at the cellular level. At the cellular level a greater mechanistic understanding is necessary, linking energy deposition patterns to DNA damage patterns and cellular response, to build appropriate biophysical models that are predictive for different radiation qualities and mixed field exposures. Defined studies using monoenergetic beams delivered under controlled conditions are building quantitative data sets of both initial and long term changes in cells as a basis for a great mechanistic understanding of radiation quality effects of relevance to not only space exposures but clinical application of ion-beams.

Distributed Egg Production Functions for Meloidogyne arenaria in Grape Varieties and Consideration of the Mechanistic Relationship between Plant and Parasite.

PubMed

Ferris, H; Schneider, S M; Semenoff, M C

1984-04-01

Nematode egg production rates, as mediated by environmental conditions and host status, are important determinants of population development. Rates of egg production by Meloidogyne arenaria varied from 0.48 to 1.0 egg per female per DD (degree days above 10 C) in different grape varieties. The length of the egg production period ranged from 550 to 855 DD where measurable, and was generally longer in those varieties where the production rate was slow. We hypothesize that if a successful infection site is established, a constant number of eggs is produced if favorable environmental conditions prevail. Mechanistic coupling structures between plant growth and nematode population models are formulated. The nematode population influences metabolite supply through its effect on physiological efficiency and also acts as a metabolic sink; the degree of plant physiological stress influences nematode population development by affecting the sex ratio and egg production rates.

Mechanistic Insight of Probiotics Derived Anticancer Pharmaceuticals: A Road Forward for Cancer Therapeutics.

PubMed

Kumar, Raman; Dhanda, Suman

2017-04-01

Probiotics are living organisms that confer health benefits when administered in adequate amounts. Probiotics are continuously being explored for their different health beneficiary activities. Anticancer activity is one of the most important benefits both from a preventive and therapeutic point of view. Though not many studies have been conducted to date in this area, a number suggest using laboratory animal models and different cell lines that there may be a mechanistic basis for the anticancer effects of probiotics and require more scientific justification and clinical trials. Most studies of probiotics are conducted for colon cancer associated with inflammatory bowel disease. Studies are also being extended to other types of cancer in different cell lines. This review summarizes studied probiotics considered for treatment of colon cancer and some other cancers (in cancer cell lines) and also proposed mechanism how probiotics are inhibiting cancer growth along with some challenges and future perspectives.

Is solar radiation a key to good red wine grape anthocyanin?

USDA-ARS?s Scientific Manuscript database

Despite a century of research, we still lack a concrete, mechanistic understanding of solar radiation and temperature effects on anthocyanin accumulation and composition, crucial for red wine grapes. Our aim was to elucidate the mechanistic response to microclimate of anthocyanin metabolism in Viti...

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.

Development of Monopole Interaction Models for Ionic Compounds. Part I: Estimation of Aqueous Henry's Law Constants for Ions and Gas Phase pKa Values for Acidic Compounds.

PubMed

Hilal, S H; Saravanaraj, A N; Carreira, L A

2014-02-01

The SPARC (SPARC Performs Automated Reasoning in Chemistry) physicochemical mechanistic models for neutral compounds have been extended to estimate Henry's Law Constant (HLC) for charged species by incorporating ionic electrostatic interaction models. Combinations of absolute aqueous pKa values, relative pKa values in the gas phase, and aqueous HLC for neutral compounds have been used to develop monopole interaction models that quantify the energy differences upon moving an ionic solute molecule from the gas phase to the liquid phase. Inter-molecular interaction energies were factored into mechanistic contributions of monopoles with polarizability, dipole, H-bonding, and resonance. The monopole ionic models were validated by a wide range of measured gas phase pKa data for 450 acidic compounds. The RMS deviation error and R(2) for the OH, SH, CO2 H, CH3 and NR2 acidic reaction centers (C) were 16.9 kcal/mol and 0.87, respectively. The calculated HLCs of ions were compared to the HLCs of 142 ions calculated by quantum mechanics. Effects of inter-molecular interaction of the monopoles with polarizability, dipole, H-bonding, and resonance on acidity of the solutes in the gas phase are discussed. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs

PubMed Central

Weeding, Emma; Houle, Jason

2010-01-01

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications. PMID:20639523

Inferring diffusion dynamics from FCS in heterogeneous nuclear environments.

PubMed

Tsekouras, Konstantinos; Siegel, Amanda P; Day, Richard N; Pressé, Steve

2015-07-07

Fluorescence correlation spectroscopy (FCS) is a noninvasive technique that probes the diffusion dynamics of proteins down to single-molecule sensitivity in living cells. Critical mechanistic insight is often drawn from FCS experiments by fitting the resulting time-intensity correlation function, G(t), to known diffusion models. When simple models fail, the complex diffusion dynamics of proteins within heterogeneous cellular environments can be fit to anomalous diffusion models with adjustable anomalous exponents. Here, we take a different approach. We use the maximum entropy method to show-first using synthetic data-that a model for proteins diffusing while stochastically binding/unbinding to various affinity sites in living cells gives rise to a G(t) that could otherwise be equally well fit using anomalous diffusion models. We explain the mechanistic insight derived from our method. In particular, using real FCS data, we describe how the effects of cell crowding and binding to affinity sites manifest themselves in the behavior of G(t). Our focus is on the diffusive behavior of an engineered protein in 1) the heterochromatin region of the cell's nucleus as well as 2) in the cell's cytoplasm and 3) in solution. The protein consists of the basic region-leucine zipper (BZip) domain of the CCAAT/enhancer-binding protein (C/EBP) fused to fluorescent proteins. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Co-targeting the HER and IGF/insulin receptor axis in breast cancer, with triple targeting with endocrine therapy for hormone-sensitive disease.

PubMed

Chakraborty, Ashok; Hatzis, Christos; DiGiovanna, Michael P

2017-05-01

Interactions between HER2, estrogen receptor (ER), and insulin-like growth factor I receptor (IGF1R) are implicated in resistance to monotherapies targeting these receptors. We have previously shown in pre-clinical studies synergistic anti-tumor effects for co-targeting each pairwise combination of HER2, IGF1R, and ER. Strikingly, synergy for HER2/IGF1R targeting occurred not only in a HER2+ model, but also in a HER2-normal model. The purpose of the current study was therefore to determine the generalizability of synergistic anti-tumor effects of co-targeting HER2/IGF1R, the anti-tumor activity of triple-targeting HER2/IGF1R/ER in hormone-dependent cell lines, and the effect of using the multi-targeting drugs neratinib (pan-HER) and BMS-754807 (dual IGF1R/insulin receptor). Proliferation and apoptosis assays were performed in a large panel of cell lines representing varying receptor expression levels. Mechanistic effects were studied using phospho-protein immunoblotting. Analyses of drug interaction effects were performed using linear mixed-effects regression models. Enhanced anti-proliferative effects of HER/IGF-insulin co-targeting were seen in most, though not all, cell lines, including HER2-normal lines. For ER+ lines, triple targeting with inclusion of anti-estrogen generally resulted in the greatest anti-tumor effects. Double or triple targeting generally resulted in marked increases in apoptosis in the sensitive lines. Mechanistic studies demonstrated that the synergy between drugs was correlated with maximal inhibition of Akt and ERK pathway signaling. Dual HER/IGF-insulin targeting, and triple targeting with inclusion of anti-estrogen drugs, shows striking anti-tumor activity across breast cancer types, and drugs with broader receptor specificity may be more effective than single receptor selective drugs, particularly for ER- cells.

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

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

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.

Incorporating an enzymatic model of effects of temperature, moisture, and substrate supply on soil respiration into an ecosystem model for two forests of northeastern USA

NASA Astrophysics Data System (ADS)

Sihi, Debjani; Davidson, Eric; Chen, Min; Savage, Kathleen; Richardson, Andrew; Keenan, Trevor; Hollinger, David

2017-04-01

Soils represent the largest terrestrial carbon (C) pool, and microbial decomposition of soil organic matter (SOM) to carbon dioxide, also called heterotrophic respiration (Rh), is an important component of the global C cycle. Temperature sensitivity of Rh is often represented with a simple Q10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed to disentangle the confounding factors of apparent temperature sensitivity of SOM decomposition and improve performance of ecosystem models and ESMs. The objective of this work was to incorporate into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen and soluble carbon substrates to the enzymatic reaction site. However, in its current configuration, DAMM depends on assumptions or inputs from other models regarding soil C inputs. Here we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration) by replacing FöBAAR's algorithms for Rh with those of DAMM. Classical root trenching experiments provided data to partition soil CO2 efflux into Rh (trenched plot) and root respiration (untrenched minus trenched plots). We used three years of high-frequency soil flux data from automated soil chambers (trenched and untrenched plots) and landscape-scale ecosystem fluxes from eddy covariance towers from two mid-latitude forests (Harvard Forest, MA and Howland Forest, ME) of northeastern USA to develop and validate the merged model and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal dynamics of Rh compared to the FöBAAR-only model for the Harvard Forest, as indicated by lower cost functions (model-data mismatch). However, DAMM-FöBAAR showed less improvement over FöBAAR-only for the boreal transition forest at Howland. The frequency of droughts is lower at Howland, due to a shallow water table, resulting in only brief water limitation affecting Rh in some years. At both sites, the declining trend of soil respiration during drought episodes was captured by the DAMM-FöBAAR model, but not the FöBAAR-only model, which simulates Rh using only a Q10 type function. Greater confidence in model prediction resulting from the inclusion of mechanistic simulation of moisture limitation on substrate availability, an emergent property of DAMM, depends on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the cost function. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than other commonly used empirical functions.

'Systems toxicology' approach identifies coordinated metabolic responses to copper in a terrestrial non-model invertebrate, the earthworm Lumbricus rubellus.

PubMed

Bundy, Jacob G; Sidhu, Jasmin K; Rana, Faisal; Spurgeon, David J; Svendsen, Claus; Wren, Jodie F; Stürzenbaum, Stephen R; Morgan, A John; Kille, Peter

2008-06-03

New methods are needed for research into non-model organisms, to monitor the effects of toxic disruption at both the molecular and functional organism level. We exposed earthworms (Lumbricus rubellus Hoffmeister) to sub-lethal levels of copper (10-480 mg/kg soil) for 70 days as a real-world situation, and monitored both molecular (cDNA transcript microarrays and nuclear magnetic resonance-based metabolic profiling: metabolomics) and ecological/functional endpoints (reproduction rate and weight change, which have direct relevance to population-level impacts). Both of the molecular endpoints, metabolomics and transcriptomics, were highly sensitive, with clear copper-induced differences even at levels below those that caused a reduction in reproductive parameters. The microarray and metabolomic data provided evidence that the copper exposure led to a disruption of energy metabolism: transcripts of enzymes from oxidative phosphorylation were significantly over-represented, and increases in transcripts of carbohydrate metabolising enzymes (maltase-glucoamylase, mannosidase) had corresponding decreases in small-molecule metabolites (glucose, mannose). Treating both enzymes and metabolites as functional cohorts led to clear inferences about changes in energetic metabolism (carbohydrate use and oxidative phosphorylation), which would not have been possible by taking a 'biomarker' approach to data analysis. Multiple post-genomic techniques can be combined to provide mechanistic information about the toxic effects of chemical contaminants, even for non-model organisms with few additional mechanistic toxicological data. With 70-day no-observed-effect and lowest-observed-effect concentrations (NOEC and LOEC) of 10 and 40 mg kg-1 for metabolomic and microarray profiles, copper is shown to interfere with energy metabolism in an important soil organism at an ecologically and functionally relevant level.

Combining plasma measurements and mechanistic modeling to explore the effect of POPs on type 2 diabetes mellitus in Norwegian women.

PubMed

Rylander, Charlotta; Sandanger, Torkjel Manning; Nøst, Therese Haugdahl; Breivik, Knut; Lund, Eiliv

2015-10-01

The number of studies on persistent organic pollutants (POPs) and type 2 diabetes mellitus (T2DM) is growing steadily. Although concentrations of many POPs in humans have decreased substantially, only some studies consider temporal and inter-individual changes in POP concentrations when assessing exposure. Here we combined plasma measurements with mechanistic modeling to generate complementary exposure measures to our single blood draw after disease diagnosis. Blood was collected between 2003-2006 from 106 subjects with T2DM and 106 age-matched controls, and POP concentrations were compared after adjustment for relevant risk factors and multiple testing. Area under the curve (AUC) of PCB-153 from birth until age 18, representing early-life exposure, and AUC from birth until time of diagnosis were generated as well as examples of life-time exposure trajectories using a mechanistic exposure model. The rank sum of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs, OR=16.9 (95% CI: 3.05-93.6)) as well as β-hexachlorocyclohexane (β-HCH, OR=203.8 (95% CI: 11.5-3620)) and 1, 1-dichloro-2,2-bis(p-chlorophenyl) ethylene (p,p'-DDE, OR=11.3 (95% CI: 2.55-49.9)) were associated with T2DM. Neither of the AUCs reflecting early life exposure and total life-time exposure at the time of diagnosis were associated with the disease. The predicted life course trajectories display clear differences within and between individuals in the past and suggest that a single blood draw provide limited information on POP exposure earlier in life. The predicted AUCs for PCB-153 did not support the positive association between T2DM and measured blood concentration of certain POPs. This may suggest that the model is either too simplistic and/or that strength of the association may vary through life and with time to/past diagnosis. Copyright © 2015 Elsevier Inc. All rights reserved.

Tear gas: an epidemiological and mechanistic reassessment

PubMed Central

Rothenberg, Craig; Achanta, Satyanarayana; Svendsen, Erik R.

2016-01-01

Deployments of tear gas and pepper spray have rapidly increased worldwide. Large amounts of tear gas have been used in densely populated cities, including Cairo, Istanbul, Rio de Janeiro, Manama (Bahrain), and Hong Kong. In the United States, tear gas was used extensively during recent riots in Ferguson, Missouri. Whereas tear gas deployment systems have rapidly improved—with aerial drone systems tested and requested by law enforcement—epidemiological and mechanistic research have lagged behind and have received little attention. Case studies and recent epidemiological studies revealed that tear gas agents can cause lung, cutaneous, and ocular injuries, with individuals affected by chronic morbidities at high risk for complications. Mechanistic studies identified the ion channels TRPV1 and TRPA1 as targets of capsaicin in pepper spray, and of the tear gas agents chloroacetophenone, CS, and CR. TRPV1 and TRPA1 localize to pain‐sensing peripheral sensory neurons and have been linked to acute and chronic pain, cough, asthma, lung injury, dermatitis, itch, and neurodegeneration. In animal models, transient receptor potential inhibitors show promising effects as potential countermeasures against tear gas injuries. On the basis of the available data, a reassessment of the health risks of tear gas exposures in the civilian population is advised, and development of new countermeasures is proposed. PMID:27391380

Evaluation of Long-Term Pavement Performance (LTTP) Climatic Data for Use in Mechanistic-Empirical Pavement Design Guide (MEPDG) Calibration and Other Pavement Analysis

DOT National Transportation Integrated Search

2015-05-01

Improvements in the Long-Term Pavement Performance (LTPP) Programs climate data are needed to support current and future research into climate effects on pavement materials, design, and performance. The calibration and enhancement of the Mechanist...

Small differences in temperature interact with solar radiation to alter anthocyanin in grapes

USDA-ARS?s Scientific Manuscript database

Despite a century of research, we still lack a concrete, mechanistic understanding of solar radiation and temperature effects on anthocyanin accumulation and composition, crucial for red wine grapes. Our aim was to elucidate the mechanistic response to microclimate of anthocyanin metabolism in Viti...

The effect of environmental factors on the implementation of the Mechanistic-empirical pavement design guide (MEPDG).

DOT National Transportation Integrated Search

2011-07-01

Current pavement design based on the AASHTO Design Guide uses an empirical approach from the results of the AASHO Road Test conducted in 1958. To address some of the limitations of the original design guide, AASHTO developed a new guide: Mechanistic ...

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.

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

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...

Modelling the biologic effect of ions with the Local Effect Model

NASA Astrophysics Data System (ADS)

Friedrich, Thomas; Elsässer, Thilo; Durante, Marco; Scholz, Michael

In many cases in radiobiological experiments as well as in ion beam therapy the Local Effect Model (LEM) has proven to be capable to describe the biologic effect of ion irradiation based on the response to X-rays. During the last years, the LEM has been extended to include important processes such as the diffusion of free radicals or the biologic effect enhancement due to clustered lesions of the DNA in a more mechanistic fashion. In its current status the predictive power of the LEM covers a wide range of ions with good quantitative precision. Hence there is potential to also apply the LEM to problems in radiation protection. In this talk, the development stages of the LEM are illustrated. Emphasis is put on the most recent version of the LEM, where spatial distributions of DNA lesions are considered. Applicability, limits and strategies for an advanced model testing are discussed. Finally, planned extensions and applications of the LEM are presented.

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

Mechanistic Modeling of the Effects of Acidosis on Thrombin Generation

DTIC Science & Technology

2015-08-01

Telemedicine and Advanced Technology Research Center; U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD; and Departments of...are the private views of the authors and are not to be construed as official or as reflecting the views of the U.S. Army or of the U.S. Department ...of Defense . This paper has been approved for public release with unlimited distribution. Reprints will not be available from the authors. Address

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.

Position-specific isotope modeling of organic micropollutants transformations through different reaction pathways

NASA Astrophysics Data System (ADS)

Jin, Biao; Rolle, Massimo

2016-04-01

Organic compounds are produced in vast quantities for industrial and agricultural use, as well as for human and animal healthcare [1]. These chemicals and their metabolites are frequently detected at trace levels in fresh water environments where they undergo degradation via different reaction pathways. Compound specific stable isotope analysis (CSIA) is a valuable tool to identify such degradation pathways in different environmental systems. Recent advances in analytical techniques have promoted the fast development and implementation of multi-element CSIA. However, quantitative frameworks to evaluate multi-element stable isotope data and incorporating mechanistic information on the degradation processes [2,3] are still lacking. In this study we propose a mechanism-based modeling approach to simultaneously evaluate concentration as well as bulk and position-specific multi-element isotope evolution during the transformation of organic micropollutants. The model explicitly simulates position-specific isotopologues for those atoms that experience isotope effects and, thereby, provides a mechanistic description of isotope fractionation occurring at different molecular positions. We validate the proposed approach with the concentration and multi-element isotope data of three selected organic micropollutants: dichlorobenzamide (BAM), isoproturon (IPU) and diclofenac (DCF). The model precisely captures the dual element isotope trends characteristic of different reaction pathways and their range of variation consistent with observed multi-element (C, N) bulk isotope fractionation. The proposed approach can also be used as a tool to explore transformation pathways in scenarios for which position-specific isotope data are not yet available. [1] Schwarzenbach, R.P., Egli, T., Hofstetter, T.B., von Gunten, U., Wehrli, B., 2010. Global Water Pollution and Human Health. Annu. Rev. Environ. Resour. doi:10.1146/annurev-environ-100809-125342. [2] Jin, B., Haderlein, S.B., Rolle, M., 2013. Integrated carbon and chlorine isotope modeling: Applications to chlorinated aliphatic hydrocarbons dechlorination. Environ. Sci. Technol. 47, 1443-1451. doi:10.1021/es304053h. [3] Jin, B., Rolle, M., 2014. Mechanistic approach to multi-element isotope modeling of organic contaminant degradation. Chemosphere 95, 131-139. doi:10.1016/j.chemosphere.2013.08.050.

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

Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA

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

Sihi, Debjani; Davidson, Eric A.; Chen, Min

Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs.more » The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.« less

Dynamics of Bacterial Gene Regulatory Networks.

PubMed

Shis, David L; Bennett, Matthew R; Igoshin, Oleg A

2018-05-20

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

Mathematical modeling of gonadotropin-releasing hormone signaling.

PubMed

Pratap, Amitesh; Garner, Kathryn L; Voliotis, Margaritis; Tsaneva-Atanasova, Krasimira; McArdle, Craig A

2017-07-05

Gonadotropin-releasing hormone (GnRH) acts via G-protein coupled receptors on pituitary gonadotropes to control reproduction. These are G q -coupled receptors that mediate acute effects of GnRH on the exocytotic secretion of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), as well as the chronic regulation of their synthesis. GnRH is secreted in short pulses and GnRH effects on its target cells are dependent upon the dynamics of these pulses. Here we overview GnRH receptors and their signaling network, placing emphasis on pulsatile signaling, and how mechanistic mathematical models and an information theoretic approach have helped further this field. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Improving Predictions of Tree Drought Mortality in the Community Land Model Using Hydraulic Physiology Theory and its Effects on Carbon Metabolism

NASA Astrophysics Data System (ADS)

McNellis, B.; Hudiburg, T. W.

2017-12-01

Tree mortality due to drought is predicted to have increasing impacts on ecosystem structure and function during the 21st century. Models can attempt to predict which forests are most at risk from drought, but novel environments may preclude analysis that relies on past observations. The inclusion of more mechanistic detail may reduce uncertainty in predictions, but can also compound model complexity, especially in global models. The Community Land Model version 5 (CLM5), itself a component of the Community Earth System Model (CESM), has recently integrated cohort-based demography into its dynamic vegetation component and is in the process of coupling this demography to a model of plant hydraulic physiology (FATES-Hydro). Previous treatment of drought stress and plant mortality within CLM has been relatively broad, but a detailed hydraulics module represents a key step towards accurate mortality prognosis. Here, we examine the structure of FATES-Hydro with respect to two key physiological attributes: tissue osmotic potentials and embolism refilling. Specifically, we ask how FATES-Hydro captures mechanistic realism within each attribute and how much support there is within the physiological literature for its further elaboration within the model structure. Additionally, connections to broader aspects of carbon metabolism within FATES are explored to better resolve emergent consequences of drought stress on ecosystem function and tree demographics. An on-going field experiment in managed stands of Pinus ponderosa and mixed conifers is assessed for model parameterization and performance across PNW forests, with important implications for future forest management strategy.

Mechanistic movement models to understand epidemic spread.

PubMed

Fofana, Abdou Moutalab; Hurford, Amy

2017-05-05

An overlooked aspect of disease ecology is considering how and why animals come into contact with one and other resulting in disease transmission. Mathematical models of disease spread frequently assume mass-action transmission, justified by stating that susceptible and infectious hosts mix readily, and foregoing any detailed description of host movement. Numerous recent studies have recorded, analysed and modelled animal movement. These movement models describe how animals move with respect to resources, conspecifics and previous movement directions and have been used to understand the conditions for the occurrence and the spread of infectious diseases when hosts perform a type of movement. Here, we summarize the effect of the different types of movement on the threshold conditions for disease spread. We identify gaps in the literature and suggest several promising directions for future research. The mechanistic inclusion of movement in epidemic models may be beneficial for the following two reasons. Firstly, the estimation of the transmission coefficient in an epidemic model is possible because animal movement data can be used to estimate the rate of contacts between conspecifics. Secondly, unsuccessful transmission events, where a susceptible host contacts an infectious host but does not become infected can be quantified. Following an outbreak, this enables disease ecologists to identify 'near misses' and to explore possible alternative epidemic outcomes given shifts in ecological or immunological parameters.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'. © 2017 The Author(s).

Predicting colloid transport through saturated porous media: A critical review

NASA Astrophysics Data System (ADS)

Molnar, Ian L.; Johnson, William P.; Gerhard, Jason I.; Willson, Clinton S.; O'Carroll, Denis M.

2015-09-01

Understanding and predicting colloid transport and retention in water-saturated porous media is important for the protection of human and ecological health. Early applications of colloid transport research before the 1990s included the removal of pathogens in granular drinking water filters. Since then, interest has expanded significantly to include such areas as source zone protection of drinking water systems and injection of nanometals for contaminated site remediation. This review summarizes predictive tools for colloid transport from the pore to field scales. First, we review experimental breakthrough and retention of colloids under favorable and unfavorable colloid/collector interactions (i.e., no significant and significant colloid-surface repulsion, respectively). Second, we review the continuum-scale modeling strategies used to describe observed transport behavior. Third, we review the following two components of colloid filtration theory: (i) mechanistic force/torque balance models of pore-scale colloid trajectories and (ii) approximating correlation equations used to predict colloid retention. The successes and limitations of these approaches for favorable conditions are summarized, as are recent developments to predict colloid retention under the unfavorable conditions particularly relevant to environmental applications. Fourth, we summarize the influences of physical and chemical heterogeneities on colloid transport and avenues for their prediction. Fifth, we review the upscaling of mechanistic model results to rate constants for use in continuum models of colloid behavior at the column and field scales. Overall, this paper clarifies the foundation for existing knowledge of colloid transport and retention, features recent advances in the field, critically assesses where existing approaches are successful and the limits of their application, and highlights outstanding challenges and future research opportunities. These challenges and opportunities include improving mechanistic descriptions, and subsequent correlation equations, for nanoparticle (i.e., Brownian particle) transport through soil, developing mechanistic descriptions of colloid retention in so-called "unfavorable" conditions via methods such as the "discrete heterogeneity" approach, and employing imaging techniques such as X-ray tomography to develop realistic expressions for grain topology and mineral distribution that can aid the development of these mechanistic approaches.

Thermodynamics-based models of transcriptional regulation with gene sequence.

PubMed

Wang, Shuqiang; Shen, Yanyan; Hu, Jinxing

2015-12-01

Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.

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.

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.

The role of photorespiration during the evolution of C4 photosynthesis in the genus Flaveria

PubMed Central

Mallmann, Julia; Heckmann, David; Bräutigam, Andrea; Lercher, Martin J; Weber, Andreas PM; Westhoff, Peter; Gowik, Udo

2014-01-01

C4 photosynthesis represents a most remarkable case of convergent evolution of a complex trait, which includes the reprogramming of the expression patterns of thousands of genes. Anatomical, physiological, and phylogenetic and analyses as well as computational modeling indicate that the establishment of a photorespiratory carbon pump (termed C2 photosynthesis) is a prerequisite for the evolution of C4. However, a mechanistic model explaining the tight connection between the evolution of C4 and C2 photosynthesis is currently lacking. Here we address this question through comparative transcriptomic and biochemical analyses of closely related C3, C3–C4, and C4 species, combined with Flux Balance Analysis constrained through a mechanistic model of carbon fixation. We show that C2 photosynthesis creates a misbalance in nitrogen metabolism between bundle sheath and mesophyll cells. Rebalancing nitrogen metabolism requires anaplerotic reactions that resemble at least parts of a basic C4 cycle. Our findings thus show how C2 photosynthesis represents a pre-adaptation for the C4 system, where the evolution of the C2 system establishes important C4 components as a side effect. DOI: http://dx.doi.org/10.7554/eLife.02478.001 PMID:24935935

Mechanistic Considerations Used in the Development of the PROFIT PCI Failure Model

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

Pankaskie, P. J.

A fuel Pellet-Zircaloy Cladding (thermo-mechanical-chemical) Interactions (PC!) failure model for estimating the probability of failure in !ransient increases in power (PROFIT) was developed. PROFIT is based on 1) standard statistical methods applied to available PC! fuel failure data and 2) a mechanistic analysis of the environmental and strain-rate-dependent stress versus strain characteristics of Zircaloy cladding. The statistical analysis of fuel failures attributable to PCI suggested that parameters in addition to power, transient increase in power, and burnup are needed to define PCI fuel failures in terms of probability estimates with known confidence limits. The PROFIT model, therefore, introduces an environmentalmore » and strain-rate dependent strain energy absorption to failure (SEAF) concept to account for the stress versus strain anomalies attributable to interstitial-disloction interaction effects in the Zircaloy cladding. Assuming that the power ramping rate is the operating corollary of strain-rate in the Zircaloy cladding, then the variables of first order importance in the PCI fuel failure phenomenon are postulated to be: 1. pre-transient fuel rod power, P{sub I}, 2. transient increase in fuel rod power, {Delta}P, 3. fuel burnup, Bu, and 4. the constitutive material property of the Zircaloy cladding, SEAF.« less

SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data.

PubMed

Woodhouse, Steven; Piterman, Nir; Wintersteiger, Christoph M; Göttgens, Berthold; Fisher, Jasmin

2018-05-25

Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge. The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages. SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.

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.

A Mechanistic Study of Arsenic (III) Rejection by Reverse Osmosis and Nanofiltration Membranes

ERIC Educational Resources Information Center

Suzuki, Tasuma

2009-01-01

Reverse osmosis/nanofiltration (RO/NF) membranes are capable to provide an effective barrier for a wide range of contaminants (including disinfection by-products precursors) in a single treatment step. However, solute rejection mechanisms by RO/NF membranes are not well understood. The lack of mechanistic information arises from experimental…

Modeling and experimental characterization of Blackglas(TM) polymer pyrolysis to ceramic and thermodynamic characterization of Blackglas(TM) ceramic

NASA Astrophysics Data System (ADS)

Wang, Feng

2000-10-01

The transformation of Blackglas(TM) polymer to ceramic is characterized by TGA-RGA/MS, Si29 and C13 NMR. Si29 NMR reveals a dependence between the postcure temperature and the microstructure of the resin. The postcure temperature that appears to give optimal mechanical and oxidative properties of Blackglas(TM) ceramic is around 150°C. The pyrolysis processing models, which are the Lumped Parameters Model (LPM), the Mechanistic Kinetic Model (MKM) and the Redistribution Reaction Model (RRM), are developed to provide an effective window of processing parameters rather than a costly, time-consuming trial and error approach. The Lumped Parameters Model (LPM) is developed to study the effects of various parameters such as temperature, curing conditions and heating rates on mass loss during the pyrolysis of resin and green composites. It can be used for the model-predictive control of the pyrolysis process; The Mechanistic Kinetic Model (MKM) is developed on the basis of known chemistry and architecture of the polysiloxane for the transformation of Blackglas(TM) polymer to ceramic and the evolution of gases. The effects of various heating protocols on the outgassing kinetics have been studied to develop an optimum protocol for a rapid pyrolysis process which gives a composite with desirable mechanical properties; The Redistribution Reaction Model (RRM) is proposed to describe how the microcompositions of silicon oxycarbide change with respect to temperature, and to the ratio O/Si in the polymer precursor. A Thermodynamic Additivity Model (TAM) is developed to estimate the heat capacity, standard heat of formation and entropy of Blackglas(TM) ceramic by means of the Neumann Kopp rule and the available thermodynamic data of the Si-C and Si-O systems. Thermal stability of this ceramic is investigated by constructing predominance diagrams, and it is shown that the internal degradation reactions, which account for a significant loss of strength, will proceed further in the Blackglas(TM) matrix than in the Nicalon fibers. This probably will induce failure in the matrix at lower temperatures than in the fibers. The predominance diagrams also explain the high temperature oxidation, reduction and volatilization experiments on silicon and silicon carbide in high vacuum.

Effect of coffee (caffeine) against human cataract blindness

PubMed Central

Varma, Shambhu D

2016-01-01

Previous biochemical and morphological studies with animal experiments have demonstrated that caffeine given topically or orally to certain experimental animal models has significant inhibitory effect on cataract formation. The present studies were undertaken to examine if there is a correlation between coffee drinking and incidence of cataract blindness in human beings. That has been found to be the case. Incidence of cataract blindness was found to be significantly lower in groups consuming higher amounts of coffee in comparison to the groups with lower coffee intake. Mechanistically, the caffeine effect could be multifactorial, involving its antioxidant as well as its bioenergetic effects on the lens. PMID:26869755

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.

Mechanistic characterization of the HDV genomic ribozyme: solvent isotope effects and proton inventories in the absence of divalent metal ions support C75 as the general acid.

PubMed

Cerrone-Szakal, Andrea L; Siegfried, Nathan A; Bevilacqua, Philip C

2008-11-05

The hepatitis delta virus (HDV) ribozyme uses the nucleobase C75 and a hydrated Mg(2+) ion as the general acid-base catalysts in phosphodiester bond cleavage at physiological salt. A mechanistic framework has been advanced that involves one Mg(2+)-independent and two Mg(2+)-dependent channels. The rate-pH profile for wild-type (WT) ribozyme in the Mg(2+)-free channel is inverted relative to the fully Mg(2+)-dependent channel, with each having a near-neutral pKa. Inversion of the rate-pH profile was used as the crux of a mechanistic argument that C75 serves as general acid both in the presence and absence of Mg(2+). However, subsequent studies on a double mutant (DM) ribozyme suggested that the pKa observed for WT in the absence of Mg(2+) arises from ionization of C41, a structural nucleobase. To investigate this further, we acquired rate-pH/pD profiles and proton inventories for WT and DM in the absence of Mg(2+). Corrections were made for effects of ionic strength on hydrogen ion activity and pH meter readings. Results are accommodated by a model wherein the Mg(2+)-free pKa observed for WT arises from ionization of C75, and DM reactivity is compromised by protonation of C41. The Brønsted base appears to be water or hydroxide ion depending on pH. The observed pKa's are related to salt-dependent pH titrations of a model oligonucleotide, as well as electrostatic calculations, which support the local environment for C75 in the absence of Mg(2+) being similar to that in the presence of Mg(2+) and impervious to bulk ions. Accordingly, the catalytic role of C75 as the general acid does not appear to depend on divalent ions or the identity of the Brønsted base.

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

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

A nested mechanistic sub-study into the effect of tranexamic acid versus placebo on intracranial haemorrhage and cerebral ischaemia in isolated traumatic brain injury: study protocol for a randomised controlled trial (CRASH-3 Trial Intracranial Bleeding Mechanistic Sub-Study [CRASH-3 IBMS]).

PubMed

Mahmood, Abda; Roberts, Ian; Shakur, Haleema

2017-07-17

Tranexamic acid prevents blood clots from breaking down and reduces bleeding. However, it is uncertain whether tranexamic acid is effective in traumatic brain injury. The CRASH-3 trial is a randomised controlled trial that will examine the effect of tranexamic acid (versus placebo) on death and disability in 13,000 patients with traumatic brain injury. The CRASH-3 trial hypothesizes that tranexamic acid will reduce intracranial haemorrhage, which will reduce the risk of death. Although it is possible that tranexamic acid will reduce intracranial bleeding, there is also a potential for harm. In particular, tranexamic acid may increase the risk of cerebral thrombosis and ischaemia. The protocol detailed here is for a mechanistic sub-study nested within the CRASH-3 trial. This mechanistic sub-study aims to examine the effect of tranexamic acid (versus placebo) on intracranial bleeding and cerebral ischaemia. The CRASH-3 Intracranial Bleeding Mechanistic Sub-Study (CRASH-3 IBMS) is nested within a prospective, double-blind, multi-centre, parallel-arm randomised trial called the CRASH-3 trial. The CRASH-3 IBMS will be conducted in a cohort of approximately 1000 isolated traumatic brain injury patients enrolled in the CRASH-3 trial. In the CRASH-3 IBMS, brain scans acquired before and after randomisation are examined, using validated methods, for evidence of intracranial bleeding and cerebral ischaemia. The primary outcome is the total volume of intracranial bleeding measured on computed tomography after randomisation, adjusting for baseline bleeding volume. Secondary outcomes include progression of intracranial haemorrhage (from pre- to post-randomisation scans), new intracranial haemorrhage (seen on post- but not pre-randomisation scans), intracranial haemorrhage following neurosurgery, and new focal ischaemic lesions (seen on post-but not pre-randomisation scans). A linear regression model will examine whether receipt of the trial treatment can predict haemorrhage volume. Bleeding volumes and new ischaemic lesions will be compared across treatment groups using relative risks and 95% confidence intervals. The CRASH-3 IBMS will provide an insight into the mechanism of action of tranexamic acid in traumatic brain injury, as well as information about the risks and benefits. Evidence from this trial could inform the management of patients with traumatic brain injury. The CRASH-3 trial was prospectively registered and the CRASH-3 IBMS is an addition to the original protocol registered at the International Standard Randomised Controlled Trials registry ( ISRCTN15088122 ) 19 July 2011, and ClinicalTrials.gov on 25 July 2011 (NCT01402882).

Mathematical modelling of the antibiotic-induced morphological transition of Pseudomonas aeruginosa

PubMed Central

Keen, Emma; Smith, David J.

2018-01-01

Here we formulate a mechanistic mathematical model to describe the growth dynamics of P. aeruginosa in the presence of the β-lactam antibiotic meropenem. The model is mechanistic in the sense that carrying capacity is taken into account through the dynamics of nutrient availability rather than via logistic growth. In accordance with our experimental results we incorporate a sub-population of cells, differing in morphology from the normal bacillary shape of P. aeruginosa bacteria, which we assume have immunity from direct antibiotic action. By fitting this model to experimental data we obtain parameter values that give insight into the growth of a bacterial population that includes different cell morphologies. The analysis of two parameters sets, that produce different long term behaviour, allows us to manipulate the system theoretically in order to explore the advantages of a shape transition that may potentially be a mechanism that allows P. aeruginosa to withstand antibiotic effects. Our results suggest that inhibition of this shape transition may be detrimental to bacterial growth and thus suggest that the transition may be a defensive mechanism implemented by bacterial machinery. In addition to this we provide strong theoretical evidence for the potential therapeutic strategy of using antimicrobial peptides (AMPs) in combination with meropenem. This proposed combination therapy exploits the shape transition as AMPs induce cell lysis by forming pores in the cytoplasmic membrane, which becomes exposed in the spherical cells. PMID:29481562

Analysis of Initial Cell Spreading Using Mechanistic Contact Formulations for a Deformable Cell Model

PubMed Central

Odenthal, Tim; Smeets, Bart; Van Liedekerke, Paul; Tijskens, Engelbert; Van Oosterwyck, Hans; Ramon, Herman

2013-01-01

Adhesion governs to a large extent the mechanical interaction between a cell and its microenvironment. As initial cell spreading is purely adhesion driven, understanding this phenomenon leads to profound insight in both cell adhesion and cell-substrate interaction. It has been found that across a wide variety of cell types, initial spreading behavior universally follows the same power laws. The simplest cell type providing this scaling of the radius of the spreading area with time are modified red blood cells (RBCs), whose elastic responses are well characterized. Using a mechanistic description of the contact interaction between a cell and its substrate in combination with a deformable RBC model, we are now able to investigate in detail the mechanisms behind this universal power law. The presented model suggests that the initial slope of the spreading curve with time results from a purely geometrical effect facilitated mainly by dissipation upon contact. Later on, the spreading rate decreases due to increasing tension and dissipation in the cell's cortex as the cell spreads more and more. To reproduce this observed initial spreading, no irreversible deformations are required. Since the model created in this effort is extensible to more complex cell types and can cope with arbitrarily shaped, smooth mechanical microenvironments of the cells, it can be useful for a wide range of investigations where forces at the cell boundary play a decisive role. PMID:24146605

Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA

USGS Publications Warehouse

Nolan, Bernard T.; Malone, Robert W.; Gronberg, Jo Ann M.; Thorp, K.R.; Ma, Liwang

2012-01-01

Nitrate leaching in the unsaturated zone poses a risk to groundwater, whereas nitrate in tile drainage is conveyed directly to streams. We developed metamodels (MMs) consisting of artificial neural networks to simplify and upscale mechanistic fate and transport models for prediction of nitrate losses by drains and leaching in the Corn Belt, USA. The two final MMs predicted nitrate concentration and flux, respectively, in the shallow subsurface. Because each MM considered both tile drainage and leaching, they represent an integrated approach to vulnerability assessment. The MMs used readily available data comprising farm fertilizer nitrogen (N), weather data, and soil properties as inputs; therefore, they were well suited for regional extrapolation. The MMs effectively related the outputs of the underlying mechanistic model (Root Zone Water Quality Model) to the inputs (R2 = 0.986 for the nitrate concentration MM). Predicted nitrate concentration was compared with measured nitrate in 38 samples of recently recharged groundwater, yielding a Pearson’s r of 0.466 (p = 0.003). Predicted nitrate generally was higher than that measured in groundwater, possibly as a result of the time-lag for modern recharge to reach well screens, denitrification in groundwater, or interception of recharge by tile drains. In a qualitative comparison, predicted nitrate concentration also compared favorably with results from a previous regression model that predicted total N in streams.

On Spatially Explicit Models of Epidemic and Endemic Cholera: The Haiti and Lake Kivu Case Studies.

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Bertuzzo, E.; Mari, L.; Finger, F.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.

2014-12-01

The first part of the Lecture deals with the predictive ability of mechanistic models for the Haitian cholera epidemic. Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. A formal model comparison framework provides a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels. Intensive computations and objective model comparisons show that parsimonious spatially explicit models accounting for spatial connections have superior explanatory power than spatially disconnected ones for short-to intermediate calibration windows. In general, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. The second part deals with approaches suitable to describe patterns of endemic cholera. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of lake Kivu. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multi-year dataset of reported cholera cases. Fourteen models, accounting for different environmental drivers, are selected in calibration. Among these, the one accounting for seasonality, El Nino Southern Oscillation, precipitation and human mobility outperforms the others in cross-validation.

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.

Mesoscale density variability in the mesosphere and thermosphere: Effects of vertical flow accelerations

NASA Technical Reports Server (NTRS)

Revelle, D. O.

1987-01-01

A mechanistic one dimensional numerical (iteration) model was developed which can be used to simulate specific types of mesoscale atmospheric density (and pressure) variability in the mesosphere and the thermosphere, namely those due to waves and those due to vertical flow accelerations. The model was developed with the idea that it could be used as a supplement to the TGCMs (thermospheric general circulation models) since such models have a very limited ability to model phenomena on small spatial scales. The simplest case to consider was the integration upward through a time averaged, height independent, horizontally divergent flow field. Vertical winds were initialized at the lower boundary using the Ekman pumping theory over flat terrain. The results of the computations are summarized.

Modeling the Effects of Lipid Composition on Stratum Corneum Bilayers Using Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Huzil, J. Torin; Sivaloganathan, Siv; Kohandel, Mohammad; Foldvari, Marianna

2011-11-01

The advancement of dermal and transdermal drug delivery requires the development of delivery systems that are suitable for large protein and nucleic acid-based therapeutic agents. However, a complete mechanistic understanding of the physical barrier properties associated with the epidermis, specifically the membrane structures within the stratum corneum, has yet to be developed. Here, we describe the assembly and computational modeling of stratum corneum lipid bilayers constructed from varying ratios of their constituent lipids (ceramide, free fatty acids and cholesterol) to determine if there is a difference in the physical properties of stratum corneum compositions.

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...

Effects of nutrient and water restriction on thermal tolerance: A test of mechanisms and hypotheses.

PubMed

Mitchell, Katherine A; Boardman, Leigh; Clusella-Trullas, Susana; Terblanche, John S

2017-10-01

Nutritional deprivation or desiccation can influence thermal tolerance by impacting the insects' ability to evaporatively cool, maintain cell membrane integrity and conduct protective or repair processes. Recovery from chilling is also linked to the re-establishment of iono- and osmo-regulatory homeostasis. Here, using Mediterranean fruit fly (Ceratitis capitata, Diptera: Tephritidae), we manipulated water and nutrient availability to test the mechanistic expectation that changes in whole organism lipid and water content can elicit variation in cold or heat tolerance (scored as chill coma recovery time and heat knockdown time). We measured body condition (body water and lipid content) as well as heat shock protein 70 gene (hsp70) and protein (HSP70) levels. A significant reduction in body water content with water restriction did not translate into differences in chill coma recovery. When nutrient restriction was coupled with water deprivation, this resulted in a significant reduction (-54%) of heat knockdown time in females but male flies were unaffected. There was no evidence for an hsp70 or HSP70 response under any of the stress treatments and therefore no correlation with heat or cold tolerance. Heat hardening decreased all hsp levels. Therefore, although body water and total body lipid content differed between the treatment groups, the contribution of these factors to thermal tolerance was inconsistent with mechanistic expectations in heat knockdown time and insignificant for chill coma recovery. These results therefore highlight that the effects of resource restriction on thermal limits in insects are mechanistically more complex than previous models of stress resistance have suggested. Copyright © 2017 Elsevier Inc. All rights reserved.

Challenges in Developing Models Describing Complex Soil Systems

NASA Astrophysics Data System (ADS)

Simunek, J.; Jacques, D.

2014-12-01

Quantitative mechanistic models that consider basic physical, mechanical, chemical, and biological processes have the potential to be powerful tools to integrate our understanding of complex soil systems, and the soil science community has often called for models that would include a large number of these diverse processes. However, once attempts have been made to develop such models, the response from the community has not always been overwhelming, especially after it discovered that these models are consequently highly complex, requiring not only a large number of parameters, not all of which can be easily (or at all) measured and/or identified, and which are often associated with large uncertainties, but also requiring from their users deep knowledge of all/most of these implemented physical, mechanical, chemical and biological processes. Real, or perceived, complexity of these models then discourages users from using them even for relatively simple applications, for which they would be perfectly adequate. Due to the nonlinear nature and chemical/biological complexity of the soil systems, it is also virtually impossible to verify these types of models analytically, raising doubts about their applicability. Code inter-comparisons, which is then likely the most suitable method to assess code capabilities and model performance, requires existence of multiple models of similar/overlapping capabilities, which may not always exist. It is thus a challenge not only to developed models describing complex soil systems, but also to persuade the soil science community in using them. As a result, complex quantitative mechanistic models are still an underutilized tool in soil science research. We will demonstrate some of the challenges discussed above on our own efforts in developing quantitative mechanistic models (such as HP1/2) for complex soil systems.

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.

From the exposome to mechanistic understanding of chemical ...

EPA Pesticide Factsheets

BACKGROUND: Current definitions of the exposome expand beyond the initial idea to consider the totality of exposure and aim to relate to biological effects. While the exposome has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. OBJECTIVES: We explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept - a framework to structure and organize the sequence of toxicological events from an initial molecular interaction of a chemical to an adverse outcome.METHODS: This review was informed by a Workshop organized by the Integrated Project EXPOSOME at the UFZ Helmholtz Centre for Environmental Research in Leipzig, Germany. DISCUSSION: The exposome encompasses all chemicals, including exogenous chemicals and endogenous compounds that are produced in response to external factors. By complementing the exposome research with the AOP concept, we can achieve a better mechanistic understanding, weigh the importance of various components of the exposome, and determine primary risk drivers. The ability to interpret multiple exposures and mixture effects at the mechanistic level requires a more holistic approach facilitated by the exposome concept.CONCLUSION: Incorporating the AOP conc

Explanation and inference: mechanistic and functional explanations guide property generalization.

PubMed

Lombrozo, Tania; Gwynne, Nicholas Z

2014-01-01

The ability to generalize from the known to the unknown is central to learning and inference. Two experiments explore the relationship between how a property is explained and how that property is generalized to novel species and artifacts. The experiments contrast the consequences of explaining a property mechanistically, by appeal to parts and processes, with the consequences of explaining the property functionally, by appeal to functions and goals. The findings suggest that properties that are explained functionally are more likely to be generalized on the basis of shared functions, with a weaker relationship between mechanistic explanations and generalization on the basis of shared parts and processes. The influence of explanation type on generalization holds even though all participants are provided with the same mechanistic and functional information, and whether an explanation type is freely generated (Experiment 1), experimentally provided (Experiment 2), or experimentally induced (Experiment 2). The experiments also demonstrate that explanations and generalizations of a particular type (mechanistic or functional) can be experimentally induced by providing sample explanations of that type, with a comparable effect when the sample explanations come from the same domain or from a different domains. These results suggest that explanations serve as a guide to generalization, and contribute to a growing body of work supporting the value of distinguishing mechanistic and functional explanations.

Use of high-throughput in vitro toxicity screening data in cancer hazard evaluations by IARC Monograph Working Groups

PubMed Central

Chiu, Weihsueh A.; Guyton, Kathryn Z.; Martin, Matthew T.; Reif, David M.; Rusyn, Ivan

2017-01-01

Evidence regarding carcinogenic mechanisms serves a critical role in International Agency for Research on Cancer (IARC) Monograph evaluations. Three recent IARC Working Groups pioneered inclusion of the US Environmental Protection Agency (EPA) ToxCast program high-throughput screening (HTS) data to supplement other mechanistic evidence. In Monograph V110, HTS profiles were compared between perfluorooctanoic acid (PFOA) and prototypical activators across multiple nuclear receptors. For Monograph V112 -113, HTS assays were mapped to 10 key characteristics of carcinogens identified by an IARC expert group, and systematically considered as an additional mechanistic data stream. Both individual assay results and ToxPi-based rankings informed mechanistic evaluations. Activation of multiple nuclear receptors in HTS assays showed that PFOA targets peroxisome proliferator activated and other receptors. ToxCast assays substantially covered 5 of 10 key characteristics, corroborating literature evidence of “induces oxidative stress” and “alters cell proliferation, cell death or nutrient supply” and filling gaps for “modulates receptor-mediated effects.” Thus, ToxCast HTS data were useful both in evaluating specific mechanistic hypotheses and in the overall evaluation of mechanistic evidence. However, additional HTS assays are needed to provide more comprehensive coverage of the 10 key characteristics of carcinogens that form the basis of current IARC mechanistic evaluations. PMID:28738424

Use of high-throughput in vitro toxicity screening data in cancer hazard evaluations by IARC Monograph Working Groups.

PubMed

Chiu, Weihsueh A; Guyton, Kathryn Z; Martin, Matthew T; Reif, David M; Rusyn, Ivan

2018-01-01

Evidence regarding carcinogenic mechanisms serves a critical role in International Agency for Research on Cancer (IARC) Monograph evaluations. Three recent IARC Working Groups pioneered inclusion of the US Environmental Protection Agency (EPA) ToxCast program high-throughput screening (HTS) data to supplement other mechanistic evidence. In Monograph V110, HTS profiles were compared between perfluorooctanoic acid (PFOA) and prototypical activators across multiple nuclear receptors. For Monograph V112-113, HTS assays were mapped to 10 key characteristics of carcinogens identified by an IARC expert group, and systematically considered as an additional mechanistic data stream. Both individual assay results and ToxPi-based rankings informed mechanistic evaluations. Activation of multiple nuclear receptors in HTS assays showed that PFOA targets not only peroxisome proliferator activated receptors, but also other receptors. ToxCast assays substantially covered 5 of 10 key characteristics, corroborating literature evidence of "induces oxidative stress" and "alters cell proliferation, cell death or nutrient supply" and filling gaps for "modulates receptor-mediated effects." Thus, ToxCast HTS data were useful both in evaluating specific mechanistic hypotheses and in contributing to the overall evaluation of mechanistic evidence. However, additional HTS assays are needed to provide more comprehensive coverage of the 10 key characteristics of carcinogens that form the basis of current IARC mechanistic evaluations.

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.

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/.

Meditative Movement, Energetic, and Physical Analyses of Three Qigong Exercises: Unification of Eastern and Western Mechanistic Exercise Theory.

PubMed

Klein, Penelope; Picard, George; Baumgarden, Joseph; Schneider, Roger

2017-09-23

Abstract : Qigong is the meditative movement and therapeutic exercise of Eastern medicine. A growing body of evidence is validating its health benefits leading to mechanistic questions of how it works. The purpose of this article is to explore mechanisms of action related to Qigong, with the intent of unifying Eastern and Western exercise theory and to present a model for Qigong exercise analysis. Three exercises from a standardized Qigong form: 'Plucking the Stars', 'Lotus Leaves Rustle in the Wind', and 'Pacing Forwards and Backwards' were selected for meditative, energetic, and physical analyses. Meditative aspects include relaxation response, interoception and exteroception. Energetic aspects include stimulation of meridians through mental intent, acupressure, and self-massage. Physical aspects include flexibility, strength, articular stimulation, neuro-integration, respiratory effect, fascial stretch, visceral massage, balance challenge CranioSacral pump, lymphatic and venous return and glandular stimulation, and physiologic response to relaxation. Knowledge of mechanisms of action for specific Qigong exercises can guide operational definition of Qigong, selection of outcomes assessment in future research, inform prescriptive practice addressing clinical health issues, and advance adoption of Qigong practice within integrative health care. The model of analysis demonstrated in this discussion may assist in these endeavors.

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

Emerging Drugs for the Treatment of Anxiety

PubMed Central

Murrough, James W.; Yaqubi, Sahab; Sayed, Sehrish; Charney, Dennis S.

2016-01-01

Introduction Anxiety disorders are among the most prevalent and disabling psychiatric disorders in the United States and worldwide. Basic research has provided critical insights into the mechanism regulating fear behavior in animals and a host of animal models have been developed in order to screen compounds for anxiolytic properties. Despite this progress, no mechanistically novel agents for the treatment of anxiety have come to market in more than two decades. Areas covered The current review will provide a critical summary of current pharmacological approaches to the treatment of anxiety and will examine the pharmacotherapeutic pipeline for treatments in development. Anxiety and related disorders considered herein include panic disorder, social anxiety disorder, generalized anxiety disorder and posttraumatic stress disorder. The glutamate, neuropeptide and endocannabinoid systems show particular promise as future targets for novel drug development. Expert opinion In the face of an ever-growing understanding of fear related behavior, the field awaits the translation of this research into mechanistically novel treatments. Obstacles will be overcome through close collaboration between basic and clinical researchers with the goal of aligning valid endophenotypes of human anxiety disorders with improved animal models. Novel approaches are needed to move basic discoveries into new, more effective treatments for our patients. PMID:26012843

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.

An Assessment of Fission Product Scrubbing in Sodium Pools Following a Core Damage Event in a Sodium Cooled Fast Reactor

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

Bucknor, M.; Farmer, M.; Grabaskas, D.

The U.S. Nuclear Regulatory Commission has stated that mechanistic source term (MST) calculations are expected to be required as part of the advanced reactor licensing process. A recent study by Argonne National Laboratory has concluded that fission product scrubbing in sodium pools is an important aspect of an MST calculation for a sodium-cooled fast reactor (SFR). To model the phenomena associated with sodium pool scrubbing, a computational tool, developed as part of the Integral Fast Reactor (IFR) program, was utilized in an MST trial calculation. This tool was developed by applying classical theories of aerosol scrubbing to the decontamination ofmore » gases produced as a result of postulated fuel pin failures during an SFR accident scenario. The model currently considers aerosol capture by Brownian diffusion, inertial deposition, and gravitational sedimentation. The effects of sodium vapour condensation on aerosol scrubbing are also treated. This paper provides details of the individual scrubbing mechanisms utilized in the IFR code as well as results from a trial mechanistic source term assessment led by Argonne National Laboratory in 2016.« less

Development and evaluation of a dimensionless mechanistic pan coating model for the prediction of coated tablet appearance.

PubMed

Niblett, Daniel; Porter, Stuart; Reynolds, Gavin; Morgan, Tomos; Greenamoyer, Jennifer; Hach, Ronald; Sido, Stephanie; Karan, Kapish; Gabbott, Ian

2017-08-07

A mathematical, mechanistic tablet film-coating model has been developed for pharmaceutical pan coating systems based on the mechanisms of atomisation, tablet bed movement and droplet drying with the main purpose of predicting tablet appearance quality. Two dimensionless quantities were used to characterise the product properties and operating parameters: the dimensionless Spray Flux (relating to area coverage of the spray droplets) and the Niblett Number (relating to the time available for drying of coating droplets). The Niblett Number is the ratio between the time a droplet needs to dry under given thermodynamic conditions and the time available for the droplet while on the surface of the tablet bed. The time available for drying on the tablet bed surface is critical for appearance quality. These two dimensionless quantities were used to select process parameters for a set of 22 coating experiments, performed over a wide range of multivariate process parameters. The dimensionless Regime Map created can be used to visualise the effect of interacting process parameters on overall tablet appearance quality and defects such as picking and logo bridging. Copyright © 2017 Elsevier B.V. All rights reserved.

Biosorption of As(III) and As(V) on the surface of TW/MnFe2O4 composite from wastewater: kinetics, mechanistic and thermodynamics

NASA Astrophysics Data System (ADS)

Podder, M. S.; Majumder, C. B.

2017-10-01

In the present study, TW/MnFe2O4 composite (MTW) was synthesized and estimated as an effective biosorbent for removing As (III) and As(V) from wastewater. Physicochemical analysis of composite was performed through SEM-EDX. 86.615 and 83.478% removal efficiency were obtained by composite dosage of 2 g/L at contact time 120 min at temperature 30 °C and pH 7.0 and 4.0 for As(III) and As(V), respectively. Kinetic results study showed that Brouers-Weron-Sotolongo and Ritchie second-order for As(III) and Brouers-Weron-Sotolongo model for As(V) were capable to describe an accurate explanation of adsorption kinetic. Applicability of mechanistic models in the current study exposed that the rate-controlling step in the biosorption of both As(III) and As(V) on the surface of composite was film diffusion rather than intraparticle diffusion. The estimated thermodynamic parameters Δ G 0, Δ H 0 and Δ S 0 revealed that the biosorption of both As(III) and As(V) on the composite was feasible, spontaneous and exothermic.

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.

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.

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...

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.

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.

Remote sensing of plant-water relations: An overview and future perspectives.

PubMed

Damm, A; Paul-Limoges, E; Haghighi, E; Simmer, C; Morsdorf, F; Schneider, F D; van der Tol, C; Migliavacca, M; Rascher, U

2018-04-25

Vegetation is a highly dynamic component of the Earth surface and substantially alters the water cycle. Particularly the process of oxygenic plant photosynthesis determines vegetation connecting the water and carbon cycle and causing various interactions and feedbacks across Earth spheres. While vegetation impacts the water cycle, it reacts to changing water availability via functional, biochemical and structural responses. Unravelling the resulting complex feedbacks and interactions between the plant-water system and environmental change is essential for any modelling approaches and predictions, but still insufficiently understood due to currently missing observations. We hypothesize that an appropriate cross-scale monitoring of plant-water relations can be achieved by combined observational and modelling approaches. This paper reviews suitable remote sensing approaches to assess plant-water relations ranging from pure observational to combined observational-modelling approaches. We use a combined energy balance and radiative transfer model to assess the explanatory power of pure observational approaches focussing on plant parameters to estimate plant-water relations, followed by an outline for a more effective use of remote sensing by their integration into soil-plant-atmosphere continuum (SPAC) models. We apply a mechanistic model simulating water movement in the SPAC to reveal insight into the complexity of relations between soil, plant and atmospheric parameters, and thus plant-water relations. We conclude that future research should focus on strategies combining observations and mechanistic modelling to advance our knowledge on the interplay between the plant-water system and environmental change, e.g. through plant transpiration. Copyright © 2018 Elsevier GmbH. All rights reserved.

The Effect of Fiber Architecture on Matrix Cracking in Sic/sic Cmc's

NASA Technical Reports Server (NTRS)

Morscher, Gregory N.

2005-01-01

Applications incorporating silicon carbide fiber reinforced silicon carbide matrix composites (CMC's) will require a wide range of fiber architectures in order to fabricate complex shape. The stress-strain response of a given SiC/SiC system for different architectures and orientations will be required in order to design and effectively life-model future components. The mechanism for non-linear stress-strain behavior in CMC's is the formation and propagation of bridged-matrix cracks throughout the composite. A considerable amount of understanding has been achieved for the stress-dependent matrix cracking behavior of SiC fiber reinforced SiC matrix systems containing melt-infiltrated Si. This presentation will outline the effect of 2D and 3D architectures and orientation on stress-dependent matrix-cracking and how this information can be used to model material behavior and serve as the starting point foe mechanistic-based life-models.

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...

The Constitutive Modeling of Thin Films with Randon Material Wrinkles

NASA Technical Reports Server (NTRS)

Murphey, Thomas W.; Mikulas, Martin M.

2001-01-01

Material wrinkles drastically alter the structural constitutive properties of thin films. Normally linear elastic materials, when wrinkled, become highly nonlinear and initially inelastic. Stiffness' reduced by 99% and negative Poisson's ratios are typically observed. This paper presents an effective continuum constitutive model for the elastic effects of material wrinkles in thin films. The model considers general two-dimensional stress and strain states (simultaneous bi-axial and shear stress/strain) and neglects out of plane bending. The constitutive model is derived from a traditional mechanics analysis of an idealized physical model of random material wrinkles. Model parameters are the directly measurable wrinkle characteristics of amplitude and wavelength. For these reasons, the equations are mechanistic and deterministic. The model is compared with bi-axial tensile test data for wrinkled Kaptong(Registered Trademark) HN and is shown to deterministically predict strain as a function of stress with an average RMS error of 22%. On average, fitting the model to test data yields an RMS error of 1.2%

Mechanistic exploration of a bi-directional PDT-based combination in pancreatic cancer (Conference Presentation)

NASA Astrophysics Data System (ADS)

Huang, Huang-Chiao; Mallidi, Srivalleesha; Liu, Joyce; Chiang, Chun-Te; Mai, Zhiming; Goldschmidt, Ruth; Rizvi, Imran; Ebrahim-Zadeh, Neema; Hasan, Tayyaba

2016-03-01

It is increasingly evident that the most effective cancer treatments will involve interactive regimens that target multiple non-overlapping pathways, preferably such that each component enhances the others to improve outcomes while minimizing systemic toxicities. Toward this goal, we developed a combination of photodynamic therapy and irinotecan, which mechanistically cooperate with each other, beyond their individual tumor destruction pathways, to cause synergistic reduction in orthotopic pancreatic tumor burden. A three-way mechanistic basis of the observed the synergism will be discussed: (i) PDT downregulates drug efflux transporters to increase intracellular irinotecan levels. (ii) Irinotecan reduces the expression of hypoxia-induced marker, which is upregulated by PDT. (iii) PDT downregulates irinotecan-induced survivin expression to amplify the apoptotic and anti-proliferative effects. The clinical translation potential of the combination will also be highlighted.

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?

Electron-conformational transformations govern the temperature dependence of the cardiac ryanodine receptor gating

NASA Astrophysics Data System (ADS)

Moskvin, A. S.; Iaparov, B. I.; Ryvkin, A. M.; Solovyova, O. E.; Markhasin, V. S.

2015-07-01

Temperature influences many aspects of cardiac excitation-contraction coupling, in particular, hypothermia increases the open probability ( P open) of cardiac sarcoplasmic reticulum (SR) Ca2+-release channels (ryanodine-sensitive RyR channels) rising the SR Ca2+ load in mammalian myocytes. However, to the best of our knowledge, no theoretical models are available for that effect. Traditional Markov chain models do not provide a reasonable molecular mechanistic insight on the origin of the temperature effects. Here in the paper we address a simple physically clear electron-conformational model to describe the RyR gating and argue that a synergetic effect of external thermal fluctuation forces (Gaussian-Markovian noise) and internal friction via the temperature stimulation/suppression of the open-close RyR tunneling probability can be considered as a main contributor to temperature effects on the RyR gating. Results of the computer modeling allowed us to successfully reproduce all the temperature effects observed for an isolated RyR gating in vitro under reducing the temperature: increase in P open and mean open time without any significant effect on mean closed

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.

Descriptive vs mechanistic scientific approach to study wound healing and its inhibition: Is there a value of translational research involving human subjects?

PubMed

Pastar, Irena; Wong, Lulu L; Egger, Andjela N; Tomic-Canic, Marjana

2018-05-01

The clinical field of wound healing is challenged by numerous hurdles. Not only are wound-healing disorders complex and multifactorial, but the corresponding patient population is diverse, often elderly and burdened by multiple comorbidities such as diabetes and cardiovascular disease. The care of such patients requires a dedicated, multidisciplinary team of physicians, surgeons, nurses and scientists. In spite of the critical clinical need, it has been over 15 years since a treatment received approval for efficacy by the FDA in the United States. Among the reasons contributing to this lack of effective new treatment modalities is poor understanding of mechanisms that inhibit healing in patients. Additionally, preclinical models do not fully reflect the disease complexity of the human condition, which brings us to a paradox: if we are to use a "mechanistic" approach that favours animal models, we can dissect specific mechanisms using advanced genetic, molecular and cellular technologies, with the caveat that it may not be directly applicable to patients. Traditionally, scientific review panels, for either grant funding or manuscript publication purposes, favour such "mechanistic" approaches whereby human tissue analyses, deemed "descriptive" science, are characterized as a "fishing expedition" and are considered "fatally flawed." However, more emerging evidence supports the notion that the use of human samples provides significant new knowledge regarding the molecular and cellular mechanisms that control wound healing and contribute to inhibition of the process in patients. Here, we discuss the advances, benefits and challenges of translational research in wound healing focusing on human subject research. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

Repurposing of tetracyclines to overcome resistance pathways associated with photochemotherapy in cancer (Conference Presentation)

NASA Astrophysics Data System (ADS)

Liu, Joyce; Huang, Huang-Chiao; Rizvi, Imran; Hasan, Tayyaba

2016-03-01

Given the consistently poor prognoses for some of the most difficult-to-treat cancers, rapidly translatable treatment regimens that offer improvements in outcomes are much needed. The repurposing of FDA approved non-cancer drugs presents an opportunity to design clinically feasible, novel combinations of therapies with a mechanistic rationale, to overcome resistance and survival pathways that render many current treatments ineffective. Tetracyclines are a class of antibiotics that demonstrate potential for such repurposing, as they have also been shown by others to affect a wide range of targets in cancer. Notably, the unique structure of tetracyclines allows them to act through both light activated and non-light mediated mechanisms. While light activation of tetracyclines can result in singlet oxygen production, their non-light mediated targets include inhibition of DNA repair enzymes and modulation of hypoxia-inducible markers, among others. With these mechanisms in mind, we seek to elucidate the benefit of including tetracyclines as part of an already promising, mechanistically cooperative photochemotherapy combination for ovarian cancer. In ovarian cancer, the dismal rates of recurrence and survival associated with the aggressive disease further emphasize the need to mechanistically reinforce treatments regimens. Thus, the results will highlight insights into the cooperative effect of repurposed tetracyclines on treatment response and molecular markers, both in vitro and in a challenging mouse model of disseminated ovarian cancer.

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

PubMed

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

2012-01-01

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.

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

Elucidation of arctigenin pharmacokinetics after intravenous and oral administrations in rats: integration of in vitro and in vivo findings via semi-mechanistic pharmacokinetic modeling.

PubMed

Gao, Qiong; Zhang, Yufeng; Wo, Siukwan; Zuo, Zhong

2014-11-01

Although arctigenin (AR) has attracted substantial research interests due to its promising and diverse therapeutic effects, studies regarding its biotransformation were limited. The current study aims to provide information regarding the pharmacokinetic properties of AR via various in vitro and in vivo experiments as well as semi-mechanistic pharmacokinetic modeling. Our in vitro rat microsome incubation studies revealed that glucuronidation was the main intestinal and liver metabolic pathway of AR, which occurred with V max, K m, and Clint of 47.5 ± 3.4 nmol/min/mg, 204 ± 22 μM, and 233 ± 9 μl/min/mg with intestinal microsomes and 2.92 ± 0.07 nmol/min/mg, 22.7 ± 1.2 μM, and 129 ± 4 μl/min/mg with liver microsomes, respectively. In addition, demethylation and hydrolysis of AR occurred with liver microsomes but not with intestinal microsomes. In vitro incubation of AR and its metabolites in intestinal content demonstrated that glucuronides of AR excreted in bile could be further hydrolyzed back to the parent compound, suggesting its potential enterohepatic circulation. Furthermore, rapid formation followed by fast elimination of arctigenic acid (AA) and arctigenin-4'-O-glucuronide (AG) was observed after both intravenous (IV) and oral administrations of AR in rats. Linear pharmacokinetics was observed at three different doses for AR, AA, and AG after IV administration of AR (0.48-2.4 mg/kg, r (2) > 0.99). Finally, an integrated semi-mechanistic pharmacokinetic model using in vitro enzyme kinetic and in vivo pharmacokinetic parameters was successfully developed to describe plasma concentrations of AR, AA, and AG after both IV and oral administration of AR at all tested doses.

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.

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...

The land-use legacy effect: Towards a mechanistic understanding of time-lagged water quality responses to land use/cover.

PubMed

Martin, Sherry L; Hayes, Daniel B; Kendall, Anthony D; Hyndman, David W

2017-02-01

Numerous studies have linked land use/land cover (LULC) to aquatic ecosystem responses, however only a few have included the dynamics of changing LULC in their analysis. In this study, we explicitly recognize changing LULC by linking mechanistic groundwater flow and travel time models to a historical time series of LULC, creating a land-use legacy map. We then illustrate the utility of legacy maps to explore relationships between dynamic LULC and lake water chemistry. We tested two main concepts about mechanisms linking LULC and lake water chemistry: groundwater pathways are an important mechanism driving legacy effects; and, LULC over multiple spatial scales is more closely related to lake chemistry than LULC over a single spatial scale. We applied statistical models to twelve water chemistry variables, ranging from nutrients to relatively conservative ions, to better understand the roles of biogeochemical reactivity and solubility on connections between LULC and aquatic ecosystem response. Our study illustrates how different areas can have long groundwater pathways that represent different LULC than what can be seen on the landscape today. These groundwater pathways delay the arrival of nutrients and other water quality constituents, thus creating a legacy of historic land uses that eventually reaches surface water. We find that: 1) several water chemistry variables are best fit by legacy LULC while others have a stronger link to current LULC, and 2) single spatial scales of LULC analysis performed worse for most variables. Our novel combination of temporal and spatial scales was the best overall model fit for most variables, including SRP where this model explained 54% of the variation. We show that it is important to explicitly account for temporal and spatial context when linking LULC to ecosystem response. Copyright © 2016. Published by Elsevier B.V.

Mechanistic mathematical modelling of mercaptopurine effects on cell cycle of human acute lymphoblastic leukaemia cells

PubMed Central

Panetta, J C; Evans, W E; Cheok, M H

2006-01-01

The antimetabolite mercaptopurine (MP) is widely used to treat childhood acute lymphoblastic leukaemia (ALL). To study the dynamics of MP on the cell cycle, we incubated human T-cell leukaemia cell lines (Molt-4 sensitive and resistant subline and P12 resistant) with 10 μM MP and measured total cell count, cell cycle distribution, percent viable, percent apoptotic, and percent dead cells serially over 72 h. We developed a mathematical model of the cell cycle dynamics after treatment with MP and used it to show that the Molt-4 sensitive controls had a significantly higher rate of cells entering apoptosis (2.7-fold, P<0.00001) relative to the resistant cell lines. Additionally, when treated with MP, the sensitive cell line showed a significant increase in the rate at which cells enter apoptosis compared to its controls (2.4-fold, P<0.00001). Of note, the resistant cell lines had a higher rate of antimetabolite incorporation into the DNA of viable cells (>1.4-fold, P<0.01). Lastly, in contrast to the other cell lines, the Molt-4 resistant subline continued to cycle, though at a rate slower relative to its control, rather than proceed to apoptosis. This led to a larger S-phase block in the Molt-4 resistant cell line, but not a higher rate of cell death. Gene expression of apoptosis, cell cycle, and repair genes were consistent with mechanistic dynamics described by the model. In summary, the mathematical model provides a quantitative assessment to compare the cell cycle effects of MP in cells with varying degrees of MP resistance. PMID:16333308

Mathematical modeling of atopic dermatitis reveals "double-switch" mechanisms underlying 4 common disease phenotypes.

PubMed

Domínguez-Hüttinger, Elisa; Christodoulides, Panayiotis; Miyauchi, Kosuke; Irvine, Alan D; Okada-Hatakeyama, Mariko; Kubo, Masato; Tanaka, Reiko J

2017-06-01

The skin barrier acts as the first line of defense against constant exposure to biological, microbial, physical, and chemical environmental stressors. Dynamic interplay between defects in the skin barrier, dysfunctional immune responses, and environmental stressors are major factors in the development of atopic dermatitis (AD). A systems biology modeling approach can yield significant insights into these complex and dynamic processes through integration of prior biological data. We sought to develop a multiscale mathematical model of AD pathogenesis that describes the dynamic interplay between the skin barrier, environmental stress, and immune dysregulation and use it to achieve a coherent mechanistic understanding of the onset, progression, and prevention of AD. We mathematically investigated synergistic effects of known genetic and environmental risk factors on the dynamic onset and progression of the AD phenotype, from a mostly asymptomatic mild phenotype to a severe treatment-resistant form. Our model analysis identified a "double switch," with 2 concatenated bistable switches, as a key network motif that dictates AD pathogenesis: the first switch is responsible for the reversible onset of inflammation, and the second switch is triggered by long-lasting or frequent activation of the first switch, causing irreversible onset of systemic T H 2 sensitization and worsening of AD symptoms. Our mathematical analysis of the bistable switch predicts that genetic risk factors decrease the threshold of environmental stressors to trigger systemic T H 2 sensitization. This analysis predicts and explains 4 common clinical AD phenotypes from a mild and reversible phenotype through to severe and recalcitrant disease and provides a mechanistic explanation for clinically demonstrated preventive effects of emollient treatments against development of AD. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Profiling of the fetal and adult rat liver transcriptome and translatome reveals discordant regulation by the mechanistic target of rapamycin (mTOR).

PubMed

Boylan, Joan M; Sanders, Jennifer A; Neretti, Nicola; Gruppuso, Philip A

2015-07-01

The mechanistic target of rapamycin (mTOR) integrates growth factor signaling, nutrient abundance, cell growth, and proliferation. On the basis of our interest in somatic growth in the late gestation fetus, we characterized the role of mTOR in the regulation of hepatic gene expression and translation initiation in fetal and adult rats. Our strategy was to manipulate mTOR signaling in vivo and then characterize the transcriptome and translating mRNA in liver tissue. In adult rats, we used the nonproliferative growth model of refeeding after a period of fasting and the proliferative model of liver regeneration following partial hepatectomy. We also studied livers from preterm fetal rats (embryonic day 19) in which fetal hepatocytes are asynchronously proliferating. All three models employed rapamycin to inhibit mTOR signaling. Analysis of the transcriptome in fasted-refed animals showed rapamycin-mediated induction of genes associated with oxidative phosphorylation. Genes associated with RNA processing were downregulated. In liver regeneration, rapamycin induced genes associated with lysosomal metabolism, steroid metabolism, and the acute phase response. In fetal animals, rapamycin inhibited expression of genes in several functional categories that were unrelated to effects in the adult animals. Translation control showed marked fetal-adult differences. In both adult models, rapamycin inhibited the translation of genes with complex 5' untranslated regions, including those encoding ribosomal proteins. Fetal translation was resistant to the effects of rapamycin. We conclude that the mTOR pathway in liver serves distinct physiological roles in the adult and fetus, with the latter representing a condition of rapamycin resistance. Copyright © 2015 the American Physiological Society.

Sex Differences in Animal Models: Focus on Addiction

PubMed Central

Becker, Jill B.

2016-01-01

The purpose of this review is to discuss ways to think about and study sex differences in preclinical animal models. We use the framework of addiction, in which animal models have excellent face and construct validity, to illustrate the importance of considering sex differences. There are four types of sex differences: qualitative, quantitative, population, and mechanistic. A better understanding of the ways males and females can differ will help scientists design experiments to characterize better the presence or absence of sex differences in new phenomena that they are investigating. We have outlined major quantitative, population, and mechanistic sex differences in the addiction domain using a heuristic framework of the three established stages of the addiction cycle: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. Female rats, in general, acquire the self-administration of drugs and alcohol more rapidly, escalate their drug taking with extended access more rapidly, show more motivational withdrawal, and (where tested in animal models of “craving”) show greater reinstatement. The one exception is that female rats show less motivational withdrawal to alcohol. The bases for these quantitative sex differences appear to be both organizational, in that estradiol-treated neonatal animals show the male phenotype, and activational, in that the female phenotype depends on the effects of gonadal hormones. In animals, differences within the estrous cycle can be observed but are relatively minor. Such hormonal effects seem to be most prevalent during the acquisition of drug taking and less influential once compulsive drug taking is established and are linked largely to progesterone and estradiol. This review emphasizes not only significant differences in the phenotypes of females and males in the domain of addiction but emphasizes the paucity of data to date in our understanding of those differences. PMID:26772794

Passive and iontophoretic transport through the skin polar pathway.

PubMed

Li, S K; Peck, K D

2013-01-01

The purpose of the present article is to briefly recount the contributions of Prof. William I. Higuchi to the area of skin transport. These contributions include developing fundamental knowledge of the barrier properties of the stratum corneum, mechanisms of skin transport, concentration gradient across skin in topical drug applications that target the viable epidermal layer, and permeation enhancement by chemical and electrical means. The complex and changeable nature of the skin barrier makes it difficult to assess and characterize the critical parameters that influence skin permeation. The systematic and mechanistic approaches taken by Dr. Higuchi in studying these parameters provided fundamental knowledge in this area and had a measured and lasting influence upon this field of study. This article specifically reviews the validation and characterization of the polar permeation pathway, the mechanistic model of skin transport, the influence of the dermis on the target skin concentration concept, and iontophoretic transport across the polar pathway of skin including the effects of electroosmosis and electropermeabilization. © 2013 S. Karger AG, Basel.

Planarians as models of cadmium-induced neoplasia provide measurable benchmarks for mechanistic studies.

PubMed

Voura, Evelyn B; Montalvo, Melissa J; Dela Roca, Kevin T; Fisher, Julia M; Defamie, Virginie; Narala, Swami R; Khokha, Rama; Mulligan, Margaret E; Evans, Colleen A

2017-08-01

Bioassays of planarian neoplasia highlight the potential of these organisms as useful standards to assess whether environmental toxins such as cadmium promote tumorigenesis. These studies complement other investigations into the exceptional healing and regeneration of planarians - processes that are driven by a population of active stem cells, or neoblasts, which are likely transformed during planarian tumor growth. Our goal was to determine if planarian tumorigenesis assays are amenable to mechanistic studies of cadmium carcinogenesis. To that end we demonstrate, by examining both counts of cell populations by size, and instances of mitosis, that the activity of the stem cell population can be monitored. We also provide evidence that specific biomodulators can affect the potential of planarian neoplastic growth, in that an inhibitor of metalloproteinases effectively blocked the development of the lesions. From these results, we infer that neoblast activity does respond to cadmium-induced tumor growth, and that metalloproteinases are required for the progression of cancer in the planarian. Copyright © 2017 Elsevier Inc. All rights reserved.

Fracture Mechanisms For SiC Fibers And SiC/SiC Composites Under Stress-Rupture Conditions at High Temperatures

NASA Technical Reports Server (NTRS)

DiCarlo, James A.; Yun, Hee Mann; Hurst, Janet B.; Viterna, L. (Technical Monitor)

2002-01-01

The successful application of SiC/SiC ceramic matrix composites as high-temperature structural materials depends strongly on maximizing the fracture or rupture life of the load-bearing fiber and matrix constituents. Using high-temperature data measured under stress-rupture test conditions, this study examines in a mechanistic manner the effects of various intrinsic and extrinsic factors on the creep and fracture behavior of a variety of SiC fiber types. It is shown that although some fiber types fracture during a large primary creep stage, the fiber creep rate just prior to fracture plays a key role in determining fiber rupture time (Monkman-Grant theory). If it is assumed that SiC matrices rupture in a similar manner as fibers with the same microstructures, one can develop simple mechanistic models to analyze and optimize the stress-rupture behavior of SiC/SiC composites for applied stresses that are initially below matrix cracking.

A theoretical study on the mechanistic highlights behind the Brønsted-acid dependent mer-fac isomerization of homoleptic carbenic iridium complexes for PhOLEDs.

PubMed

Setzer, Tobias; Lennartz, Christian; Dreuw, Andreas

2017-06-06

Recently, a successful Brønsted-acid mediated geometric isomerization of the meridional homoleptic carbenic iridium(iii) complexes tris-(N-phenyl,N-methyl-benzimidazol-2-yl)iridium(iii) (1) and tris-(N-phenyl,N-benzyl-benzimidazol-2-yl)iridium(iii) (2) into their facial form has been reported. In the present work the pronounced acid-dependency of this particular isomerization procedure is revisited and additional mechanistic pathways are taken into account. Moreover, the acid-induced material decomposition is addressed. All calculations are carried out using density functional theory (DFT) while the environmental effects in solution are accounted for by the COSMO-RS model. The simulated results clearly reveal the outstanding importance of the complex interplay between acid strength, coordinating power of the corresponding base and the steric influence of the ligand system in contrast to the plain calculation of minimum energy pathways for selected complexes. Eventually, general rules to enhance the material-specific reaction yields are provided.

Modeling greenhouse gas emissions from dairy farms.

PubMed

Rotz, C Alan

2017-11-15

Dairy farms have been identified as an important source of greenhouse gas emissions. Within the farm, important emissions include enteric CH 4 from the animals, CH 4 and N 2 O from manure in housing facilities during long-term storage and during field application, and N 2 O from nitrification and denitrification processes in the soil used to produce feed crops and pasture. Models using a wide range in level of detail have been developed to represent or predict these emissions. They include constant emission factors, variable process-related emission factors, empirical or statistical models, mechanistic process simulations, and life cycle assessment. To fully represent farm emissions, models representing the various emission sources must be integrated to capture the combined effects and interactions of all important components. Farm models have been developed using relationships across the full scale of detail, from constant emission factors to detailed mechanistic simulations. Simpler models, based upon emission factors and empirical relationships, tend to provide better tools for decision support, whereas more complex farm simulations provide better tools for research and education. To look beyond the farm boundaries, life cycle assessment provides an environmental accounting tool for quantifying and evaluating emissions over the full cycle, from producing the resources used on the farm through processing, distribution, consumption, and waste handling of the milk and dairy products produced. Models are useful for improving our understanding of farm processes and their interacting effects on greenhouse gas emissions. Through better understanding, they assist in the development and evaluation of mitigation strategies for reducing emissions and improving overall sustainability of dairy farms. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Triptolide abrogates growth of colon cancer and induces cell cycle arrest by inhibiting transcriptional activation of E2F.

PubMed

Oliveira, Amanda; Beyer, Georg; Chugh, Rohit; Skube, Steven J; Majumder, Kaustav; Banerjee, Sulagna; Sangwan, Veena; Li, Lihua; Dawra, Rajinder; Subramanian, Subbaya; Saluja, Ashok; Dudeja, Vikas

2015-06-01

Despite significant progress in diagnostics and therapeutics, over 50 thousand patients die from colorectal cancer annually. Hence, there is urgent need for new lines of treatment. Triptolide, a natural compound isolated from the Chinese herb Tripterygium wilfordii, is effective against multiple cancers. We have synthesized a water soluble analog of triptolide, named Minnelide, which is currently in phase I trial against pancreatic cancer. The aims of the current study were to evaluate whether triptolide/Minnelide is effective against colorectal cancer and to elucidate the mechanism by which triptolide induces cell death in colorectal cancer. Efficacy of Minnelide was evaluated in subcutaneous xenograft and liver metastasis model of colorectal cancer. For mechanistic studies, colon cancer cell lines HCT116 and HT29 were treated with triptolide and the effect on viability, caspase activation, annexin positivity, lactate dehydrogenase release, and cell cycle progression was evaluated. Effect of triptolide on E2F transcriptional activity, mRNA levels of E2F-dependent genes, E2F1- retinoblastoma protein (Rb) binding, and proteins levels of regulator of G1-S transition was also measured. DNA binding of E2F1 was evaluated by chromatin immunoprecipitation assay. Triptolide decreased colon cancer cell viability in a dose- and time-dependent fashion. Minnelide markedly inhibited the growth of colon cancer in the xenograft and liver metastasis model of colon cancer and more than doubles the median survival of animals with liver metastases from colon cancer. Mechanistically, we demonstrate that at low concentrations triptolide induces apoptotic cell death but at higher concentrations it induces cell cycle arrest. Our data suggest that triptolide is able to induce G1 cell cycle arrest by inhibiting transcriptional activation of E2F1. Our data also show that triptolide downregulates E2F activity by potentially modulating events downstream of DNA binding. Therefore, we conclude that Triptolide and Minnelide are effective against colon cancer in multiple pre-clinical models.

A general model for stray dose calculation of static and intensity-modulated photon radiation.

PubMed

Hauri, Pascal; Hälg, Roger A; Besserer, Jürgen; Schneider, Uwe

2016-04-01

There is an increasing number of cancer survivors who are at risk of developing late effects caused by ionizing radiation such as induction of second tumors. Hence, the determination of out-of-field dose for a particular treatment plan in the patient's anatomy is of great importance. The purpose of this study was to analytically model the stray dose according to its three major components. For patient scatter, a mechanistic model was developed. For collimator scatter and head leakage, an empirical approach was used. The models utilize a nominal beam energy of 6 MeV to describe two linear accelerator types of a single vendor. The parameters of the models were adjusted using ionization chamber measurements registering total absorbed dose in simple geometries. Whole-body dose measurements using thermoluminescent dosimeters in an anthropomorphic phantom for static and intensity-modulated treatment plans were compared to the 3D out-of-field dose distributions calculated by a combined model. The absolute mean difference between the whole-body predicted and the measured out-of-field dose of four different plans was 11% with a maximum difference below 44%. Computation time of 36 000 dose points for one field was around 30 s. By combining the model-calculated stray dose with the treatment planning system dose, the whole-body dose distribution can be viewed in the treatment planning system. The results suggest that the model is accurate, fast and can be used for a wide range of treatment modalities to calculate the whole-body dose distribution for clinical analysis. For similar energy spectra, the mechanistic patient scatter model can be used independently of treatment machine or beam orientation.

Potassium titanyl phosphate laser tissue ablation: development and experimental validation of a new numerical model.

PubMed

Elkhalil, Hossam; Akkin, Taner; Pearce, John; Bischof, John

2012-10-01

The photoselective vaporization of prostate (PVP) green light (532 nm) laser is increasingly being used as an alternative to the transurethral resection of prostate (TURP) for treatment of benign prostatic hyperplasia (BPH) in older patients and those who are poor surgical candidates. In order to achieve the goals of increased tissue removal volume (i.e., "ablation" in the engineering sense) and reduced collateral thermal damage during the PVP green light treatment, a two dimensional computational model for laser tissue ablation based on available parameters in the literature has been developed and compared to experiments. The model is based on the control volume finite difference and the enthalpy method with a mechanistically defined energy necessary to ablate (i.e., physically remove) a volume of tissue (i.e., energy of ablation E(ab)). The model was able to capture the general trends experimentally observed in terms of ablation and coagulation areas, their ratio (therapeutic index (TI)), and the ablation rate (AR) (mm(3)/s). The model and experiment were in good agreement at a smaller working distance (WD) (distance from the tissue in mm) and a larger scanning speed (SS) (laser scan speed in mm/s). However, the model and experiment deviated somewhat with a larger WD and a smaller SS; this is most likely due to optical shielding and heat diffusion in the laser scanning direction, which are neglected in the model. This model is a useful first step in the mechanistic prediction of PVP based BPH laser tissue ablation. Future modeling efforts should focus on optical shielding, heat diffusion in the laser scanning direction (i.e., including 3D effects), convective heat losses at the tissue boundary, and the dynamic optical, thermal, and coagulation properties of BPH tissue.

Impacts of Climate Change on Biofuels Production

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

Melillo, Jerry M.

2014-04-30

The overall goal of this research project was to improve and use our biogeochemistry model, TEM, to simulate the effects of climate change and other environmental changes on the production of biofuel feedstocks. We used the improved version of TEM that is coupled with the economic model, EPPA, a part of MIT’s Earth System Model, to explore how alternative uses of land, including land for biofuels production, can help society meet proposed climate targets. During the course of this project, we have made refinements to TEM that include development of a more mechanistic plant module, with improved ecohydrology and considerationmore » of plant-water relations, and a more detailed treatment of soil nitrogen dynamics, especially processes that add or remove nitrogen from ecosystems. We have documented our changes to TEM and used the model to explore the effects on production in land ecosystems, including changes in biofuels production.« less

Chinese medicine and biomodulation in cancer patients—Part one

PubMed Central

Sagar, S.M.; Wong, R.K.

2008-01-01

Traditional Chinese Medicine (tcm) may be integrated with conventional Western medicine to enhance the care of patients with cancer. Although tcm is normally implemented as a whole system, recent reductionist research suggests mechanisms for the effects of acupuncture, herbs, and nutrition within the scientific model of biomedicine. The health model of Chinese medicine accommodates physical and pharmacologic interventions within the framework of a body–mind network. A Cartesian split does not occur within this model, but to allow for scientific exploration within the restrictions of positivism, reductionism, and controls for confounding factors, the components must necessarily be separated. Still, whole-systems research is important to evaluate effectiveness when applying the full model in clinical practice. Scientific analysis provides a mechanistic understanding of the processes that will improve the design of clinical studies and enhance safety. Enough preliminary evidence is available to encourage quality clinical trials to evaluate the efficacy of integrating tcm into Western cancer care. PMID:18317584

Utility of Small Animal Models of Developmental Programming.

PubMed

Reynolds, Clare M; Vickers, Mark H

2018-01-01

Any effective strategy to tackle the global obesity and rising noncommunicable disease epidemic requires an in-depth understanding of the mechanisms that underlie these conditions that manifest as a consequence of complex gene-environment interactions. In this context, it is now well established that alterations in the early life environment, including suboptimal nutrition, can result in an increased risk for a range of metabolic, cardiovascular, and behavioral disorders in later life, a process preferentially termed developmental programming. To date, most of the mechanistic knowledge around the processes underpinning development programming has been derived from preclinical research performed mostly, but not exclusively, in laboratory mouse and rat strains. This review will cover the utility of small animal models in developmental programming, the limitations of such models, and potential future directions that are required to fully maximize information derived from preclinical models in order to effectively translate to clinical use.

Incorporating evolutionary processes into population viability models.

PubMed

Pierson, Jennifer C; Beissinger, Steven R; Bragg, Jason G; Coates, David J; Oostermeijer, J Gerard B; Sunnucks, Paul; Schumaker, Nathan H; Trotter, Meredith V; Young, Andrew G

2015-06-01

We examined how ecological and evolutionary (eco-evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco-evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco-evo PVA using individual-based models with individual-level genotype tracking and dynamic genotype-phenotype mapping to model emergent population-level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco-evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence. © 2014 Society for Conservation Biology.

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...

SU-E-T-248: An Extended Generalized Equivalent Uniform Dose Accounting for Dose-Range Dependency of Radio-Biological Parameters.

PubMed

Troeller, A; Soehn, M; Yan, D

2012-06-01

Introducing an extended, phenomenological, generalized equivalent uniform dose (eEUD) that incorporates multiple volume-effect parameters for different dose-ranges. The generalized EUD (gEUD) was introduced as an estimate of the EUD that incorporates a single, tissue-specific parameter - the volume-effect-parameter (VEP) 'a'. As a purely phenomenological concept, its radio-biological equivalency to a given inhomogeneous dose distribution is not a priori clear and mechanistic models based on radio-biological parameters are assumed to better resemble the underlying biology. However, for normal organs mechanistic models are hard to derive, since the structural organization of the tissue plays a significant role. Consequently, phenomenological approaches might be especially useful in order to describe dose-response for normal tissues. However, the single parameter used to estimate the gEUD may not suffice in accurately representing more complex biological effects that have been discussed in the literature. For instance, radio-biological parameters and hence the effects of fractionation are known to be dose-range dependent. Therefore, we propose an extended phenomenological eEUD formula that incorporates multiple VEPs accounting for dose-range dependency. The eEUD introduced is a piecewise polynomial expansion of the gEUD formula. In general, it allows for an arbitrary number of VEPs, each valid for a certain dose-range. We proved that the formula fulfills required mathematical and physical criteria such as invertibility of the underlying dose-effect and continuity in dose. Furthermore, it contains the gEUD as a special case, if all VEPs are equal to 'a' from the gEUD model. The eEUD is a concept that expands the gEUD such that it can theoretically represent dose-range dependent effects. Its practicality, however, remains to be shown. As a next step, this will be done by estimating the eEUD from patient data using maximum-likelihood based NTCP modelling in the same way it is commonly done for the gEUD. © 2012 American Association of Physicists in Medicine.

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.

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

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.

Assessment of the impact of climate shifts on malaria transmission in the Sahel.

PubMed

Bomblies, Arne; Eltahir, Elfatih A B

2009-09-01

Climate affects malaria transmission through a complex network of causative pathways. We seek to evaluate the impact of hypothetical climate change scenarios on malaria transmission in the Sahel by using a novel mechanistic, high spatial- and temporal-resolution coupled hydrology and agent-based entomology model. The hydrology model component resolves individual precipitation events and individual breeding pools. The impact of future potential climate shifts on the representative Sahel village of Banizoumbou, Niger, is estimated by forcing the model of Banizoumbou environment with meteorological data from two locations along the north-south climatological gradient observed in the Sahel--both for warmer, drier scenarios from the north and cooler, wetter scenarios from the south. These shifts in climate represent hypothetical but historically realistic climate change scenarios. For Banizoumbou climatic conditions (latitude 13.54 N), a shift toward cooler, wetter conditions may dramatically increase mosquito abundance; however, our modeling results indicate that the increased malaria transmissibility is not simply proportional to the precipitation increase. The cooler, wetter conditions increase the length of the sporogonic cycle, dampening a large vectorial capacity increase otherwise brought about by increased mosquito survival and greater overall abundance. Furthermore, simulations varying rainfall event frequency demonstrate the importance of precipitation patterns, rather than simply average or time-integrated precipitation, as a controlling factor of these dynamics. Modeling results suggest that in addition to changes in temperature and total precipitation, changes in rainfall patterns are very important to predict changes in disease susceptibility resulting from climate shifts. The combined effect of these climate-shift-induced perturbations can be represented with the aid of a detailed mechanistic model.

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

Hsp70 in cancer: back to the future

PubMed Central

Sherman, Michael Y.; Gabai, Vladimir L.

2014-01-01

Mechanistic studies from cell culture and animal models have revealed critical roles for the heat shock protein Hsp70 in cancer initiation and progression. Surprisingly, many effects of Hsp70 on cancer have not been related to its chaperone activity, but rather to its role(s) in regulating cell signaling. A major factor that directs Hsp70 signaling activity appears to be the co-chaperone Bag3. Here, we review these recent breakthroughs, and how these discoveries drive drug development efforts. PMID:25347739

Systems Biology Approach Reveals a Calcium-Dependent Mechanism for Basal Toxicity in Daphnia magna.

PubMed

Antczak, Philipp; White, Thomas A; Giri, Anirudha; Michelangeli, Francesco; Viant, Mark R; Cronin, Mark T D; Vulpe, Chris; Falciani, Francesco

2015-09-15

The expanding diversity and ever increasing amounts of man-made chemicals discharged to the environment pose largely unknown hazards to ecosystem and human health. The concept of adverse outcome pathways (AOPs) emerged as a comprehensive framework for risk assessment. However, the limited mechanistic information available for most chemicals and a lack of biological pathway annotation in many species represent significant challenges to effective implementation of this approach. Here, a systems level, multistep modeling strategy demonstrates how to integrate information on chemical structure with mechanistic insight from genomic studies, and phenotypic effects to define a putative adverse outcome pathway. Results indicated that transcriptional changes indicative of intracellular calcium mobilization were significantly overrepresented in Daphnia magna (DM) exposed to sublethal doses of presumed narcotic chemicals with log Kow ≥ 1.8. Treatment of DM with a calcium ATPase pump inhibitor substantially recapitulated the common transcriptional changes. We hypothesize that calcium mobilization is a potential key molecular initiating event in DM basal (narcosis) toxicity. Heart beat rate analysis and metabolome analysis indicated sublethal effects consistent with perturbations of calcium preceding overt acute toxicity. Together, the results indicate that altered calcium homeostasis may be a key early event in basal toxicity or narcosis induced by lipophilic compounds.

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

Kinetic model of water disinfection using peracetic acid including synergistic effects.

PubMed

Flores, Marina J; Brandi, Rodolfo J; Cassano, Alberto E; Labas, Marisol D

2016-01-01

The disinfection efficiencies of a commercial mixture of peracetic acid against Escherichia coli were studied in laboratory scale experiments. The joint and separate action of two disinfectant agents, hydrogen peroxide and peracetic acid, were evaluated in order to observe synergistic effects. A kinetic model for each component of the mixture and for the commercial mixture was proposed. Through simple mathematical equations, the model describes different stages of attack by disinfectants during the inactivation process. Based on the experiments and the kinetic parameters obtained, it could be established that the efficiency of hydrogen peroxide was much lower than that of peracetic acid alone. However, the contribution of hydrogen peroxide was very important in the commercial mixture. It should be noted that this improvement occurred only after peracetic acid had initiated the attack on the cell. This synergistic effect was successfully explained by the proposed scheme and was verified by experimental results. Besides providing a clearer mechanistic understanding of water disinfection, such models may improve our ability to design reactors.

PSPs and ERPs: applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data.

PubMed

Laszlo, Sarah; Armstrong, Blair C

2014-05-01

The Parallel Distributed Processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials--the source of the ERP signal--into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one. Copyright © 2014 Elsevier Inc. All rights reserved.

Identification of the mechanism of action of a glucokinase activator from oral glucose tolerance test data in type 2 diabetic patients based on an integrated glucose-insulin model.

PubMed

Jauslin, Petra M; Karlsson, Mats O; Frey, Nicolas

2012-12-01

A mechanistic drug-disease model was developed on the basis of a previously published integrated glucose-insulin model by Jauslin et al. A glucokinase activator was used as a test compound to evaluate the model's ability to identify a drug's mechanism of action and estimate its effects on glucose and insulin profiles following oral glucose tolerance tests. A kinetic-pharmacodynamic approach was chosen to describe the drug's pharmacodynamic effects in a dose-response-time model. Four possible mechanisms of action of antidiabetic drugs were evaluated, and the corresponding affected model parameters were identified: insulin secretion, glucose production, insulin effect on glucose elimination, and insulin-independent glucose elimination. Inclusion of drug effects in the model at these sites of action was first tested one-by-one and then in combination. The results demonstrate the ability of this model to identify the dual mechanism of action of a glucokinase activator and describe and predict its effects: Estimating a stimulating drug effect on insulin secretion and an inhibiting effect on glucose output resulted in a significantly better model fit than any other combination of effect sites. The model may be used for dose finding in early clinical drug development and for gaining more insight into a drug candidate's mechanism of action.

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.

Perspectives from the symposium: The role of nutrition in infant and toddler brain and behavioral development.

PubMed

Rosales, Francisco J; Zeisel, Steven H

2008-06-01

This symposium examined current trends in neuroscience and developmental psychology as they apply to assessing the effects of nutrients on brain and behavioral development of 0-6-year-olds. Although the spectrum of nutrients with brain effects has not changed much in the last 25 years, there has been an explosion in new knowledge about the genetics, structure and function of the brain. This has helped to link the brain mechanistic pathway by which these nutrients act with cognitive functions. A clear example of this is linking of brain structural changes due to hypoglycemia versus hyperglycemia with cognitive functions by using magnetic resonance imaging (MRI) to assess changes in brain-region volumes in combination with cognitive test of intelligence, memory and processing speed. Another example is the use of event-related potential (ERP) studies to show that infants of diabetic mothers have impairments in memory from birth through 8 months of age that are consistent with alterations in mechanistic pathways of memory observed in animal models of perinatal iron deficiency. However, gaps remain in the understanding of how nutrients and neurotrophic factors interact with each other in optimizing brain development and function.

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.

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.

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.

A mechanistic modelling approach to polymer dissolution using magnetic resonance microimaging.

PubMed

Kaunisto, Erik; Abrahmsen-Alami, Susanna; Borgquist, Per; Larsson, Anette; Nilsson, Bernt; Axelsson, Anders

2010-10-15

In this paper a computationally efficient mathematical model describing the swelling and dissolution of a polyethylene oxide tablet is presented. The model was calibrated against polymer release, front position and water concentration profile data inside the gel layer, using two different diffusion models. The water concentration profiles were obtained from magnetic resonance microimaging data which, in addition to the previously used texture analysis method, can help to validate and discriminate between the mechanisms of swelling, diffusion and erosion in relation to the dissolution process. Critical parameters were identified through a comprehensive sensitivity analysis, and the effect of hydrodynamic shearing was investigated by using two different stirring rates. Good agreement was obtained between the experimental results and the model. Copyright © 2010 Elsevier B.V. All rights reserved.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.

PubMed

Allen, R J; Rieger, T R; Musante, C J

2016-03-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.

Fuel thermal conductivity (FTHCON). Status report. [PWR; BWR

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

Hagrman, D. L.

1979-02-01

An improvement of the fuel thermal conductivity subcode is described which is part of the fuel rod behavior modeling task performed at EG and G Idaho, Inc. The original version was published in the Materials Properties (MATPRO) Handbook, Section A-2 (Fuel Thermal Conductivity). The improved version incorporates data which were not included in the previous work and omits some previously used data which are believed to come from cracked specimens. The models for the effect of porosity on thermal conductivity and for the electronic contribution to thermal coductivity have been completely revised in order to place these models on amore » more mechanistic basis. As a result of modeling improvements the standard error of the model with respect to its data base has been significantly reduced.« less

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...

Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics.

PubMed

Xu, Chonggang; Fisher, Rosie; Wullschleger, Stan D; Wilson, Cathy J; Cai, Michael; McDowell, Nate G

2012-01-01

Nitrogen is a dominant regulator of vegetation dynamics, net primary production, and terrestrial carbon cycles; however, most ecosystem models use a rather simplistic relationship between leaf nitrogen content and photosynthetic capacity. Such an approach does not consider how patterns of nitrogen allocation may change with differences in light intensity, growing-season temperature and CO(2) concentration. To account for this known variability in nitrogen-photosynthesis relationships, we develop a mechanistic nitrogen allocation model based on a trade-off of nitrogen allocated between growth and storage, and an optimization of nitrogen allocated among light capture, electron transport, carboxylation, and respiration. The developed model is able to predict the acclimation of photosynthetic capacity to changes in CO(2) concentration, temperature, and radiation when evaluated against published data of V(c,max) (maximum carboxylation rate) and J(max) (maximum electron transport rate). A sensitivity analysis of the model for herbaceous plants, deciduous and evergreen trees implies that elevated CO(2) concentrations lead to lower allocation of nitrogen to carboxylation but higher allocation to storage. Higher growing-season temperatures cause lower allocation of nitrogen to carboxylation, due to higher nitrogen requirements for light capture pigments and for storage. Lower levels of radiation have a much stronger effect on allocation of nitrogen to carboxylation for herbaceous plants than for trees, resulting from higher nitrogen requirements for light capture for herbaceous plants. As far as we know, this is the first model of complete nitrogen allocation that simultaneously considers nitrogen allocation to light capture, electron transport, carboxylation, respiration and storage, and the responses of each to altered environmental conditions. We expect this model could potentially improve our confidence in simulations of carbon-nitrogen interactions and the vegetation feedbacks to climate in Earth system models.

Toward a Mechanistic Modeling of Nitrogen Limitation on Vegetation Dynamics

PubMed Central

Xu, Chonggang; Fisher, Rosie; Wullschleger, Stan D.; Wilson, Cathy J.; Cai, Michael; McDowell, Nate G.

2012-01-01

Nitrogen is a dominant regulator of vegetation dynamics, net primary production, and terrestrial carbon cycles; however, most ecosystem models use a rather simplistic relationship between leaf nitrogen content and photosynthetic capacity. Such an approach does not consider how patterns of nitrogen allocation may change with differences in light intensity, growing-season temperature and CO2 concentration. To account for this known variability in nitrogen-photosynthesis relationships, we develop a mechanistic nitrogen allocation model based on a trade-off of nitrogen allocated between growth and storage, and an optimization of nitrogen allocated among light capture, electron transport, carboxylation, and respiration. The developed model is able to predict the acclimation of photosynthetic capacity to changes in CO2 concentration, temperature, and radiation when evaluated against published data of Vc,max (maximum carboxylation rate) and Jmax (maximum electron transport rate). A sensitivity analysis of the model for herbaceous plants, deciduous and evergreen trees implies that elevated CO2 concentrations lead to lower allocation of nitrogen to carboxylation but higher allocation to storage. Higher growing-season temperatures cause lower allocation of nitrogen to carboxylation, due to higher nitrogen requirements for light capture pigments and for storage. Lower levels of radiation have a much stronger effect on allocation of nitrogen to carboxylation for herbaceous plants than for trees, resulting from higher nitrogen requirements for light capture for herbaceous plants. As far as we know, this is the first model of complete nitrogen allocation that simultaneously considers nitrogen allocation to light capture, electron transport, carboxylation, respiration and storage, and the responses of each to altered environmental conditions. We expect this model could potentially improve our confidence in simulations of carbon-nitrogen interactions and the vegetation feedbacks to climate in Earth system models. PMID:22649564

Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions

PubMed Central

Momeni, Babak; Xie, Li; Shou, Wenying

2017-01-01

Pairwise models are commonly used to describe many-species communities. In these models, an individual receives additive fitness effects from pairwise interactions with each species in the community ('additivity assumption'). All pairwise interactions are typically represented by a single equation where parameters reflect signs and strengths of fitness effects ('universality assumption'). Here, we show that a single equation fails to qualitatively capture diverse pairwise microbial interactions. We build mechanistic reference models for two microbial species engaging in commonly-found chemical-mediated interactions, and attempt to derive pairwise models. Different equations are appropriate depending on whether a mediator is consumable or reusable, whether an interaction is mediated by one or more mediators, and sometimes even on quantitative details of the community (e.g. relative fitness of the two species, initial conditions). Our results, combined with potential violation of the additivity assumption in many-species communities, suggest that pairwise modeling will often fail to predict microbial dynamics. DOI: http://dx.doi.org/10.7554/eLife.25051.001 PMID:28350295

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.

Warming reduces metabolic rate in marine snails: adaptation to fluctuating high temperatures challenges the metabolic theory of ecology.

PubMed

Marshall, David J; McQuaid, Christopher D

2011-01-22

The universal temperature-dependence model (UTD) of the metabolic theory of ecology (MTE) proposes that temperature controls mass-scaled, whole-animal resting metabolic rate according to the first principles of physics (Boltzmann kinetics). Controversy surrounds the model's implication of a mechanistic basis for metabolism that excludes the effects of adaptive regulation, and it is unclear how this would apply to organisms that live in fringe environments and typically show considerable metabolic adaptation. We explored thermal scaling of metabolism in a rocky-shore eulittoral-fringe snail (Echinolittorina malaccana) that experiences constrained energy gain and fluctuating high temperatures (between 25°C and approximately 50°C) during prolonged emersion (weeks). In contrast to the prediction of the UTD model, metabolic rate was often negatively related to temperature over a benign range (30-40°C), the relationship depending on (i) the temperature range, (ii) the degree of metabolic depression (related to the quiescent period), and (iii) whether snails were isolated within their shells. Apparent activation energies (E) varied between 0.05 and -0.43 eV, deviating excessively from the UTD's predicted range of between 0.6 and 0.7 eV. The lowering of metabolism when heated should improve energy conservation in a high-temperature environment and challenges both the theory's generality and its mechanistic basis.

Inflammation-Dependent IL18 Signaling Restricts Hepatocellular Carcinoma Growth by Enhancing the Accumulation and Activity of Tumor-Infiltrating Lymphocytes.

PubMed

Markowitz, Geoffrey J; Yang, Pengyuan; Fu, Jing; Michelotti, Gregory A; Chen, Rui; Sui, Jianhua; Yang, Bin; Qin, Wen-Hao; Zhang, Zheng; Wang, Fu-Sheng; Diehl, Anna Mae; Li, Qi-Jing; Wang, Hongyang; Wang, Xiao-Fan

2016-04-15

Chronic inflammation in liver tissue is an underlying cause of hepatocellular carcinoma. High levels of inflammatory cytokine IL18 in the circulation of patients with hepatocellular carcinoma correlates with poor prognosis. However, conflicting results have been reported for IL18 in hepatocellular carcinoma development and progression. In this study, we used tissue specimens from hepatocellular carcinoma patients and clinically relevant mouse models of hepatocellular carcinoma to evaluate IL18 expression and function. In a mouse model of liver fibrosis that recapitulates a tumor-promoting microenvironment, global deletion of the IL18 receptor IL18R1 enhanced tumor growth and burden. Similarly, in a carcinogen-induced model of liver tumorigenesis, IL18R1 deletion increased tumor burden. Mechanistically, we found that IL18 exerted inflammation-dependent tumor-suppressive effects largely by promoting the differentiation, activity, and survival of tumor-infiltrating T cells. Finally, differences in the expression of IL18 in tumor tissue versus nontumor tissue were more predictive of patient outcome than overall tissue expression. Taken together, our findings resolve a long-standing contradiction regarding a tumor-suppressive role for IL18 in established hepatocellular carcinoma and provide a mechanistic explanation for the complex relationship between its expression pattern and hepatocellular carcinoma prognosis. Cancer Res; 76(8); 2394-405. ©2016 AACR. ©2016 American Association for Cancer Research.

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.