Multiparadigm modeling 2009

Multiparadigm modeling 2009

Developed the coupling environment for the multiparadigm modelling platform, conducted simulations and contributed to the manuscript: HK.

Led the research and provided guidance regarding biological phenomena at play and specific mechanisms pertinent to existing and future bioreactors: ZC. Led the research, provided guidance regarding the computational modelling aspects of the platform, reviewed and expanded the results obtained and contributed to the manuscript preparation: YV.

Performed the experiments: HK. Analyzed the data: HK YV. Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches.

In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa.

In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis.

In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth.

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The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications. The diseases of cellular deficiency [1] can be only treated if the lost cell population is either regenerated or compensated using autologous substitutes [2][3].

Given that certain adult human tissues lose their capacity to regenerate [4]they rely exclusively, in case of a critical injury, on functionally similar substitutes [4] — [7].

The principles of tissue engineering can be used to develop such biological substitutes, with remarkably similar properties as those of the host tissues, in vitro [4][6] — [9].

This requires recapitulation of certain key developmental events ex vivo thereby necessitating tight control over the artificial growth environment [3][7][10]. Bioreactors, which have evolved significantly in both their complexity and functionality over the last two decades, are devices that have been successfully utilized towards this end [2][3][10]. Apart from their primary design objective which is to regulate the cellular microenvironment to support cell viability, promote their 3D organization and provide the cells with spatiotemporally controlled signals they also offer the user the possibility to seed cells dynamically within 3D matrices, overcome the constraints inherent to static cultures and stimulate the developing constructs physically [3][10].

Despite the technological advances that have been made in the sector of regenerative medicine and bioreactor technology, there is still a pressing need for safe and clinically efficacious autologous substitutes [3]. Translating regenerative medicine from bench to bed-side would not only require a good product but also robust, controllable and cost-effective manufacturing bioprocesses that are compliant with the evolving regulatory frameworks [3][11].

Bioreactors serve ideally towards this end as they are the key element for the development of automated, standardized, traceable, cost-effective and safe manufacturing processes for engineered tissues for clinical applications [3]. However, utilized primarily as black boxes, where trial and error eventually leads to the desirable cellular outcome [3][12]bioreactors have an enormous ground to cover for that eventuality to be realized.

Currently, the yields are qualitatively poor and the process of cell growth is often not reproducible. The problem stems from the fact that little is known about the impact of specific bioreactor mass transport characteristics and features on the expansion and growth of cells within the device. Investigators in recent years have begun applying computational tools [12][13] to study mass transport inside the bioreactor and how that may influence cell dynamics, but this extremely complex interplay has thus far proven elusive.

Analyses based on tackling directly the differential equations governing transport have not only been successful in quantifying mass transport and hydrodynamics inside the bioreactors; their use has been extended to, given certain assumptions, studying cellular dynamics as well [12][14]. Such models usually either assume absence of neo-tissue within the interconnected pore space in a scaffold or cell attachment only along the surfaces of the scaffold [12]. The differential approach models the cell population, the surrounding extra-cellular framework and nutrients as distributed continua [14].

The matrix in which the cells grow can be treated as a porous medium [14] and one can utilize a wide variety of available computational methods to quantify the distribution of any number of substances being transported and diffusing inside it. Whereas the continuum approach captures the transport phenomena quite accurately, the fact that it investigates biological phenomena at cell population level, disregarding entirely the cellular heterogeneity — central to biological function [14][15] — and the low-level system details [16]hinders detailed analysis of cellular dynamics [11][17][18].

In order to understand the impact of cell level behavior on the overall cell population discrete models can be employed [14] — [16][18]. The models that have been tried using this approach usually assume a constant supply of nutrients, which is not fully reflective of the actual conditions even under carefully designed experiments [15][20].

Furthermore, the discrete models available in the literature, despite capturing processes such as contact inhibition, persistent random walk and cell division with marked accuracy, do not consider the impact of chemotaxis and apoptosis on the overall growth dynamics of a cellular colony [15][20].

More recently, hybrid models, which are a combination of the continuum and discrete approaches, have been utilized to study the impact of transport phenomena on cellular dynamics [14][16][21][22].To develop complex systems and tackle their inherent complexity, executable modelling takes a prominent role in the development cycle.

But whereas good tool support exists for programming, tools for executable modelling have not yet reached the same level of functionality and maturity. In particular, live programming is seeing increasing support in programming tools, allowing users to dynamically change the source code of a running application.

This significantly reduces the edit—compile—debug cycle and grants the ability to gauge the effect of code changes instantly, aiding in debugging and code comprehension in general. In the modelling domain, however, live modelling only has limited support for a few formalisms. In this paper, we propose a Multi-Paradigm Modelling approach to add liveness to modelling languages in a generic way, which is reusable across multiple formalisms.

Live programming concepts and techniques are transposed to domain-specific executable modelling languages, clearly distinguishing between generic and language-specific concepts. To evaluate our approach, live modelling is implemented for three modelling languages, for which the implementation of liveness substantially differs. This is a preview of subscription content, log in to check access.

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TerraME: Multiparadigm Modeling Toolkit

Armstrong, J. ACM, New York Bousse, E. Brunet, G. Burckhardt, S. Burnett, M. Cellier, F. Springer, Secaucus Czaplicki, E. Edwards, J. Fabry, R. Favre, J. Goldberg, A. Addison-Wesley Longman, Boston Hancock, C. Hopcroft, J. Kelly, S. Wiley, New York Kuhn, A. Springer, Berlin Lieberman, H. Lindeman, R. Lucio, L. Mannadiar, R. In: Malloy, B. McDirmid, S. In: Proceedings of Onward! Mellor, S. Addison-Wesley, Reading Meyers, B.This release is strictly an updated version of the original November CTP release to support Visual Studio and.

It contains no other fixes outside of those required to work with the new RC. Due to breaking changes in Visual Studio RC, we recommend you uninstall and install in the following order. Uninstall Visual Studio and. Install Visual Studio and.

M Tools:. Thanks to everyone for being so patient while we worked through these issues. We expect to release another update when Visual Studio is officially released. However, Intellisense doesn't work. Has anyone else experienced this?

We're expecting to have a new CTP release shortly; we'll be posting information here as soon as it's ready. This site uses cookies for analytics, personalized content and ads. By continuing to browse this site, you agree to this use. Learn more. The content you requested has been removed. Ask a question. Quick access. Search related threads. Remove From My Forums. Asked by:. SQL Server Modeling. This forum has been retired and in now read-only. Sign in to vote. Thursday, March 4, PM. Thanks, Robert Towne Robert.

Wednesday, April 14, PM. Thursday, April 15, AM. Tuesday, April 20, PM. Help us improve MSDN. Make a suggestion.These global estimates are more than 15 times higher than the number of laboratory-confirmed deaths reported to the World Health Organization WHO.

WHO has acknowledged for some time that official, lab-confirmed reports are an underestimate of actual number of influenza deaths. Diagnostic specimens are not always collected from people who die with influenza; for others, influenza virus may not be detectable by the time of death. Because of these challenges, modeling is used to estimate the actual burden of disease. While studies looking at the burden of the H1N1 pandemic by country have been published previously, this is the first study to assess the global mortality impact of the pandemic.

The authors of the study had to overcome numerous challenges to arrive at these estimates, including lack of data specific to influenza in many countries, variability in terms of the level and timing of influenza virus circulation and differences across countries based on socio-economic factors listed above.

They did this by using influenza surveillance data from high- middle- and low-income countries, case fatality ratios reported from high income countries and the World Health Organization Burden of Disease data on lower respiratory tract infection mortality. The estimated number of deaths from this model was similar to previous mortality estimates during the first 12 months of H1N1 virus circulation in some countries, including the United States. According to the study, the largest death burden may have occurred in countries in the African and Southeast Asian regions, where it was estimated that more than half of all H1N1-related deaths occurred.

People living in these countries may be at higher risk for death from influenza complications due to differences in access to and quality of health care, nutritional status, the prevalence of underlying conditions, the age structure of the population and limited availability of influenza vaccines and antiviral medications.

Fatimah Dawood of CDC. To illustrate the impact of the shift in the age distribution of influenza deaths to younger age groups during the pandemic, researchers calculated the number of years of life lost due to H1N1-associated deaths. They estimated that 3 times as many years of life were lost during the first year of H1N1 virus circulation than would have occurred for the same number of deaths during a typical influenza season. Though the most recent influenza pandemic was hard on the young, the impact on the global population overall during the first year was less severe than that of previous pandemics.

Estimates of pandemic influenza mortality ranged from 0.

multiparadigm modeling 2009

It was estimated that 0. Because respiratory or cardiovascular influenza-related complications can lead to death, researchers estimated both respiratory and cardiovascular deaths to reach a total global estimate of mortality.

An estimatedrespiratory deaths occurred, while an additional 46, deaths were attributed to cardiovascular complications. Influenza-associated cardiovascular deaths were only estimated in persons 18 years of age and older since cardiovascular complications are relatively rare in children younger than 18 years. Source: Dawood, F. Estimated global mortality associated with the first 12 months of pandemic influenza A H1N1 virus circulation: a modelling study external icon.

Lancet Infect Dis. Skip directly to site content Skip directly to page options Skip directly to A-Z link. Influenza Flu. Section Navigation. Minus Related Pages. What CDC Does. To receive weekly email updates about Seasonal Flu, enter your email address: Email Address.

What's this? Links with this icon indicate that you are leaving the CDC website. Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.

You will be subject to the destination website's privacy policy when you follow the link. CDC is not responsible for Section compliance accessibility on other federal or private website. Cancel Continue.We expect that systematic and seamless computational upscaling and downscaling for modeling, predicting, or optimizing material and system properties and behavior with atomistic resolution will eventually be sufficiently accurate and practical that it will transform the mode of development in the materials, chemical, catalysis, and Pharma industries.

However, despite truly dramatic progress in methods, software, and hardware, this goal remains elusive, particularly for systems that exhibit inherently complex chemistry under normal or extreme conditions of temperature, pressure, radiation, and others.

We describe here some of the significant progress towards solving these problems via a general multiscale, multiparadigm strategy based on first-principles quantum mechanics QMand the development of breakthrough methods for treating reaction processes, excited electronic states, and weak bonding effects on the conformational dynamics of large-scale molecular systems.

A Multi-Paradigm Modeling Framework to Simulate Dynamic Reciprocity in a Bioreactor

These methods have resulted directly from filling in the physical and chemical gaps in existing theoretical and computational models, within the multiscale, multiparadigm strategy. To illustrate the procedure we demonstrate the application and transferability of such methods on an ample set of challenging problems that span multiple fields, system length- and timescales, and that lay beyond the realm of existing computational or, in some case, experimental approaches, including understanding the solvation effects on the reactivity of organic and organometallic structures, predicting transmembrane protein structures, understanding carbon nanotube nucleation and growth, understanding the effects of electronic excitations in materials subjected to extreme conditions of temperature and pressure, following the dynamics and energetics of long-term conformational evolution of DNA macromolecules, and predicting the long-term mechanisms involved in enhancing the mechanical response of polymer-based hydrogels.

The material on hydrogel mechanics for tissue engineering scaffolding is based upon work supported by the National Science Foundation CMMI Skip to main content. Advertisement Hide. Goddard III. Chapter First Online: 18 January This is a preview of subscription content, log in to check access. Schrodinger E Quantification of the eigen-value problem. Ann Phys 79 6 — Google Scholar.

multiparadigm modeling 2009

Messiah A ed Quantum mechanics, vol 1. J Chem Phys 65 10 — Google Scholar. Phys Rev Lett 24 4 Google Scholar. Phys Rev B 71 12 4 Google Scholar. Morokuma K et al Model studies of the structures, reactivities, and reaction mechanisms of metalloenzymes. Fisher DR et al An optimized initialization algorithm to ensure accuracy in quantum Monte Carlo calculations.

J Comput Chem 29 14 — Google Scholar. Comput Phys Commun 3 — Google Scholar. J Chem Phys 16 10 Google Scholar.Model-Based Design of complex software systems is an activity that requires the use of different modeling formalisms, with different perspectives of the system, to cover all relevant aspects of the system, to avoid over-design, to employ manageable models and to support system integration.

The comprehensive use of models in design has created a set of challenges beyond those of supporting one isolated design task. In particular, the need to combine, couple, and integrate models at different levels of abstraction and in different formalisms is posing a set of specific problems that must be tackled.

Multi-Paradigm Modeling is precisely the research field to focus on developing an appropriate set of concepts and tools to address the challenge of integrating models of different aspects of a software system specified using different formalisms and eventually at different levels of abstraction. Unable to display preview. Download preview PDF. Skip to main content. Advertisement Hide.

Recent Advances in Multi-paradigm Modeling. Conference paper. This is a preview of subscription content, log in to check access. Gheorghe, L. Rajhans, A. Schuster, A. Schiffelers, R. Groothuis, M.

Comparison of multi-paradigm programming languages

Jiang, J. Yie, A. Barroca, B. Meszaros, T. Asztalos, M.

multiparadigm modeling 2009

Budapest University of Technology and Economics. Personalised recommendations. Cite paper How to cite? ENW EndNote. Buy options.Figure above. Our Multiscale, Multiparadigm Simulation Strategy Motivation and General Overview Understanding natural phenomena from science or optimizing processes from engineering requires, by today's standard, synchronized contributions from theory, experiment and computation.

In an important number of cases, computer simulations -based on fundamental theory- supplement experiment, but in many others, they are the enabling tool for the study and comprehension of complex systems and phenomena that would otherwise be too expensive or dangerous, or even impossible, to study by direct experimentation.

Our research involves developing first principles-based theory, methods and efficient multiparadigm computational algorithms and tools capable of seamlessly bridging length and time scales to enable de novo design, characterization and prediction of material properties and processes and their application into solving currently "impossible" problems.

Contact: Andres Jaramillo-Botero [ ajaramil at wag. Our Multiscale, Multiparadigm Simulation Strategy. Motivation and General Overview Understanding natural phenomena from science or optimizing processes from engineering requires, by today's standard, synchronized contributions from theory, experiment and computation.

Further classical approximations on these systems include coarse-grain models based on rigid-bodies of constrained particles, suitable, for example, in sampling the dihedral conformational space of large-scale molecular systems, Monte Carlo techniques, averaging and homogenization techniques to explore larger and longer length- and time-scales, close to or within the mesoscale regime.

At the higher-end of the length- and time-scales, phenomenological-based continuum-level methods [including, Finite Elements] are the norm, yet these are incapable of capturing fundamental nanoscale properties and phenomena that are critical to understanding, elucidating and optimizing the behavior of matter at the macroscale.

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