2,975 research outputs found
Assessing the impact of non-additive noise on modelling transcriptional regulation with Gaussian processes
In transcriptional regulation, transcription factors (TFs) are often
unobservable at mRNA level or may be controlled outside of the system being
modelled. Gaussian processes are a promising approach for dealing with these
difficulties as a prior distribution can be defined over the latent TF activity
profiles and the posterior distribution inferred from the observed expression levels
of potential target genes. However previous approaches have been based on the
assumption of additive Gaussian noise to maintain analytical tractability. We
investigate the influence of a more realistic form of noise on a biologically accurate
system based on Michaelis-Menten kinetics
Modelling transcriptional regulation with Gaussian processes
A challenging problem in systems biology is the quantitative modelling
of transcriptional regulation. Transcription factors (TFs), which are the
key proteins at the centre of the regulatory processes, may be subject
to post-translational modification, rendering them unobservable at the
mRNA level, or they may be controlled outside of the subsystem being
modelled. In both cases, a mechanistic model description of the regula-
tory system needs to be able to deal with latent activity profiles of the key
regulators. A promising approach to deal with these difficulties is based
on using Gaussian processes to define a prior distribution over the latent
TF activity profiles. Inference is based on the principles of non-parametric
Bayesian statistics, consistently inferring the posterior distribution of the
unknown TF activities from the observed expression levels of potential
target genes. The present work provides explicit solutions to the differ-
ential equations needed to model the data in this manner, as well as the
derivatives needed for effective optimisation. The work further explores
identifiability issues not fully shown in previous work and looks at how
this can cause difficulties with inference. We subsequently look at how the
method works on two different TFs, including looking at how the model
works with a more biologically realistic mechanistic model. Finally we
analyse the effect of more biologically realistic non-Gaussian noise on the
biologically realistic model showing how this can cause a reduction in the
accuracy of the inference
Inferring dynamic genetic networks with low order independencies
In this paper, we propose a novel inference method for dynamic genetic
networks which makes it possible to face with a number of time measurements n
much smaller than the number of genes p. The approach is based on the concept
of low order conditional dependence graph that we extend here in the case of
Dynamic Bayesian Networks. Most of our results are based on the theory of
graphical models associated with the Directed Acyclic Graphs (DAGs). In this
way, we define a minimal DAG G which describes exactly the full order
conditional dependencies given the past of the process. Then, to face with the
large p and small n estimation case, we propose to approximate DAG G by
considering low order conditional independencies. We introduce partial qth
order conditional dependence DAGs G(q) and analyze their probabilistic
properties. In general, DAGs G(q) differ from DAG G but still reflect relevant
dependence facts for sparse networks such as genetic networks. By using this
approximation, we set out a non-bayesian inference method and demonstrate the
effectiveness of this approach on both simulated and real data analysis. The
inference procedure is implemented in the R package 'G1DBN' freely available
from the CRAN archive
Weighted-Lasso for Structured Network Inference from Time Course Data
We present a weighted-Lasso method to infer the parameters of a first-order
vector auto-regressive model that describes time course expression data
generated by directed gene-to-gene regulation networks. These networks are
assumed to own a prior internal structure of connectivity which drives the
inference method. This prior structure can be either derived from prior
biological knowledge or inferred by the method itself. We illustrate the
performance of this structure-based penalization both on synthetic data and on
two canonical regulatory networks, first yeast cell cycle regulation network by
analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network
by analysing U. Alon's lab data
Genome-wide discovery of modulators of transcriptional interactions in human B lymphocytes
Transcriptional interactions in a cell are modulated by a variety of
mechanisms that prevent their representation as pure pairwise interactions
between a transcription factor and its target(s). These include, among others,
transcription factor activation by phosphorylation and acetylation, formation
of active complexes with one or more co-factors, and mRNA/protein degradation
and stabilization processes.
This paper presents a first step towards the systematic, genome-wide
computational inference of genes that modulate the interactions of specific
transcription factors at the post-transcriptional level. The method uses a
statistical test based on changes in the mutual information between a
transcription factor and each of its candidate targets, conditional on the
expression of a third gene. The approach was first validated on a synthetic
network model, and then tested in the context of a mammalian cellular system.
By analyzing 254 microarray expression profiles of normal and tumor related
human B lymphocytes, we investigated the post transcriptional modulators of the
MYC proto-oncogene, an important transcription factor involved in
tumorigenesis. Our method discovered a set of 100 putative modulator genes,
responsible for modulating 205 regulatory relationships between MYC and its
targets. The set is significantly enriched in molecules with function
consistent with their activities as modulators of cellular interactions,
recapitulates established MYC regulation pathways, and provides a notable
repertoire of novel regulators of MYC function. The approach has broad
applicability and can be used to discover modulators of any other transcription
factor, provided that adequate expression profile data are available.Comment: 15 pages, 3 figures, 2 tables; minor changes following referees'
comments; accepted to RECOMB0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes
Multiple biological processes are driven by oscillatory gene expression at
different time scales. Pulsatile dynamics are thought to be widespread, and
single-cell live imaging of gene expression has lead to a surge of dynamic,
possibly oscillatory, data for different gene networks. However, the regulation
of gene expression at the level of an individual cell involves reactions
between finite numbers of molecules, and this can result in inherent randomness
in expression dynamics, which blurs the boundaries between aperiodic
fluctuations and noisy oscillators. Thus, there is an acute need for an
objective statistical method for classifying whether an experimentally derived
noisy time series is periodic. Here we present a new data analysis method that
combines mechanistic stochastic modelling with the powerful methods of
non-parametric regression with Gaussian processes. Our method can distinguish
oscillatory gene expression from random fluctuations of non-oscillatory
expression in single-cell time series, despite peak-to-peak variability in
period and amplitude of single-cell oscillations. We show that our method
outperforms the Lomb-Scargle periodogram in successfully classifying cells as
oscillatory or non-oscillatory in data simulated from a simple genetic
oscillator model and in experimental data. Analysis of bioluminescent live cell
imaging shows a significantly greater number of oscillatory cells when
luciferase is driven by a {\it Hes1} promoter (10/19), which has previously
been reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter
(0/25). The method can be applied to data from any gene network to both
quantify the proportion of oscillating cells within a population and to measure
the period and quality of oscillations. It is publicly available as a MATLAB
package.Comment: 36 pages, 17 figure
How to understand the cell by breaking it: network analysis of gene perturbation screens
Modern high-throughput gene perturbation screens are key technologies at the
forefront of genetic research. Combined with rich phenotypic descriptors they
enable researchers to observe detailed cellular reactions to experimental
perturbations on a genome-wide scale. This review surveys the current
state-of-the-art in analyzing perturbation screens from a network point of
view. We describe approaches to make the step from the parts list to the wiring
diagram by using phenotypes for network inference and integrating them with
complementary data sources. The first part of the review describes methods to
analyze one- or low-dimensional phenotypes like viability or reporter activity;
the second part concentrates on high-dimensional phenotypes showing global
changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio
Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana
Regulation of gene expression is crucial for organism growth, and it is one of the challenges in Systems Biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyse two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants to infer their regulatory network. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale-free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation
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