Combining micro and macro-modeling in DEVS for computational biology

Uhrmacher,; Ewald,; John, John; Maus,; Jeschke, Sabina; Biermann,

doi:10.1109/wsc.2007.4419683

Cited by 32 publications

(31 citation statements)

References 25 publications

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“…The features of ml-DEVS, such as multiple levels of behavior, up-and downward causation between those levels, variable structures, hierarchical composition, and modular model design, make the formalism suitable for modeling agents, their environment, and the direct and indirect interaction between them. More details on the formalism and its execution semantics can be found in [12,11].…”

Section: Simulation Modelmentioning

confidence: 99%

Evaluating the robustness of activity recognition using computational causal behavior models

Steiniger

Bader

Kirste

2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

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Activity recognition is a challenging research problem in ubiquitous computing domain and has to tackle omnipresent uncertainties, e.g., resulting from ambiguous or intermittent sensor readings. In this paper, we introduce an activity recognition approach based on causal modeling and probabilistic plan recognition. To evaluate the performance of our approach systematically, we generated sensor data with different error rates using a simulation. This data served as input for the activity recognition in a series of experiments. In these experiments we stepwise introduced and combined additional sources of uncertainty, i.e., different duration models and ignoring certain sensors, to demonstrate the robustness of our approach. Our evaluation shows that Computational Causal Behavior Models provide a basis for a robust activity recognition system.

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Section: Simulation Modelmentioning

confidence: 99%

Evaluating the robustness of activity recognition using computational causal behavior models

Steiniger

Bader

Kirste

2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

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“…concentration-based, and individual-based approaches leads us to multi-level modeling and simulation. In (Uhrmacher, Ewald, John, Maus, Jeschke, and Biermann 2007) we introduced a specific formalism which relates individual and population level. The downward causation reflects the impact that the population level has on the individuals, e.g.…”

Section: Introductionmentioning

confidence: 99%

Multi-resolution spatial simulation for molecular crowding

Jeschke

Uhrmacher

2008

2008 Winter Simulation Conference

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Spatial phenomena attract increasingly interest in computational biology. Molecular crowding, i.e. a dense population of macromolecules, is known to have a significant impact on the kinetics of molecules. However, an in-detail inspection of cell behavior in time and space is extremely costly. To balance between cost and accuracy, multi-resolution approaches offer one solution. Particularly, a combination of individual and lattice-population based algorithms promise an adequate treatment of phenomena like macromolecular crowding. In realizing such an approach, central questions are how to specify and synchronize the interaction between population and individual spatial level, and to decide what is best treated at a specific level, respectively. Based on an algorithm which combines the Next Subvolume Method and a simple, individual-based spatial approach, we will present possible answers to these questions, and will discuss first experimental results.

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“…Most biological systems are complex and broken into definable sub-units. The DEVS formalism would allow these sub-units to be represented as individual atomic models, and with the hierarchal nature of DEVS would allow these atomic models to be coupled in order to model the entire system [21]. These atomic models can be re-used in other systems, when applicable, with relative ease.…”

Section: Cfd and Cell-devs Modeling Of Biological Systems And The Medmentioning

confidence: 99%

Computational Fluid Dynamic Solver Based on Cellular Discrete-Event Simulation for use in Biological Systems

Schyndel¹

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The use of computer simulations in biology and the medical research field has gained in popularity. These simulations are providing researchers the opportunity to better predict the behavior of biological systems before performing long and expensive physical trials. The modeling of large biological systems would benefit from a method of approximating fluid flow quickly and accurately. Currently, no analytical solution exists; instead, many different numerical methods attempt to provide accurate approximations. They are referred to as Computational Fluid Dynamic solvers (CFD). The Discrete Event System Specification (DEVS) has rarely been used for modeling the physics of fluid flow. In this thesis we show how Cell-DEVS, a derivative of the DEVS formalism that conforms to the Cellular Automata parameters, can be used to provide realistic approximations of fluid flow. The algorithms used in the CFD presented in this thesis are based on the Navier-Stokes equations for non-linear fluid flow, which are an extension to Newton's Second Law of motion. The goal of the Cell-DEVS based CFD model will be to accurately approximate the fluid flow with minimal computational effort. Furthermore, the design of the solver should be such that it can be easily adjusted for use in a wide range of biological systems.iii

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Combining micro and macro-modeling in DEVS for computational biology

Cited by 32 publications

References 25 publications

Evaluating the robustness of activity recognition using computational causal behavior models

Evaluating the robustness of activity recognition using computational causal behavior models

Multi-resolution spatial simulation for molecular crowding

Computational Fluid Dynamic Solver Based on Cellular Discrete-Event Simulation for use in Biological Systems

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