3,791 research outputs found
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
Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53
Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets.
Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region.
Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes
Current advances in systems and integrative biology
Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal
Multiple tests of association with biological annotation metadata
We propose a general and formal statistical framework for multiple tests of
association between known fixed features of a genome and unknown parameters of
the distribution of variable features of this genome in a population of
interest. The known gene-annotation profiles, corresponding to the fixed
features of the genome, may concern Gene Ontology (GO) annotation, pathway
membership, regulation by particular transcription factors, nucleotide
sequences, or protein sequences. The unknown gene-parameter profiles,
corresponding to the variable features of the genome, may be, for example,
regression coefficients relating possibly censored biological and clinical
outcomes to genome-wide transcript levels, DNA copy numbers, and other
covariates. A generic question of great interest in current genomic research
regards the detection of associations between biological annotation metadata
and genome-wide expression measures. This biological question may be translated
as the test of multiple hypotheses concerning association measures between
gene-annotation profiles and gene-parameter profiles. A general and rigorous
formulation of the statistical inference question allows us to apply the
multiple hypothesis testing methodology developed in [Multiple Testing
Procedures with Applications to Genomics (2008) Springer, New York] and related
articles, to control a broad class of Type I error rates, defined as
generalized tail probabilities and expected values for arbitrary functions of
the numbers of Type I errors and rejected hypotheses. The resampling-based
single-step and stepwise multiple testing procedures of [Multiple Testing
Procedures with Applications to Genomics (2008) Springer, New York] take into
account the joint distribution of the test statistics and provide Type I error
control in testing problems involving general data generating distributions
(with arbitrary dependence structures among variables), null hypotheses, and
test statistics.Comment: Published in at http://dx.doi.org/10.1214/193940307000000446 the IMS
Collections (http://www.imstat.org/publications/imscollections.htm) by the
Institute of Mathematical Statistics (http://www.imstat.org
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