Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

20042016_pizzaclub_part2

125 views

Published on

A new mathematical framework for network analysis.

Published in: Science
  • Be the first to comment

  • Be the first to like this

20042016_pizzaclub_part2

  1. 1. eLife, 2014 Pizza Club 20 April 2016 Gaia Zaffaroni
  2. 2. Some terminology • GRN: gene regulatory network • A network is composed of: • Nodes, represent genes • Edges, represent interactions, e.g. protein-protein physical interaction, co-expression, transcriptional regulation, … • Topology: the structure of the network • Robustness: is a complex property of the system that makes it able to tolerate a wide variety of perturbations (any change in the conditions) maintaining its function
  3. 3. Motifs Tran, N. H. et al. Counting motifs in the human interactome. Nat. Commun. 4:2241 doi: 10.1038/ncomms3241 (2013).
  4. 4. Introduction • Interaction networks are a fundamental feature of biological systems • Biological networks are stable: they can recover their equilibrium state after perturbation • Selective pressure causes them to have specific topologies • Transcriptional networks: • Nodes=genes and transcription factors • Edges=transcriptional regulation • Assumption: gene expression level corresponds to protein activity level •  these networks cannot capture post-transcriptional and translational regulations
  5. 5. Real networks • Collection of curated transcriptional networks • Examples: E.coli, M.tuberculosis, P.aeruginosa, S.cerevisiae, mouse and human
  6. 6. Hypothesis • To be stable, the network should not depend on the change of any of the individual quantitative parameters • protein concentration, • affinity for a DNA sequence, • promoter availability, • rate of transcription • It should also be stable to the addition of new links • The robustness then should depend on qualitative features of the network
  7. 7. Qualitative Stability • The topology is stable even if the edge strength changes • Mathematical concept: • Long feedback loops are negative for stability • They are in general associated with oscillations, but in a real system they can cause chaotic behavior
  8. 8. Presence of feedback loops
  9. 9. Presence of incomplete feedback loops
  10. 10. TF regulation
  11. 11. Motifs
  12. 12. Illegal feedback loops E. coli There are 7 2-node feedback loops: 4 are into potentially instable motifs 3 can act as switches These genes are related with drug resistance and/or acid resistance Similar configuration that can display chaotic behavior
  13. 13. Cancer cells K562 (Leukemia cell line) GM12878 (non-cancer cell line)
  14. 14. Dynamic networks Murine dentritic cells after stimulation with pathogens
  15. 15. Dynamic networks
  16. 16. Conclusions • BQS allows to do new predictions based on the robustness “criteria” • It provides theoretical justification for observed network features • It helps in explaining the overall structure of GRNs at different scales

×