24,286 research outputs found
Transcriptional delay stabilizes bistable gene networks
Transcriptional delay can significantly impact the dynamics of gene networks.
Here we examine how such delay affects bistable systems. We investigate several
stochastic models of bistable gene networks and find that increasing delay
dramatically increases the mean residence times near stable states. To explain
this, we introduce a non-Markovian, analytically tractable reduced model. The
model shows that stabilization is the consequence of an increased number of
failed transitions between stable states. Each of the bistable systems that we
simulate behaves in this manner
Failed "nonaccelerating" models of prokaryote gene regulatory networks
Much current network analysis is predicated on the assumption that important
biological networks will either possess scale free or exponential statistics
which are independent of network size allowing unconstrained network growth
over time. In this paper, we demonstrate that such network growth models are
unable to explain recent comparative genomics results on the growth of
prokaryote regulatory gene networks as a function of gene number. This failure
largely results as prokaryote regulatory gene networks are "accelerating" and
have total link numbers growing faster than linearly with network size and so
can exhibit transitions from stationary to nonstationary statistics and from
random to scale-free to regular statistics at particular critical network
sizes. In the limit, these networks can undergo transitions so marked as to
constrain network sizes to be below some critical value. This is of interest as
the regulatory gene networks of single celled prokaryotes are indeed
characterized by an accelerating quadratic growth with gene count and are size
constrained to be less than about 10,000 genes encoded in DNA sequence of less
than about 10 megabases. We develop two "nonaccelerating" network models of
prokaryote regulatory gene networks in an endeavor to match observation and
demonstrate that these approaches fail to reproduce observed statistics.Comment: Corrected error in biological input parameter: 13 pages, 9 figure
Designer Gene Networks: Towards Fundamental Cellular Control
The engineered control of cellular function through the design of synthetic
genetic networks is becoming plausible. Here we show how a naturally occurring
network can be used as a parts list for artificial network design, and how
model formulation leads to computational and analytical approaches relevant to
nonlinear dynamics and statistical physics.Comment: 35 pages, 8 figure
Stochastic models and numerical algorithms for a class of regulatory gene networks
Regulatory gene networks contain generic modules like those involving
feedback loops, which are essential for the regulation of many biological
functions. We consider a class of self-regulated genes which are the building
blocks of many regulatory gene networks, and study the steady state
distributions of the associated Gillespie algorithm by providing efficient
numerical algorithms. We also study a regulatory gene network of interest in
synthetic biology and in gene therapy, using mean-field models with time
delays. Convergence of the related time-nonhomogeneous Markov chain is
established for a class of linear catalytic networks with feedback loop
A Fast Reconstruction Algorithm for Gene Networks
This paper deals with gene networks whose dynamics is assumed to be generated
by a continuous-time, linear, time invariant, finite dimensional system (LTI)
at steady state. In particular, we deal with the problem of network
reconstruction in the typical practical situation in which the number of
available data is largely insufficient to uniquely determine the network. In
order to try to remove this ambiguity, we will exploit the biologically a
priori assumption of network sparseness, and propose a new algorithm for
network reconstruction having a very low computational complexity (linear in
the number of genes) so to be able to deal also with very large networks (say,
thousands of genes). Its performances are also tested both on artificial data
(generated with linear models) and on real data obtained by Gardner et al. from
the SOS pathway in Escherichia coli.Comment: 12 pages, 3 figure
Spectral analysis of gene expression profiles using gene networks
Microarrays have become extremely useful for analysing genetic phenomena, but
establishing a relation between microarray analysis results (typically a list
of genes) and their biological significance is often difficult. Currently, the
standard approach is to map a posteriori the results onto gene networks to
elucidate the functions perturbed at the level of pathways. However,
integrating a priori knowledge of the gene networks could help in the
statistical analysis of gene expression data and in their biological
interpretation. Here we propose a method to integrate a priori the knowledge of
a gene network in the analysis of gene expression data. The approach is based
on the spectral decomposition of gene expression profiles with respect to the
eigenfunctions of the graph, resulting in an attenuation of the high-frequency
components of the expression profiles with respect to the topology of the
graph. We show how to derive unsupervised and supervised classification
algorithms of expression profiles, resulting in classifiers with biological
relevance. We applied the method to the analysis of a set of expression
profiles from irradiated and non-irradiated yeast strains. It performed at
least as well as the usual classification but provides much more biologically
relevant results and allows a direct biological interpretation
Inference algorithms for gene networks: a statistical mechanics analysis
The inference of gene regulatory networks from high throughput gene
expression data is one of the major challenges in systems biology. This paper
aims at analysing and comparing two different algorithmic approaches. The first
approach uses pairwise correlations between regulated and regulating genes; the
second one uses message-passing techniques for inferring activating and
inhibiting regulatory interactions. The performance of these two algorithms can
be analysed theoretically on well-defined test sets, using tools from the
statistical physics of disordered systems like the replica method. We find that
the second algorithm outperforms the first one since it takes into account
collective effects of multiple regulators
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