19,940 research outputs found
Canalization in the Critical States of Highly Connected Networks of Competing Boolean Nodes
Canalization is a classic concept in Developmental Biology that is thought to
be an important feature of evolving systems. In a Boolean network it is a form
of network robustness in which a subset of the input signals control the
behavior of a node regardless of the remaining input. It has been shown that
Boolean networks can become canalized if they evolve through a frustrated
competition between nodes. This was demonstrated for large networks in which
each node had K=3 inputs. Those networks evolve to a critical steady-state at
the boarder of two phases of dynamical behavior. Moreover, the evolution of
these networks was shown to be associated with the symmetry of the evolutionary
dynamics. We extend these results to the more highly connected K>3 cases and
show that similar canalized critical steady states emerge with the same
associated dynamical symmetry, but only if the evolutionary dynamics is biased
toward homogeneous Boolean functions.Comment: 8 pages, 5 figure
Evolving Gene Regulatory Networks with Mobile DNA Mechanisms
This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1303.722
Canalizing Kauffman networks: non-ergodicity and its effect on their critical behavior
Boolean Networks have been used to study numerous phenomena, including gene
regulation, neural networks, social interactions, and biological evolution.
Here, we propose a general method for determining the critical behavior of
Boolean systems built from arbitrary ensembles of Boolean functions. In
particular, we solve the critical condition for systems of units operating
according to canalizing functions and present strong numerical evidence that
our approach correctly predicts the phase transition from order to chaos in
such systems.Comment: to be published in PR
The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks
We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the
interplay between evolvability and robustness of model gene regulatory networks
with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006)
we find that networks with SFin topologies, that is SF topology for incoming
nodes and ER topology for outgoing nodes, are significantly more evolvable
towards specific oscillatory targets than networks with ER topology for both
incoming and outgoing nodes. Similar results are found for networks with SFboth
and SFout topologies. The functionality of the SFout topology, which most
closely resembles the structure of biological gene networks (Babu et al.,
2004), is compared to the ER topology in further detail through an extension to
multiple target outputs, with either an oscillatory or a non-oscillatory
nature. For multiple oscillatory targets of the same length, the differences
between SFout and ER networks are enhanced, but for non-oscillatory targets
both types of networks show fairly similar evolvability. We find that SF
networks generate oscillations much more easily than ER networks do, and this
may explain why SF networks are more evolvable than ER networks are for
oscillatory phenotypes. In spite of their greater evolvability, we find that
networks with SFout topologies are also more robust to mutations than ER
networks. Furthermore, the SFout topologies are more robust to changes in
initial conditions (environmental robustness). For both topologies, we find
that once a population of networks has reached the target state, further
neutral evolution can lead to an increase in both the mutational robustness and
the environmental robustness to changes in initial conditions.Comment: 16 pages, 15 figure
Topological Evolution of Dynamical Networks: Global Criticality from Local Dynamics
We evolve network topology of an asymmetrically connected threshold network
by a simple local rewiring rule: quiet nodes grow links, active nodes lose
links. This leads to convergence of the average connectivity of the network
towards the critical value in the limit of large system size . How
this principle could generate self-organization in natural complex systems is
discussed for two examples: neural networks and regulatory networks in the
genome.Comment: 4 pages RevTeX, 4 figures PostScript, revised versio
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