A flexible architecture for modeling and simulation of diffusional association

Up to now, it is not possible to obtain analytical solutions for complex molecular association processes (e.g. Molecule recognition in Signaling or catalysis). Instead Brownian Dynamics (BD) simulations are commonly used to estimate the rate of diffusional association, e.g. to be later used in mesoscopic simulations. Meanwhile a portfolio of diffusional association (DA) methods have been developed that exploit BD. However, DA methods do not clearly distinguish between modeling, simulation, and experiment settings. This hampers to classify and compare the existing methods with respect to, for instance model assumptions, simulation approximations or specific optimization strategies for steering the computation of trajectories. To address this deficiency we propose FADA (Flexible Architecture for Diffusional Association) - an architecture that allows the flexible definition of the experiment comprising a formal description of the model in SpacePi, different simulators, as well as validation and analysis methods. Based on the NAM (Northrup-Allison-McCammon) method, which forms the basis of many existing DA methods, we illustrate the structure and functioning of FADA. A discussion of future validation experiments illuminates how the FADA can be exploited in order to estimate reaction rates and how validation techniques may be applied to validate additional features of the model

Rule-based multi-level modeling of cell biological systems

<p>Abstract</p> <p>Background</p> <p>Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.</p> <p>Results</p> <p>Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.</p> <p>Conclusions</p> <p>Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.</p

Hunger and market dynamics in pre-modern communities: insights into the effects of market intervention from a multi-agent model

'Nahrungsmittelknappheit und Hungerkrisen waren ein wichtiger Faktor des Lebensstandards in vormodernen städtischen Gemeinwesen. Die Räte der Stadt versuchten üblicherweise, die sich bei solchen Ereignissen ergebende Marktdynamik durch Intervention unter Kontrolle zu halten. Unklar ist allerdings, ob damit der starke Anstieg von Nahrungsmittelpreisen und Löhnen tatsächlich langfristig gedämpft werden konnte. Mit einem Multi-Agenten-Modell, in dem in vereinfachter Form die wesentlichen ökonomischen Austauschbeziehungen einer vormodernen Stadt abgebildet sind, werden die Wirkungen von Markteingriffen der städtischen Obrigkeit bei Hungersnöten auf die langfristige wirtschaftliche und demographische Entwicklung simuliert. Marktintervention zeigt sich dabei grundsätzlich als eine Strategie, mit der die wohlfahrtsmindernde Wirkung einer Nahrungsmittelknappheit sehr wohl beschränkt werden konnte. Um allerdings zu verhindern, dass sich aus einer Nahrungsmittelknappheit eine Hungerkrise entwickelte, mussten Marktinterventionen zielgerichtet und in mehrere Märkte gleichzeitig erfolgen, ebenso wie sich ihr wohlfahrtserhaltender Effekt erst nach einiger Zeit entfaltete.' (Autorenreferat)'Food shortages and hunger had been a great threat to the standard of living in urban communities in the Middle Ages and in early modern times. In order to cope with this sort of critical events, local governments and municipal councils commonly tried to control market dynamics, but it is not clear, whether in cases like this the typical market reaction of rising prices of foodstuffs and wages could really be moderated in the long-run through an intervention in markets. In the present article, a simplified multi-agent-based model of the pre-modern urban economy is used which allows a simulation of effects that different strategies of crisis management had on the medium-term and long-range economic and demographic developments in an urban community experiencing a food shortage. Intervention in markets turns out to be a strategic choice of local authorities by which very likely wealth-destroying consequences of food shortages or even famines could be reduced to some extent. A successful intervention preventing a temporary food shortage turning into a substantial nutritional crisis nonetheless had to be goal-directed and of complex design, and showed its full wealth-keeping effects only after a considerably long period of time.' (author's abstract

06161 Abstracts Collection -- Simulation and Verification of Dynamic Systems

From 17.04.06 to 22.04.06, the Dagstuhl Seminar 06161 ``Simulation and Verification of Dynamic Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Challenges for Modeling and Simulation Methods in Systems Biology

Systems Biology is aimed at analyzing the behavior and interrelationships of biological systems and is characterized by combining experimentation, theory, and computation. Dedicated to exploring current challenges, the panel brings together people from a variety of disciplines whose perspectives illuminate diverse facets of Systems Biology and the challenges for modeling and simulation methods

A FLEXIBLE AND SCALABLE EXPERIMENTATION LAYER

Modeling and simulation frameworks for use in different application domains, throughout the complete development process, and in different hardware environments need to be highly scalable. For achieving an efficient execution, different simulation algorithms and data structures must be provided to compute a concrete model on a concrete platform efficiently. The support of parallel simulation techniques becomes increasingly important in this context, which is due to the growing availability of multi-core processors and network-based computers. This leads to more complex simulation systems that are harder to configure correctly. We present an experimentation layer for the modeling and simulation framework JAMES II. It greatly facilitates the configuration and usage of the system for a user and supports distributed optimization, on-demand observation, and various distributed and non-distributed scenarios.