5,141 research outputs found
Control of complex networks requires both structure and dynamics
The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure
Emergence of robustness against noise: A structural phase transition in evolved models of gene regulatory networks
We investigate the evolution of Boolean networks subject to a selective
pressure which favors robustness against noise, as a model of evolved genetic
regulatory systems. By mapping the evolutionary process into a statistical
ensemble and minimizing its associated free energy, we find the structural
properties which emerge as the selective pressure is increased and identify a
phase transition from a random topology to a "segregated core" structure, where
a smaller and more densely connected subset of the nodes is responsible for
most of the regulation in the network. This segregated structure is very
similar qualitatively to what is found in gene regulatory networks, where only
a much smaller subset of genes --- those responsible for transcription factors
--- is responsible for global regulation. We obtain the full phase diagram of
the evolutionary process as a function of selective pressure and the average
number of inputs per node. We compare the theoretical predictions with Monte
Carlo simulations of evolved networks and with empirical data for Saccharomyces
cerevisiae and Escherichia coli.Comment: 12 pages, 10 figure
The Number of Different Binary Functions Generated by NK-Kauffman Networks and the Emergence of Genetic Robustness
We determine the average number , of \textit{NK}-Kauffman
networks that give rise to the same binary function. We show that, for , there exists a connectivity critical value such that () for and
for . We find that is not a
constant, but scales very slowly with , as . The problem of genetic robustness emerges as a statistical property
of the ensemble of \textit{NK}-Kauffman networks and impose tight constraints
in the average number of epistatic interactions that the genotype-phenotype map
can have.Comment: 4 figures 18 page
Reliability of genetic networks is evolvable
Control of the living cell functions with remarkable reliability despite the
stochastic nature of the underlying molecular networks -- a property presumably
optimized by biological evolution. We here ask to what extent the property of a
stochastic dynamical network to produce reliable dynamics is an evolvable
trait. Using an evolutionary algorithm based on a deterministic selection
criterion for the reliability of dynamical attractors, we evolve dynamical
networks of noisy discrete threshold nodes. We find that, starting from any
random network, reliability of the attractor landscape can often be achieved
with only few small changes to the network structure. Further, the evolvability
of networks towards reliable dynamics while retaining their function is
investigated and a high success rate is found.Comment: 5 pages, 3 figure
The Number of Different Binary Functions Generated by NK-Kauffman Networks and the Emergence of Genetic Robustness
We determine the average number , of \textit{NK}-Kauffman
networks that give rise to the same binary function. We show that, for , there exists a connectivity critical value such that () for and
for . We find that is not a
constant, but scales very slowly with , as . The problem of genetic robustness emerges as a statistical property
of the ensemble of \textit{NK}-Kauffman networks and impose tight constraints
in the average number of epistatic interactions that the genotype-phenotype map
can have.Comment: 4 figures 18 page
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