51,937 research outputs found
Sparse regulatory networks
In many organisms the expression levels of each gene are controlled by the
activation levels of known "Transcription Factors" (TF). A problem of
considerable interest is that of estimating the "Transcription Regulation
Networks" (TRN) relating the TFs and genes. While the expression levels of
genes can be observed, the activation levels of the corresponding TFs are
usually unknown, greatly increasing the difficulty of the problem. Based on
previous experimental work, it is often the case that partial information about
the TRN is available. For example, certain TFs may be known to regulate a given
gene or in other cases a connection may be predicted with a certain
probability. In general, the biology of the problem indicates there will be
very few connections between TFs and genes. Several methods have been proposed
for estimating TRNs. However, they all suffer from problems such as unrealistic
assumptions about prior knowledge of the network structure or computational
limitations. We propose a new approach that can directly utilize prior
information about the network structure in conjunction with observed gene
expression data to estimate the TRN. Our approach uses penalties on the
network to ensure a sparse structure. This has the advantage of being
computationally efficient as well as making many fewer assumptions about the
network structure. We use our methodology to construct the TRN for E. coli and
show that the estimate is biologically sensible and compares favorably with
previous estimates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating sample-specific regulatory networks
Biological systems are driven by intricate interactions among the complex
array of molecules that comprise the cell. Many methods have been developed to
reconstruct network models of those interactions. These methods often draw on
large numbers of samples with measured gene expression profiles to infer
connections between genes (or gene products). The result is an aggregate
network model representing a single estimate for the likelihood of each
interaction, or "edge," in the network. While informative, aggregate models
fail to capture the heterogeneity that is represented in any population. Here
we propose a method to reverse engineer sample-specific networks from aggregate
network models. We demonstrate the accuracy and applicability of our approach
in several data sets, including simulated data, microarray expression data from
synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid
cell lines. We show that these sample-specific networks can be used to study
changes in network topology across time and to characterize shifts in gene
regulation that may not be apparent in expression data. We believe the ability
to generate sample-specific networks will greatly facilitate the application of
network methods to the increasingly large, complex, and heterogeneous
multi-omic data sets that are currently being generated, and ultimately support
the emerging field of precision network medicine
Existence of Oscillations in Cyclic Gene Regulatory Networks with Time Delay
This paper is concerned with conditions for the existence of oscillations in
gene regulatory networks with negative cyclic feedback, where time delays in
transcription, translation and translocation process are explicitly considered.
The primary goal of this paper is to propose systematic analysis tools that are
useful for a broad class of cyclic gene regulatory networks, and to provide
novel biological insights. To this end, we adopt a simplified model that is
suitable for capturing the essence of a large class of gene regulatory
networks. It is first shown that local instability of the unique equilibrium
state results in oscillations based on a Poincare-Bendixson type theorem. Then,
a graphical existence condition, which is equivalent to the local instability
of a unique equilibrium, is derived. Based on the graphical condition, the
existence condition is analytically presented in terms of biochemical
parameters. This allows us to find the dimensionless parameters that primarily
affect the existence of oscillations, and to provide biological insights. The
analytic conditions and biological insights are illustrated with two existing
biochemical networks, Repressilator and the Hes7 gene regulatory networks
Properties of developmental gene regulatory networks
The modular components, or subcircuits, of developmental gene regulatory networks (GRNs) execute specific developmental functions, such as the specification of cell identity. We survey examples of such subcircuits and relate their structures to corresponding developmental functions. These relations transcend organisms and genes, as illustrated by the similar structures of the subcircuits controlling the specification of the mesectoderm in the Drosophila embryo and the endomesoderm in the sea urchin, even though the respective subcircuits are composed of nonorthologous regulatory genes
Dominant Vertices in Regulatory Networks Dynamics
Discrete-time regulatory networks are dynamical systems on directed graphs,
with a structure inspired on natural systems of interacting units. There is a
natural notion of determination amongst vertices, which we use to classify the
nodes of the network, and to determine what we call "sets of dominant
vertices". In this paper we prove that in the asymptotic regime, the projection
of the dynamics on a dominant set allows us to determine the state of the whole
system at all times. We provide an algorithm to find sets of dominant vertices,
and we test its accuracy on three families of theoretical examples. Then, by
using the same algorithm, we study the relation between the structure of the
underlying network and the corresponding dominant set of vertices. We also
present a result concerning the inheritability of the dominance between
strongly connected networks
Intracellular Regulatory Networks are close to Monotone Systems
Several meso-scale biological intracellular regulatory networks that have specified directionality of interactions have been recently assembled from experimental literature. Directed networks where links are characterized as positive or negative can be converted to systems of differential equations and analyzed as dynamical systems. Such analyses have shown that networks containing only sign-consistent loops, such as positive feed-forward and feedback loops function as monotone systems that display well-ordered behavior. Perturbations to monotone systems have unambiguous global effects and a predictability characteristic that confers advantages for robustness and adaptability. We find that three intracellular regulatory networks: bacterial and yeast transcriptional networks and a mammalian signaling network contain far more sign-consistent feedback and feed-forward loops than expected for shuffled networks. Inconsistent loops with negative links can be more easily removed from real regulatory networks as compared to shuffled networks. This topological feature in real networks emerges from the presence of hubs that are enriched for either negative or positive links, and is not due to a preference for double negative links in paths. These observations indicate that intracellular regulatory networks may be close to monotone systems and that this network topology contributes to the dynamic stability
Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series
To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level. The advent of high-throughput postgenomic technologies has spurred substantial interest in reverse engineering these networks from data, and several techniques from machine learning and multivariate statistics have recently been proposed. The present article discusses the problem of inferring gene regulatory networks from gene expression time series, and we focus our exposition on the methodology of Bayesian networks. We describe dynamic Bayesian networks and explain their advantages over other statistical methods. We introduce a novel information sharing scheme, which allows us to infer gene regulatory networks from multiple sources of gene expression data more accurately. We illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy. The main application of our method is related to the problem of circadian regulation in plants, where we aim to reconstruct the regulatory networks of nine circadian genes in Arabidopsis thaliana from four gene expression time series obtained under different experimental conditions
Discovering study-specific gene regulatory networks
This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets
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