277,716 research outputs found
Stochastic Calculus of Wrapped Compartments
The Calculus of Wrapped Compartments (CWC) is a variant of the Calculus of
Looping Sequences (CLS). While keeping the same expressiveness, CWC strongly
simplifies the development of automatic tools for the analysis of biological
systems. The main simplification consists in the removal of the sequencing
operator, thus lightening the formal treatment of the patterns to be matched in
a term (whose complexity in CLS is strongly affected by the variables matching
in the sequences).
We define a stochastic semantics for this new calculus. As an application we
model the interaction between macrophages and apoptotic neutrophils and a
mechanism of gene regulation in E.Coli
Modelling Cell Cycle using Different Levels of Representation
Understanding the behaviour of biological systems requires a complex setting
of in vitro and in vivo experiments, which attracts high costs in terms of time
and resources. The use of mathematical models allows researchers to perform
computerised simulations of biological systems, which are called in silico
experiments, to attain important insights and predictions about the system
behaviour with a considerably lower cost. Computer visualisation is an
important part of this approach, since it provides a realistic representation
of the system behaviour. We define a formal methodology to model biological
systems using different levels of representation: a purely formal
representation, which we call molecular level, models the biochemical dynamics
of the system; visualisation-oriented representations, which we call visual
levels, provide views of the biological system at a higher level of
organisation and are equipped with the necessary spatial information to
generate the appropriate visualisation. We choose Spatial CLS, a formal
language belonging to the class of Calculi of Looping Sequences, as the
formalism for modelling all representation levels. We illustrate our approach
using the budding yeast cell cycle as a case study
Modelling the Dynamics of an Aedes albopictus Population
We present a methodology for modelling population dynamics with formal means
of computer science. This allows unambiguous description of systems and
application of analysis tools such as simulators and model checkers. In
particular, the dynamics of a population of Aedes albopictus (a species of
mosquito) and its modelling with the Stochastic Calculus of Looping Sequences
(Stochastic CLS) are considered. The use of Stochastic CLS to model population
dynamics requires an extension which allows environmental events (such as
changes in the temperature and rainfalls) to be taken into account. A simulator
for the constructed model is developed via translation into the specification
language Maude, and used to compare the dynamics obtained from the model with
real data.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314
Field theory for a reaction-diffusion model of quasispecies dynamics
RNA viruses are known to replicate with extremely high mutation rates. These
rates are actually close to the so-called error threshold. This threshold is in
fact a critical point beyond which genetic information is lost through a
second-order phase transition, which has been dubbed the ``error catastrophe.''
Here we explore this phenomenon using a field theory approximation to the
spatially extended Swetina-Schuster quasispecies model [J. Swetina and P.
Schuster, Biophys. Chem. {\bf 16}, 329 (1982)], a single-sharp-peak landscape.
In analogy with standard absorbing-state phase transitions, we develop a
reaction-diffusion model whose discrete rules mimic the Swetina-Schuster model.
The field theory representation of the reaction-diffusion system is
constructed. The proposed field theory belongs to the same universality class
than a conserved reaction-diffusion model previously proposed [F. van Wijland
{\em et al.}, Physica A {\bf 251}, 179 (1998)]. From the field theory, we
obtain the full set of exponents that characterize the critical behavior at the
error threshold. Our results present the error catastrophe from a new point of
view and suggest that spatial degrees of freedom can modify several mean field
predictions previously considered, leading to the definition of characteristic
exponents that could be experimentally measurable.Comment: 13 page
A Type System for a Stochastic CLS
The Stochastic Calculus of Looping Sequences is suitable to describe the
evolution of microbiological systems, taking into account the speed of the
described activities. We propose a type system for this calculus that models
how the presence of positive and negative catalysers can modify these speeds.
We claim that types are the right abstraction in order to represent the
interaction between elements without specifying exactly the element positions.
Our claim is supported through an example modelling the lactose operon
Stochastic reaction & diffusion on growing domains: understanding the breakdown of robust pattern formation
Many biological patterns, from population densities to animal coat markings, can be thought of as heterogeneous spatiotemporal distributions of mobile agents. Many mathematical models have been proposed to account for the emergence of this complexity, but, in general, they have consisted of deterministic systems of differential equations, which do not take into account the stochastic nature of population interactions. One particular, pertinent criticism of these deterministic systems is that the exhibited patterns can often be highly sensitive to changes in initial conditions, domain geometry, parameter values, etc. Due to this sensitivity, we seek to understand the effects of stochasticity and growth on paradigm biological patterning models. In this paper, we extend spatial Fourier analysis and growing domain mapping techniques to encompass stochastic Turing systems. Through this we find that the stochastic systems are able to realize much richer dynamics than their deterministic counterparts, in that patterns are able to exist outside the standard Turing parameter range. Further, it is seen that the inherent stochasticity in the reactions appears to be more important than the noise generated by growth, when considering which wave modes are excited. Finally, although growth is able to generate robust pattern sequences in the deterministic case, we see that stochastic effects destroy this mechanism for conferring robustness. However, through Fourier analysis we are able to suggest a reason behind this lack of robustness and identify possible mechanisms by which to reclaim it
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