Inferring transcriptional logic from multiple dynamic experiments

Abstract

MotivationThe 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.ResultsWe 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 implementationPrograms 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.Supplementary information Supplementary data are available at Bioinformatics online.

Plant Synthetic Biology: Quantifying the “Known Unknowns” and Discovering the “Unknown Unknowns”

“…Several groups have developed frameworks for computational design of the optimal set of experiments to identify the mathematical relationship among the signaling inputs, network status, and the developmental outcome, i.e. model selection (Busetto et al, 2013;Apri et al, 2014;Vanlier et al, 2014;Minas et al, 2017;Rougny et al, 2018). Other statistical frameworks aim to design optimal experiments for determining parameter uncertainty in the chosen model (Dehghannasiri et al, 2015;Fan et al, 2015;Imani et al, 2018;Mohsenizadeh et al, 2018).…”

“…For example, Dehghannasiri et al 2015provides a method for prioritizing future experiments based on existing knowledge of a gene regulatory network and the desired intervention in the network, where intervention in this case is a therapy targeting a pathological network state. Systems biology approaches including similar frameworks have facilitated inference of networks and logic in plant development (Astola et al, 2014;Fisher and Sozzani, 2016;Ristova et al, 2016;de Luis Balaguer et al, 2017;Minas et al, 2017;Shibata et al, 2018;Varala et al, 2018). In addition to optimally improving our knowledge of developmental networks, connecting signaling network models with phenotypic outcome models are of particular importance to the goal of engineering plant development (Prusinkiewicz and Runions, 2012;O'Connor et al, 2014;Landrein et al, 2015;Mellor et al, 2017;Schnepf et al, 2018).…”