1,202 research outputs found
Non-equilibrium dynamics of gene expression and the Jarzynski equality
In order to express specific genes at the right time, the transcription of
genes is regulated by the presence and absence of transcription factor
molecules. With transcription factor concentrations undergoing constant
changes, gene transcription takes place out of equilibrium. In this paper we
discuss a simple mapping between dynamic models of gene expression and
stochastic systems driven out of equilibrium. Using this mapping, results of
nonequilibrium statistical mechanics such as the Jarzynski equality and the
fluctuation theorem are demonstrated for gene expression dynamics. Applications
of this approach include the determination of regulatory interactions between
genes from experimental gene expression data
Applying weighted network measures to microarray distance matrices
In recent work we presented a new approach to the analysis of weighted
networks, by providing a straightforward generalization of any network measure
defined on unweighted networks. This approach is based on the translation of a
weighted network into an ensemble of edges, and is particularly suited to the
analysis of fully connected weighted networks. Here we apply our method to
several such networks including distance matrices, and show that the clustering
coefficient, constructed by using the ensemble approach, provides meaningful
insights into the systems studied. In the particular case of two data sets from
microarray experiments the clustering coefficient identifies a number of
biologically significant genes, outperforming existing identification
approaches.Comment: Accepted for publication in J. Phys.
Full-scale fire tests of post-tensioned timber beams
This paper describes a series of full-scale furnace tests on loaded post tensioned LVL beams. Each beam was designed to exhibit a specific failure mechanism when exposed to the standard ISO834 fire. In addition to the beams a number of steel anchorage protection schemes were also investigated. These included wrapping the ends in kaowool, using intumescent paint, covering the anchorage with fire rated plasterboard and covering the anchorage with timber (LVL). The results of the full-scale tests cover temperature distributions through the timber members during the tests, the temperatures reached within the cavity and those of the tendons suspended within the cavity, the relaxation of the tendons during the test, the failure mechanisms experienced, and a summary of the anchorage protection details and their effectiveness. Recommendations for the design of both post-tensioned timber beams and associated anchorages are also provided
Regulatory networks and connected components of the neutral space
The functioning of a living cell is largely determined by the structure of
its regulatory network, comprising non-linear interactions between regulatory
genes. An important factor for the stability and evolvability of such
regulatory systems is neutrality - typically a large number of alternative
network structures give rise to the necessary dynamics. Here we study the
discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004]
and the set of networks capable of reproducing it, which we call functional.
Among these, the empirical yeast wildtype network is close to optimal with
respect to sparse wiring. Under point mutations, which establish or delete
single interactions, the neutral space of functional networks is fragmented
into 4.7 * 10^8 components. One of the smaller ones contains the wildtype
network. On average, functional networks reachable from the wildtype by
mutations are sparser, have higher noise resilience and fewer fixed point
attractors as compared with networks outside of this wildtype component.Comment: 6 pages, 5 figure
Unravelling the Yeast Cell Cycle Using the TriGen Algorithm
Analyzing microarray data represents a computational challenge
due to the characteristics of these data. Clustering techniques are
widely applied to create groups of genes that exhibit a similar behavior
under the conditions tested. Biclustering emerges as an improvement of
classical clustering since it relaxes the constraints for grouping allowing
genes to be evaluated only under a subset of the conditions and not under
all of them. However, this technique is not appropriate for the analysis of
temporal microarray data in which the genes are evaluated under certain
conditions at several time points. In this paper, we present the results of
applying the TriGen algorithm, a genetic algorithm that finds triclusters
that take into account the experimental conditions and the time points,
to the yeast cell cycle problem, where the goal is to identify all genes
whose expression levels are regulated by the cell cycle
Dynamics of gene expression and the regulatory inference problem
From the response to external stimuli to cell division and death, the
dynamics of living cells is based on the expression of specific genes at
specific times. The decision when to express a gene is implemented by the
binding and unbinding of transcription factor molecules to regulatory DNA.
Here, we construct stochastic models of gene expression dynamics and test them
on experimental time-series data of messenger-RNA concentrations. The models
are used to infer biophysical parameters of gene transcription, including the
statistics of transcription factor-DNA binding and the target genes controlled
by a given transcription factor.Comment: revised version to appear in Europhys. Lett., new titl
A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and analysis methods
Motivation: The rapid expansion of whole-genome copy number (CN) studies brings a demand for increased precision and resolution of CN estimates. Recent studies have obtained CN estimates from more than one platform for the same set of samples, and it is natural to want to combine the different estimates in order to meet this demand. Estimates from different platforms show different degrees of attenuation of the true CN changes. Similar differences can be observed in CNs from the same platform run in different labs, or in the same lab, with different analytical methods. This is the reason why it is not straightforward to combine CN estimates from different sources (platforms, labs and analysis methods)
Application of regulatory sequence analysis and metabolic network analysis to the interpretation of gene expression data
We present two complementary approaches for the interpretation of clusters of
co-regulated genes, such as those obtained from DNA chips and related methods.
Starting from a cluster of genes with similar expression profiles, two basic
questions can be asked:
1. Which mechanism is responsible for the coordinated transcriptional response
of the genes? This question is approached by extracting motifs that are shared
between the upstream sequences of these genes. The motifs extracted are putative
cis-acting regulatory elements.
2. What is the physiological meaning for the cell to express together these
genes? One way to answer the question is to search for potential metabolic
pathways that could be catalyzed by the products of the genes. This can be
done by selecting the genes from the cluster that code for enzymes, and trying
to assemble the catalyzed reactions to form metabolic pathways.
We present tools to answer these two questions, and we illustrate their use with
selected examples in the yeast Saccharomyces cerevisiae. The tools are available
on the web (http://ucmb.ulb.ac.be/bioinformatics/rsa-tools/;
http://www.ebi.ac.uk/research/pfbp/; http://www.soi.city.ac.uk/~msch/)
The Iterative Signature Algorithm for the analysis of large scale gene expression data
We present a new approach for the analysis of genome-wide expression data.
Our method is designed to overcome the limitations of traditional techniques,
when applied to large-scale data. Rather than alloting each gene to a single
cluster, we assign both genes and conditions to context-dependent and
potentially overlapping transcription modules. We provide a rigorous definition
of a transcription module as the object to be retrieved from the expression
data. An efficient algorithm, that searches for the modules encoded in the data
by iteratively refining sets of genes and conditions until they match this
definition, is established. Each iteration involves a linear map, induced by
the normalized expression matrix, followed by the application of a threshold
function. We argue that our method is in fact a generalization of Singular
Value Decomposition, which corresponds to the special case where no threshold
is applied. We show analytically that for noisy expression data our approach
leads to better classification due to the implementation of the threshold. This
result is confirmed by numerical analyses based on in-silico expression data.
We discuss briefly results obtained by applying our algorithm to expression
data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure
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