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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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Related in: MedlinePlus

Unregulated TFs in P. aeruginosa, M. tuberculosis and other yeast datasets.Percentage of unregulated TFs for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (2002) (C), the yeast dataset derived from Luscombe et al. (2004) (D), and the yeast dataset derived from MacIsaac et al. (2006) (E). In each case the real dataset is compared with a randomly simulated network containing the same number of genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note the statistically significant difference of real and random data in all the dataset considered.DOI:http://dx.doi.org/10.7554/eLife.02863.016
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fig4s1: Unregulated TFs in P. aeruginosa, M. tuberculosis and other yeast datasets.Percentage of unregulated TFs for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (2002) (C), the yeast dataset derived from Luscombe et al. (2004) (D), and the yeast dataset derived from MacIsaac et al. (2006) (E). In each case the real dataset is compared with a randomly simulated network containing the same number of genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note the statistically significant difference of real and random data in all the dataset considered.DOI:http://dx.doi.org/10.7554/eLife.02863.016

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)

Unregulated TFs in P. aeruginosa, M. tuberculosis and other yeast datasets.Percentage of unregulated TFs for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (2002) (C), the yeast dataset derived from Luscombe et al. (2004) (D), and the yeast dataset derived from MacIsaac et al. (2006) (E). In each case the real dataset is compared with a randomly simulated network containing the same number of genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note the statistically significant difference of real and random data in all the dataset considered.DOI:http://dx.doi.org/10.7554/eLife.02863.016
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4s1: Unregulated TFs in P. aeruginosa, M. tuberculosis and other yeast datasets.Percentage of unregulated TFs for M. tuberculosis (A), P. aeruginosa (B), the yeast dataset derived from Lee et al. (2002) (C), the yeast dataset derived from Luscombe et al. (2004) (D), and the yeast dataset derived from MacIsaac et al. (2006) (E). In each case the real dataset is compared with a randomly simulated network containing the same number of genes, TFs and connections. For the random networks, each graph reports the mean and standard deviation. Note the statistically significant difference of real and random data in all the dataset considered.DOI:http://dx.doi.org/10.7554/eLife.02863.016
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