3,342 research outputs found
General Iteration graphs and Boolean automata circuits
This article is set in the field of regulation networks modeled by discrete
dynamical systems. It focuses on Boolean automata networks. In such networks,
there are many ways to update the states of every element. When this is done
deterministically, at each time step of a discretised time flow and according
to a predefined order, we say that the network is updated according to
block-sequential update schedule (blocks of elements are updated sequentially
while, within each block, the elements are updated synchronously). Many
studies, for the sake of simplicity and with some biologically motivated
reasons, have concentrated on networks updated with one particular
block-sequential update schedule (more often the synchronous/parallel update
schedule or the sequential update schedules). The aim of this paper is to give
an argument formally proven and inspired by biological considerations in favour
of the fact that the choice of a particular update schedule does not matter so
much in terms of the possible and likely dynamical behaviours that networks may
display
Boolean networks with robust and reliable trajectories
We construct and investigate Boolean networks that follow a given reliable
trajectory in state space, which is insensitive to fluctuations in the updating
schedule, and which is also robust against noise. Robustness is quantified as
the probability that the dynamics return to the reliable trajectory after a
perturbation of the state of a single node. In order to achieve high
robustness, we navigate through the space of possible update functions by using
an evolutionary algorithm. We constrain the networks to having the minimum
number of connections required to obtain the reliable trajectory. Surprisingly,
we find that robustness always reaches values close to 100 percent during the
evolutionary optimization process. The set of update functions can be evolved
such that it differs only slightly from that of networks that were not
optimized with respect to robustness. The state space of the optimized networks
is dominated by the basin of attraction of the reliable trajectory.Comment: 12 pages, 9 figure
On the effects of firing memory in the dynamics of conjunctive networks
Boolean networks are one of the most studied discrete models in the context
of the study of gene expression. In order to define the dynamics associated to
a Boolean network, there are several \emph{update schemes} that range from
parallel or \emph{synchronous} to \emph{asynchronous.} However, studying each
possible dynamics defined by different update schemes might not be efficient.
In this context, considering some type of temporal delay in the dynamics of
Boolean networks emerges as an alternative approach. In this paper, we focus in
studying the effect of a particular type of delay called \emph{firing memory}
in the dynamics of Boolean networks. Particularly, we focus in symmetric
(non-directed) conjunctive networks and we show that there exist examples that
exhibit attractors of non-polynomial period. In addition, we study the
prediction problem consisting in determinate if some vertex will eventually
change its state, given an initial condition. We prove that this problem is
{\bf PSPACE}-complete
Boolean networks synchronism sensitivity and XOR circulant networks convergence time
In this paper are presented first results of a theoretical study on the role
of non-monotone interactions in Boolean automata networks. We propose to
analyse the contribution of non-monotony to the diversity and complexity in
their dynamical behaviours according to two axes. The first one consists in
supporting the idea that non-monotony has a peculiar influence on the
sensitivity to synchronism of such networks. It leads us to the second axis
that presents preliminary results and builds an understanding of the dynamical
behaviours, in particular concerning convergence times, of specific
non-monotone Boolean automata networks called XOR circulant networks.Comment: In Proceedings AUTOMATA&JAC 2012, arXiv:1208.249
Block-sequential update schedules and Boolean automata circuits
International audienceOur work is set in the framework of complex dynamical systems and, more precisely, that of Boolean automata networks modeling regulation networks. We study how the choice of an update schedule impacts on the dynamics of such a network. To do this, we explain how studying the dynamics of any network updated with an arbitrary block-sequential update schedule can be reduced to the study of the dynamics of a different network updated in parallel. We give special attention to networks whose underlying structure is a circuit, that is, Boolean automata circuits. These particular and simple networks are known to serve as the "engines'' of the dynamics of arbitrary regulation networks containing them as sub-networks in that they are responsible for their variety of dynamical behaviours. We give both the number of attractors of period , and the total number of attractors in the dynamics of Boolean automata circuits updated with any block-sequential update schedule. We also detail the variety of dynamical behaviours that such networks may exhibit according to the update schedule
Turning block-sequential automata networks into smaller parallel networks with isomorphic limit dynamics
We state an algorithm that, given an automata network and a block-sequential
update schedule, produces an automata network of the same size or smaller with
the same limit dynamics under the parallel update schedule. Then, we focus on
the family of automata cycles which share a unique path of automata, called
tangential cycles, and show that a restriction of our algorithm allows to
reduce any instance of these networks under a block-sequential update schedule
into a smaller parallel network of the family and to characterize the number of
reductions operated while conserving their limit dynamics. We also show that
any tangential cycles reduced by our main algorithm are transformed into a
network whose size is that of the largest cycle of the initial network. We end
by showing that the restricted algorithm allows the direct characterization of
block-sequential double cycles as parallel ones.Comment: Accepted at CIE 202
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