29,376 research outputs found
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
Reverse-engineering of polynomial dynamical systems
Multivariate polynomial dynamical systems over finite fields have been
studied in several contexts, including engineering and mathematical biology. An
important problem is to construct models of such systems from a partial
specification of dynamic properties, e.g., from a collection of state
transition measurements. Here, we consider static models, which are directed
graphs that represent the causal relationships between system variables,
so-called wiring diagrams. This paper contains an algorithm which computes all
possible minimal wiring diagrams for a given set of state transition
measurements. The paper also contains several statistical measures for model
selection. The algorithm uses primary decomposition of monomial ideals as the
principal tool. An application to the reverse-engineering of a gene regulatory
network is included. The algorithm and the statistical measures are implemented
in Macaulay2 and are available from the authors
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
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
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