6,422 research outputs found
Chaotic Gene Regulatory Networks Can Be Robust Against Mutations and Noise
Robustness to mutations and noise has been shown to evolve through
stabilizing selection for optimal phenotypes in model gene regulatory networks.
The ability to evolve robust mutants is known to depend on the network
architecture. How do the dynamical properties and state-space structures of
networks with high and low robustness differ? Does selection operate on the
global dynamical behavior of the networks? What kind of state-space structures
are favored by selection? We provide damage propagation analysis and an
extensive statistical analysis of state spaces of these model networks to show
that the change in their dynamical properties due to stabilizing selection for
optimal phenotypes is minor. Most notably, the networks that are most robust to
both mutations and noise are highly chaotic. Certain properties of chaotic
networks, such as being able to produce large attractor basins, can be useful
for maintaining a stable gene-expression pattern. Our findings indicate that
conventional measures of stability, such as the damage-propagation rate, do not
provide much information about robustness to mutations or noise in model gene
regulatory networks.Comment: JTB accepte
Evolution of Canalizing Boolean Networks
Boolean networks with canalizing functions are used to model gene regulatory
networks. In order to learn how such networks may behave under evolutionary
forces, we simulate the evolution of a single Boolean network by means of an
adaptive walk, which allows us to explore the fitness landscape. Mutations
change the connections and the functions of the nodes. Our fitness criterion is
the robustness of the dynamical attractors against small perturbations. We find
that with this fitness criterion the global maximum is always reached and that
there is a huge neutral space of 100% fitness. Furthermore, in spite of having
such a high degree of robustness, the evolved networks still share many
features with "chaotic" networks.Comment: 8 pages, 10 figures; revised and extended versio
Boolean network model predicts cell cycle sequence of fission yeast
A Boolean network model of the cell-cycle regulatory network of fission yeast
(Schizosaccharomyces Pombe) is constructed solely on the basis of the known
biochemical interaction topology. Simulating the model in the computer,
faithfully reproduces the known sequence of regulatory activity patterns along
the cell cycle of the living cell. Contrary to existing differential equation
models, no parameters enter the model except the structure of the regulatory
circuitry. The dynamical properties of the model indicate that the biological
dynamical sequence is robustly implemented in the regulatory network, with the
biological stationary state G1 corresponding to the dominant attractor in state
space, and with the biological regulatory sequence being a strongly attractive
trajectory. Comparing the fission yeast cell-cycle model to a similar model of
the corresponding network in S. cerevisiae, a remarkable difference in
circuitry, as well as dynamics is observed. While the latter operates in a
strongly damped mode, driven by external excitation, the S. pombe network
represents an auto-excited system with external damping.Comment: 10 pages, 3 figure
Robustness of Transcriptional Regulation in Yeast-like Model Boolean Networks
We investigate the dynamical properties of the transcriptional regulation of
gene expression in the yeast Saccharomyces Cerevisiae within the framework of a
synchronously and deterministically updated Boolean network model. By means of
a dynamically determinant subnetwork, we explore the robustness of
transcriptional regulation as a function of the type of Boolean functions used
in the model that mimic the influence of regulating agents on the transcription
level of a gene. We compare the results obtained for the actual yeast network
with those from two different model networks, one with similar in-degree
distribution as the yeast and random otherwise, and another due to Balcan et
al., where the global topology of the yeast network is reproduced faithfully.
We, surprisingly, find that the first set of model networks better reproduce
the results found with the actual yeast network, even though the Balcan et al.
model networks are structurally more similar to that of yeast.Comment: 7 pages, 4 figures, To appear in Int. J. Bifurcation and Chaos, typos
were corrected and 2 references were adde
Shaping Robust System through Evolution
Biological functions are generated as a result of developmental dynamics that
form phenotypes governed by genotypes. The dynamical system for development is
shaped through genetic evolution following natural selection based on the
fitness of the phenotype. Here we study how this dynamical system is robust to
noise during development and to genetic change by mutation. We adopt a
simplified transcription regulation network model to govern gene expression,
which gives a fitness function. Through simulations of the network that
undergoes mutation and selection, we show that a certain level of noise in gene
expression is required for the network to acquire both types of robustness. The
results reveal how the noise that cells encounter during development shapes any
network's robustness, not only to noise but also to mutations. We also
establish a relationship between developmental and mutational robustness
through phenotypic variances caused by genetic variation and epigenetic noise.
A universal relationship between the two variances is derived, akin to the
fluctuation-dissipation relationship known in physics
The Influence of Canalization on the Robustness of Boolean Networks
Time- and state-discrete dynamical systems are frequently used to model
molecular networks. This paper provides a collection of mathematical and
computational tools for the study of robustness in Boolean network models. The
focus is on networks governed by -canalizing functions, a recently
introduced class of Boolean functions that contains the well-studied class of
nested canalizing functions. The activities and sensitivity of a function
quantify the impact of input changes on the function output. This paper
generalizes the latter concept to -sensitivity and provides formulas for the
activities and -sensitivity of general -canalizing functions as well as
canalizing functions with more precisely defined structure. A popular measure
for the robustness of a network, the Derrida value, can be expressed as a
weighted sum of the -sensitivities of the governing canalizing functions,
and can also be calculated for a stochastic extension of Boolean networks.
These findings provide a computationally efficient way to obtain Derrida values
of Boolean networks, deterministic or stochastic, that does not involve
simulation.Comment: 16 pages, 2 figures, 3 table
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