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Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.

ABSTRACT: 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.

SUBMITTER: Shojaie A

PROVIDER: S-EPMC3938831 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.

Shojaie Ali A Jauhiainen Alexandra A Kallitsis Michael M Michailidis George G

PloS one 20140228 2

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 ...[more]

PMID: 24586224

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Project description:MotivationSingle cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.ResultsWe developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.Availability and implementationMATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:MotivationIdentifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.ResultsIn this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.

Project description:The â??small perturbationâ?? approach is critical in studying the â??steady stateâ?? of a biological system. In our experiments, small perturbations were generated by applying a series of repeating intermittent small doses of ultraviolet radiation to a human keratinocyte cell line, HaCaT. The biological response was assessed by monitoring the gene expression profiles using a high reliability and high resolution cDNA microarray system. Following intermittent 10 J/m2 UVB small perturbations, two opposite classes of genes, down-regulated and up-regulated, exhibited an immediate response followed by relaxation between each small perturbation, but were prolonged down- or up-regulated without relaxation while larger doses (233 or 582.5 J/m2) of UVB were applied. A repeated cycle pattern of gene expression following small perturbations is an indication of the existence of steady states. This cycle pattern is suppressed when large perturbations are applied. We believe that this is a universal phenomenon. In our experiments, the functions of up-regulated genes were mainly associated with anti-proliferation, anti-mitogenesis, and apoptosis. On the other hand, down-regulated genes were mainly related to proliferation, mitogenesis, and anti-apoptosis. In conclusion, this study experimentally proves the concept of steady state at the transcription level and demonstrates the feasibility of using small perturbation approaches for investigating steady states. This study could also set a foundation of computational systems biology, which has implicitly used the concept of steady state. Keywords: time course Three UVB exposures are indicated by UV at time points of 0, 8 and 16 hours. T1, T2, T3, T4, T5, and T6 denote the sampling time points. T1, T3 and T5 are allocated 30 minutes after the corresponding UVB irradiation. T2, T4 and T6 are allocated 8 hours after each UVB irradiation. At each sampling time point, two samples (control and UV-irradiated) are collected. See supplementary file Loop_design.pdf for further explanation.

Dataset Information

Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.

Publications

Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.

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