37,994 research outputs found
StochKit-FF: Efficient Systems Biology on Multicore Architectures
The stochastic modelling of biological systems is an informative, and in some
cases, very adequate technique, which may however result in being more
expensive than other modelling approaches, such as differential equations. We
present StochKit-FF, a parallel version of StochKit, a reference toolkit for
stochastic simulations. StochKit-FF is based on the FastFlow programming
toolkit for multicores and exploits the novel concept of selective memory. We
experiment StochKit-FF on a model of HIV infection dynamics, with the aim of
extracting information from efficiently run experiments, here in terms of
average and variance and, on a longer term, of more structured data.Comment: 14 pages + cover pag
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
Simulation modelling and visualisation: toolkits for building artificial worlds
Simulations users at all levels make heavy use of compute resources to drive computational
simulations for greatly varying applications areas of research using different simulation
paradigms. Simulations are implemented in many software forms, ranging from highly standardised
and general models that run in proprietary software packages to ad hoc hand-crafted
simulations codes for very specific applications. Visualisation of the workings or results of a
simulation is another highly valuable capability for simulation developers and practitioners.
There are many different software libraries and methods available for creating a visualisation
layer for simulations, and it is often a difficult and time-consuming process to assemble a
toolkit of these libraries and other resources that best suits a particular simulation model. We
present here a break-down of the main simulation paradigms, and discuss differing toolkits and
approaches that different researchers have taken to tackle coupled simulation and visualisation
in each paradigm
Simulating city growth by using the cellular automata algorithm
The objective of this thesis is to develop and implement a Cellular Automata
(CA) algorithm to simulate urban growth process. It attempts to satisfy the
need to predict the future shape of a city, the way land uses sprawl in the
surroundings of that city and its population. Salonica city in Greece is
selected as a case study to simulate its urban growth. Cellular automaton
(CA) based models are increasingly used to investigate cities and urban
systems. Sprawling cities may be considered as complex adaptive systems,
and this warrants use of methodology that can accommodate the space-time
dynamics of many interacting entities. Automata tools are well-suited for
representation of such systems. By means of illustrating this point, the
development of a model for simulating the sprawl of land uses such as
commercial and residential and calculating the population who will reside in
the city is discussed
Knowing one's place: a free-energy approach to pattern regulation.
Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization-of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs-and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model-predicting the signals sensed by cells in the target morphology-and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies-that currently focus on molecular pathways-with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics
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