Estimating the activity of transcription factors by the effect on their target genes

Abstract

Motivation: Understanding regulation of transcription is central for elucidating cellular regulation. Several statistical and mechanistic models have come up the last couple of years explaining gene transcription levels using information of potential transcriptional regulators as transcription factors (TFs) and information from epigenetic modifications. The activity of TFs is often inferred by their transcription levels, promoter binding and epigenetic effects. However, in principle, these methods do not take hard-to-measure influences such as post-transcriptional modifications into account.Results: For TFs, we present a novel concept circumventing this problem. We estimate the regulatory activity of TFs using their cumulative effects on their target genes. We established our model using expression data of 59 cell lines from the National Cancer Institute. The trained model was applied to an independent expression dataset of melanoma cells yielding excellent expression predictions and elucidated regulation of melanogenesis.Availability and implementation: Using mixed-integer linear programming, we implemented a switch-like optimization enabling a constrained but optimal selection of TFs and optimal model selection estimating their effects. The method is generic and can also be applied to further regulators of transcription.Contact: rainer.koenig@uni-jena.deSupplementary information: Supplementary data are available at Bioinformatics online.

The pan-cancer pathological regulatory landscape

“…Contrarily, the expression levels of the TF’s targets, in which all the above mentioned effects are integrated, seem to be a more reasonable readout of TF activity. Despite the simplicity of this idea and its enormous potential, only a few algorithmic proposals have been made that exploit TF’s target expression levels to infer the corresponding TF activities, such as BASE22, RENATO23, REACTIN24, RABIT25 or others26. These methods have been applied to the study of survival in breast cancer27 or to obtain signatures of tumour stage in kidney renal clear cell carcinoma28.…”

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

“…However, identification of functional regulatory interactions is hampered by the fact that expression is a poor proxy for TFs activity [16,17] and phosphorylation sites often display no functional impact in protein activity [7,33]. Therefore, to circumvent these limitations we have predicted the changes in activity of TFs and K/Ps.…”

“…Firstly, our computational approach predicted the in vivo activity of TFs and K/Ps. For that purpose, we considered prior-knowledge on regulatory interactions and mathematical approaches that have been developed to predict the activity status of transcription factors [16,17] and kinases [18,19] (Fig 1A). The activity of regulatory proteins is difficult to measure directly, yet provides functional information about the protein regulators involved in a cellular response.…”

Biophysically Motivated Regulatory Network Inference: Progress and Prospects

“…The key to efficiently estimating TF activities genomewide is data integration [7,[147][148][149][150][151][152][153][154][155][156][157][158][159] . Consider how we would estimate TF activities if we had a perfect regulatory model: we would simply plug in the measured expression observations and solve for the TF activities.…”

“…In practice, we are faced with estimating TF activities from noisy data with a very incomplete set of regulatory interactions. There are several groups that have demonstrated progress in developing methods that, with real data, can derive activity estimates that match assays of activity for select TFs [7,[147][148][149][150][151][152][153][154][155][156][157][158][159] . Importantly, these estimated TF activities improve downstream analysis and ultimately the accuracy and coverage of regulatory network inference.…”