15,941 research outputs found
Modular Random Boolean Networks
Random Boolean networks (RBNs) have been a popular model of genetic
regulatory networks for more than four decades. However, most RBN studies have
been made with random topologies, while real regulatory networks have been
found to be modular. In this work, we extend classical RBNs to define modular
RBNs. Statistical experiments and analytical results show that modularity has a
strong effect on the properties of RBNs. In particular, modular RBNs have more
attractors and are closer to criticality when chaotic dynamics would be
expected, compared to classical RBNs.Comment: 33 pages, 14 figures, 11 tables. Corrected version, added experiments
with large networks confirming results. Accepted in Artificial Lif
Searching for network modules
When analyzing complex networks a key target is to uncover their modular
structure, which means searching for a family of modules, namely node subsets
spanning each a subnetwork more densely connected than the average. This work
proposes a novel type of objective function for graph clustering, in the form
of a multilinear polynomial whose coefficients are determined by network
topology. It may be thought of as a potential function, to be maximized, taking
its values on fuzzy clusterings or families of fuzzy subsets of nodes over
which every node distributes a unit membership. When suitably parametrized,
this potential is shown to attain its maximum when every node concentrates its
all unit membership on some module. The output thus is a partition, while the
original discrete optimization problem is turned into a continuous version
allowing to conceive alternative search strategies. The instance of the problem
being a pseudo-Boolean function assigning real-valued cluster scores to node
subsets, modularity maximization is employed to exemplify a so-called quadratic
form, in that the scores of singletons and pairs also fully determine the
scores of larger clusters, while the resulting multilinear polynomial potential
function has degree 2. After considering further quadratic instances, different
from modularity and obtained by interpreting network topology in alternative
manners, a greedy local-search strategy for the continuous framework is
analytically compared with an existing greedy agglomerative procedure for the
discrete case. Overlapping is finally discussed in terms of multiple runs, i.e.
several local searches with different initializations.Comment: 10 page
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Complex Networks
Introduction to the Special Issue on Complex Networks, Artificial Life
journal.Comment: 7 pages, in pres
Lattices with non-Shannon Inequalities
We study the existence or absence of non-Shannon inequalities for variables
that are related by functional dependencies. Although the power-set on four
variables is the smallest Boolean lattice with non-Shannon inequalities there
exist lattices with many more variables without non-Shannon inequalities. We
search for conditions that ensures that no non-Shannon inequalities exist. It
is demonstrated that 3-dimensional distributive lattices cannot have
non-Shannon inequalities and planar modular lattices cannot have non-Shannon
inequalities. The existence of non-Shannon inequalities is related to the
question of whether a lattice is isomorphic to a lattice of subgroups of a
group.Comment: Ten pages. Submitted to ISIT 2015. The appendix will not appear in
the proceeding
Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks
Random Boolean networks (RBNs) are frequently employed for modelling complex
systems driven by information processing, e.g. for gene regulatory networks
(GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system
consisting of distinct adaptive RBNs - subnetworks - connected by a set of
permanent interlinks. Information measures and internal subnetworks topology of
HARBN coevolve and reach steady-states that are specific for a given network
structure. We investigate mean node information, mean edge information as well
as a mean node degree as functions of model parameters and demonstrate HARBN's
ability to describe complex hierarchical systems.Comment: 9 pages, 6 figure
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