Dataset Information

Inferring transcriptional logic from multiple dynamic experiments.

ABSTRACT:

Motivation

The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable.

Results

We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions.

Availability and implementation

Programs are written in MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc., Natick, Massachusetts, United States and are available on GitHub https://github.com/giorgosminas/TRS and at http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software.

Contact

giorgos.minas@warwick.ac.uk or b.f.finkenstadt@warwick.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Minas G

PROVIDER: S-EPMC5860162 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Inferring transcriptional logic from multiple dynamic experiments.

Minas Giorgos G Jenkins Dafyd J DJ Rand David A DA Finkenstädt Bärbel B

Bioinformatics (Oxford, England) 20171101 21

<h4>Motivation</h4>The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable.<h4>Results</h4>We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcri ...[more]

PMID: 28666320

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Project description:BACKGROUND: Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. METHODOLOGY: Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. PRINCIPAL FINDINGS: SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. CONCLUSIONS/SIGNIFICANCE: The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method.

Project description:Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters and applied it to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences. Our analyses reveal a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts which are transcription-factor specific, including a striking ~10-bp periodic relationship between gene expression and binding-site location. We show how this approach can measure transcription-factor sequence specificities and the sensitivity of transcription-factor sites to the surrounding sequence context, and we compare the activity of 75 yeast transcription factors. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control. Expression profiling of 6500 synthetic promoters in yeast (Saccharomyces cerevisiae). The results were achieved using a method for obtaining pooled and highly accurate expression measurements (closely related to MPRA). In short, the synthetic promoters, which contain unique barcodes, are inserted upstream to a yellow florescence reporter gene, the cells are sorted by their florescence level (using FACS) to expression bins, the promoters in each expression bin are amplified and are send to parallel sequencing (SOLiD) to determine the percent of cells of each promoter in each expression bin; finally an mean expression value is extracted from the raw results. The measurements contain two replicates of exponentially growing yeast cells in SC-Gal-URA (synthetic complete media with 2% galactose and without uracil).

Dataset Information

Inferring transcriptional logic from multiple dynamic experiments.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Inferring transcriptional logic from multiple dynamic experiments.

Similar Datasets

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