Known IEGs IUN and TRIBl were among the additional seven IEGs and five transcription factors that had this response in three of the four data sets, see S4 Fig for the numbers of Ensembl genes in the intersection of the kinetic signature categories across all data sets.

Clusters associated with transcription factors were also in more accessible regions (p = 0.018), but, surprisingly, clusters assigned to known IEGs or nucleotide binding genes did not differ significantly from the reference set in either MCF7 time course (p > 0.08 by Wilcoxon rank sum test).

In contrast, enhancer activity for transcription factors was 30% greater than for non-TFs (901 genes; p = 2.5e-14).

Many immediate-early genes (IEGs) encode transcription factors which regulate secondary response genes (SRGs) [6].

It is believed that delayed IEGs can be identified by their increased length, greater number of exons and lack of transcription factor activity in addition to the delayed timing of their expression in comparison with typical IEGs [6].

On the attenuation of the immediate-early response, detailed kinetic modelling of the transient upregulation of the Atf3 transcription factor (an inhibitor of Egr1) has concluded that, following induction, the mechanism whereby Atf3 is rapidly repressed is likely to involve newly-synthesised miRNA [13].

Only three of these genes were transcription factors and six were IEGs therefore these sets were essentially disjoint.

The timing of IEG induction and that of known transcription factors (TFs) is contrasted in Fig 2C where a relatively consistent pattern of IEG activation beginning with FOS, DUSP1 and IER2, and continuing with IUN, C-MYC, EGR1 and DUSP2 can be seen.

A number of non-IEG transcription factors were also activated: TBX2 activates early and RUNX1 late in the timelines.

Next, using human ENCODE ChlP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs.

Thus, we developed a general-purpose computational method using logic-circuit models from electronics and applied it to a human leukemia dataset, identifying the genome-Wide cooperatiVity of transcription factors and microRNAs.

Gene expression, transcription factor and miRNA datasets

transcription factors with their target genes identified in [49,50], and found 39,011 TF-TF-tar-get triplets with 2464 unique targets.

These regulatory factors affecting gene eXpression take several forms, such as transcription factors (TFs), which directly or indirectly bind DNA at promoter and enhancer regions of their target genes, and non-coding RNAs (e.g.

In this paper, we evaluate Loregic’s ability to analyze transcription factors , miRNAs and their target genes.

Scores of logic-gate-consistent triplets targeting the transcription factors at top, middle and bottom hierarchical layers in yeast.

We have applied this marker-free approach to screen for transcription factors that regulate mammary stem cell differentiation in a 3D model of tissue morphogenesis and identified RUNX1 as a stem cell regulator.

As a first step, we used gene-expression profiling to identify 39 developmentally implicated transcription factors (TFs) expressed in MCFIOA cells (82 Table).

For the idealized experiment, gene expression was profiled using standard qPCR and the 17 genes profiled were randomly selected transcription factors expressed by MCFIOA cells and implicated in differentiation.

MCFIOA cells were seeded onto a 96 well plate at a density of 7500 cells per well and infected the next day with hairpin lentiVirus targeting an expressed developmental transcription factor .

Inhibiting RUNXl expression caused a 2-fold increase in the number of stem cell micro-col-onies, suggesting that this transcription factor was required for primary human breast stem cells to differentiate in culture (Fig 8B).

Developmental transcription factors expressed in MCFlOA cells that were targeted with shRNAs.

We use stochastic and ordinary-differential-equation modeling frameworks to examine various possible mechanisms of gene regulation by multiple transcription factors .

We find that the assembly of large transcription factor complexes on chromatin via equilibrium-binding mechanisms is highly inefficient and insensitive to concentration changes of single regulatory proteins.

This leads to multiple transcription factors binding to the same promoter.

Eukaryotic transcription depends on dozens of proteins, including transcription factors (TFs), chromatin remodellers and RNA polymerase II components [1—7].

Gene repression by transcription factors , and glucocorticoid receptors (GR) in particular, is a critical, but poorly understood, physiological response.

The theory accommodates any number of pathway steps, transcription factors , and cofactors that alter the Amax, Amin, and/or IC50 of GR-controlled gene repression.

Finally, as for the theory for gene induction [20,22] , the current theory for gene repression is general for any gene induction and gene repression process displaying a linear-fractional dose-response and thus could be of use to analyze the mechanisms of other inducible transcription factors .

Finally, it is commonly accepted that transcription factors sometimes form oligomers before they act [17].

and patS) while rounded boxes and circles represent transcription factors (NtcA, HetR and PatS) and smaller molecules (2-OG and 0N) respectively.

Multiple transcription factors related to heterocyst formation are up-regulated by HetR, including hetR itself [35], ntcA [36] and patS [35].

Another transcription factor , PatS, inhibits the DNA-binding activity of HetR [8, 35, 40, 41].

Subscripts and superscripts identify the binding site and the transcription factor for Which the constants are given respectively.