16,332 research outputs found
A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data
Inferring gene regulatory networks is an important problem in systems
biology. However, these networks can be hard to infer from experimental data
because of the inherent variability in biological data as well as the large
number of genes involved. We propose a fast, simple method for inferring
regulatory relationships between genes from knockdown experiments in the NIH
LINCS dataset by calculating posterior probabilities, incorporating prior
information. We show that the method is able to find previously identified
edges from TRANSFAC and JASPAR and discuss the merits and limitations of this
approach
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
Towards knowledge-based gene expression data mining
The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks
Lipid Metabolism and Comparative Genomics
Unilever asked the Study Group to focus on two problems. The first concerned dysregulated lipid metabolism which is a feature of many diseases including metabolic syndrome, obesity and coronary heart disease. The Study Group was asked to develop a model of the kinetics of lipoprotein metabolism between healthy and obese states incorporating the activities of key enzymes.
The second concerned the use of comparative genomics in understanding and comparing metabolic networks in bacterium. Comparative genomics is a method to make inferences on the genome of a new organism using information of a previously charaterised organism. The first mathematical question is how one would quantify such a metabolic map in a statistical sense, in particular, where there are different levels of confidence for presense of different parts of the map. The next and most important question is how one can design a measurement strategy to maximise the confidence in the accuracy of the metabolic map
Learning from Neighbors about a Changing State
Agents learn about a changing state using private signals and past actions of
neighbors in a network. We characterize equilibrium learning and social
influence in this setting. We then examine when agents can aggregate
information well, responding quickly to recent changes. A key sufficient
condition for good aggregation is that each individual's neighbors have
sufficiently different types of private information. In contrast, when signals
are homogeneous, aggregation is suboptimal on any network. We also examine
behavioral versions of the model, and show that achieving good aggregation
requires a sophisticated understanding of correlations in neighbors' actions.
The model provides a Bayesian foundation for a tractable learning dynamic in
networks, closely related to the DeGroot model, and offers new tools for
counterfactual and welfare analyses.Comment: minor revision tweaking exposition relative to v5 - which added new
Section 3.2.2, new Theorem 2, new Section 7.1, many local revision
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