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Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks.

Albergante L, Blow JJ, Newman TJ - Elife (2014)

Bottom Line: The gene regulatory network (GRN) is the central decision-making module of the cell.BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response.BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, University of Dundee, Dundee, United Kingdom l.albergante@dundee.ac.uk.

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Number of unregulated TFs in the RegulonDB (E. coli) and Harbison et al. (2004) (yeast) dataset under different statistical conditions.The number of unregulated TFs in E. coli (A) and yeast (B) remain very high regardless of the conditions used to construct the GRNs, supporting the strong embedding of this feature in the GRNs. See the caption of Figure 2—figure supplement 3 for the description of the different conditions associated with the network reconstruction.DOI:http://dx.doi.org/10.7554/eLife.02863.019
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fig4s4: Number of unregulated TFs in the RegulonDB (E. coli) and Harbison et al. (2004) (yeast) dataset under different statistical conditions.The number of unregulated TFs in E. coli (A) and yeast (B) remain very high regardless of the conditions used to construct the GRNs, supporting the strong embedding of this feature in the GRNs. See the caption of Figure 2—figure supplement 3 for the description of the different conditions associated with the network reconstruction.DOI:http://dx.doi.org/10.7554/eLife.02863.019

Mentions: An important global network property constrained by BQS is the degree of cross-regulation between TFs. Since a TF must be both regulated and regulating to take part in a feedback loop, one way that GRNs could satisfy BQS and minimise the risk of unstable loops being formed, is by having a high proportion of TFs that are not regulated by other TFs. Consistent with this prediction, the percentage of unregulated TFs in E. coli, S. cerevisiae, M. tuberculosis, P. aeruginosa, human and other yeast datasets is very high (Figure 4A, Figure 4—figure supplement 1A–E). Comparison with random networks indicates that the probability of obtaining this proportion of unregulated TFs by chance is between 10−68 and 10−39 (Figure 4—figure supplement 2A–C). Similar results hold for M. tuberculosis (Figure 4—figure supplement 1A), P. aeruginosa (Figure 4—figure supplement 1B), and other yeast datasets (Figure 4—figure supplement 1C–E). These results are robust to variations in the confidence levels of the E. coli and S. cerevisiae GRNs (Figure 4—figure supplement 4A,B), and remain valid when different random models are considered (Figure 2—figure supplement 4A).10.7554/eLife.02863.015Figure 4.Evidence for BQS from TF regulation.


Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks.

Albergante L, Blow JJ, Newman TJ - Elife (2014)

Number of unregulated TFs in the RegulonDB (E. coli) and Harbison et al. (2004) (yeast) dataset under different statistical conditions.The number of unregulated TFs in E. coli (A) and yeast (B) remain very high regardless of the conditions used to construct the GRNs, supporting the strong embedding of this feature in the GRNs. See the caption of Figure 2—figure supplement 3 for the description of the different conditions associated with the network reconstruction.DOI:http://dx.doi.org/10.7554/eLife.02863.019
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4151086&req=5

fig4s4: Number of unregulated TFs in the RegulonDB (E. coli) and Harbison et al. (2004) (yeast) dataset under different statistical conditions.The number of unregulated TFs in E. coli (A) and yeast (B) remain very high regardless of the conditions used to construct the GRNs, supporting the strong embedding of this feature in the GRNs. See the caption of Figure 2—figure supplement 3 for the description of the different conditions associated with the network reconstruction.DOI:http://dx.doi.org/10.7554/eLife.02863.019
Mentions: An important global network property constrained by BQS is the degree of cross-regulation between TFs. Since a TF must be both regulated and regulating to take part in a feedback loop, one way that GRNs could satisfy BQS and minimise the risk of unstable loops being formed, is by having a high proportion of TFs that are not regulated by other TFs. Consistent with this prediction, the percentage of unregulated TFs in E. coli, S. cerevisiae, M. tuberculosis, P. aeruginosa, human and other yeast datasets is very high (Figure 4A, Figure 4—figure supplement 1A–E). Comparison with random networks indicates that the probability of obtaining this proportion of unregulated TFs by chance is between 10−68 and 10−39 (Figure 4—figure supplement 2A–C). Similar results hold for M. tuberculosis (Figure 4—figure supplement 1A), P. aeruginosa (Figure 4—figure supplement 1B), and other yeast datasets (Figure 4—figure supplement 1C–E). These results are robust to variations in the confidence levels of the E. coli and S. cerevisiae GRNs (Figure 4—figure supplement 4A,B), and remain valid when different random models are considered (Figure 2—figure supplement 4A).10.7554/eLife.02863.015Figure 4.Evidence for BQS from TF regulation.

Bottom Line: The gene regulatory network (GRN) is the central decision-making module of the cell.BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response.BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, University of Dundee, Dundee, United Kingdom l.albergante@dundee.ac.uk.

Show MeSH
Related in: MedlinePlus