10,028 research outputs found
Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks
The gene regulatory network (GRN) is the central decision‐making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large‐scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation. DOI: http://dx.doi.org/10.7554/eLife.02863.00
An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.
Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task in
functional genomics. Data obtained from experiments that perturb genes by
knockouts or RNA interference contain useful information for addressing this
reconstruction problem. However, such data can be limited in size and/or are
expensive to acquire. On the other hand, observational data of the organism in
steady state (e.g. wild-type) are more readily available, but their
informational content is inadequate for the task at hand. We develop a
computational approach to appropriately utilize both data sources for
estimating a regulatory network. The proposed approach is based on a three-step
algorithm to estimate the underlying directed but cyclic network, that uses as
input both perturbation screens and steady state gene expression data. In the
first step, the algorithm determines causal orderings of the genes that are
consistent with the perturbation data, by combining an exhaustive search method
with a fast heuristic that in turn couples a Monte Carlo technique with a fast
search algorithm. In the second step, for each obtained causal ordering, a
regulatory network is estimated using a penalized likelihood based method,
while in the third step a consensus network is constructed from the highest
scored ones. Extensive computational experiments show that the algorithm
performs well in reconstructing the underlying network and clearly outperforms
competing approaches that rely only on a single data source. Further, it is
established that the algorithm produces a consistent estimate of the regulatory
network.Comment: 24 pages, 4 figures, 6 table
Complex-based analysis of dysregulated cellular processes in cancer
Background: Differential expression analysis of (individual) genes is often
used to study their roles in diseases. However, diseases such as cancer are a
result of the combined effect of multiple genes. Gene products such as proteins
seldom act in isolation, but instead constitute stable multi-protein complexes
performing dedicated functions. Therefore, complexes aggregate the effect of
individual genes (proteins) and can be used to gain a better understanding of
cancer mechanisms. Here, we observe that complexes show considerable changes in
their expression, in turn directed by the concerted action of transcription
factors (TFs), across cancer conditions. We seek to gain novel insights into
cancer mechanisms through a systematic analysis of complexes and their
transcriptional regulation.
Results: We integrated large-scale protein-interaction (PPI) and
gene-expression datasets to identify complexes that exhibit significant changes
in their expression across different conditions in cancer. We devised a
log-linear model to relate these changes to the differential regulation of
complexes by TFs. The application of our model on two case studies involving
pancreatic and familial breast tumour conditions revealed: (i) complexes in
core cellular processes, especially those responsible for maintaining genome
stability and cell proliferation (e.g. DNA damage repair and cell cycle) show
considerable changes in expression; (ii) these changes include decrease and
countering increase for different sets of complexes indicative of compensatory
mechanisms coming into play in tumours; and (iii) TFs work in cooperative and
counteractive ways to regulate these mechanisms. Such aberrant complexes and
their regulating TFs play vital roles in the initiation and progression of
cancer.Comment: 22 pages, BMC Systems Biolog
Mathematical modelling plant signalling networks
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting
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