G.Balázsi, A.L.Barabási, and Z.N.Oltvai

1. Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli G.Balázsi, A.L.Barabási, and Z.N.Oltvai

2. Central Dogma • The DNA is segmented into genes, where each gene encodes a protein • The central dogma of biology –is a two step process • The interesting thing here is the feedback – proteins that are produced from DNA can influence the transcription rate of other proteins – Transcription factors

3. Gene Regulation Example of Gene regulation: Lac operon

4. Microarrays • Technique to detect the gene expression • Two complementary DNA strand “Hybridize” (find its match), signal is emitted to denote its expression • DNA attached to solid support(glass, plastic) • RNA is labeled– “Target”. Bound DNA is the “probe”

5. Z-score and Normalization • Normalization: Adjusting values measured on different scales to a common scale • Take any two number : x = {2,6} • Calculate mean and standard deviation Convert the population to z-score • Recalculate mean and standard deviation

6. Cell and environmental cues • Cells react to environmental cues/factors • Reaction are normally Regulatory • Each reaction is highly interconnected forming a Regulatory Network. • E. Coli – 1000 protein types at any given moment >4000 genes (or possible protein types) – need regulatory mechanism to select the active set • We are interested in the design principles of this network

7. Questions raised and claims proposed • Extensive studies about the TR Networks- But are we missing extracellular environmental factors? -Topological units of environmental signal processing called ORIGONS are present at the root nodes as sensor’s specialized for their detection • How do they react when complex environmental signals are subjected? -They brake down to elementary components and develop response by reassembling near the output layer

8. X Y The E.coli Regulatory Network • Nodes are proteins (or the genes that encode them) • Edges represent regulatory relation between two proteins

9. Real vs Random Networks • Patterns that occur in the real network are much more complex than in a randomized network, and have functional significance. • The randomized networks share the same number of edges and number of nodes, but edges are assigned at random

10. 13 3-node circuits Methods-Analysis of the Networks • Find n-node sub-graphs in real graph. • Find all n-node sub-graphs in a set of randomized graphs with the same distribution of incoming and outgoing arrows. Assign Z-score for each sub-graph. • Sub-graphs with high Z-scores are denoted as Network Motifs. (Newman, 2000, Sneppen, Malsov 2002)

11. E.Coli Transcription Network as we know

12. E.Coli Network form random graph

13. Methods: DATA and control

14. Correlation and Covariance Node-Node Correlation Node – Signal Covariance Sc - signal Node signal covariance between node i and signal for all the genes belonging to operon I LRr,c - Log ratios of row (Genes of operon1) LRr’ ,c - Log ratios of row (Genes ofoperon2) i,j- operons, Standard deviation from row r

15. Double Z score and Hypothesis Testing Ho – Origons are Significantly affected by the external signal Ha – They are not affected at all Test Statistic: z Test For nodes within the network, Where, (cov)ns for all n- nodes =1,2… µNs and are mean and std deviation of For nodes affected by external signals, Where, µo = (zNs)o over all No nodes of Origon and µR and are mean and std deviation for No nodes in the Randomized graph. Based on the Z score, the Null/Alternate Hypothesis is accepted or Rejected.

16. Results – Regulatory interaction + Sub networks • Based on the studies, 76 input nodes (sensor TF’s) and 312 output nodes(non TF proteins) were assigned • Topological units can be differentiated into operon from layer 0 (input layer)and sensory inputs (TF’s with environment dependent activity) • Thus, these sub networks directly or indirectly regulate upon an environmental signal- Regulation

17. Results: Single and complex signals • Single signal affects the activity of expression level of single sensor TF • For Complex signal, signal processing takes place via isolated origon (25 origons) and hence signal re-combination of connected origons ( 6 origons of 3 2-node ) • The feed forward loop is a filter for transient signals while allowing fast shutdown

18. Results: Intracellular Node correlation • Given two nodes, measure the deviation of their correlation from correlation of randomly chosen nodes. • From the Z values for non- specific perturbation, both in the fnr and crp region , there is no significant changes • Obvious significance for FNR- specific

19. Results: Motifs and Sub graphs occupy distinct position in the Origon • Signal percolates from top to bottom altering the Gene expression (SRI- Single regulatory Interactions; DIV-divergence; CNV- convergence; CAS- Cascade; FFL- Feed Forward) • The three node network is picked and relative abundance was found • FFL graphs were significantly more • Majority of Origons were Trees • They contained DIV and CAS sub graphs • CNV was absent from origons(combine perturbation during propagation • SRI are low pass filter

20. SRI network and Mechanism • SRI has a simple mechanism • Based on the time- course of the output protein levels after periodic perturbation, the relative abundance of the sub graphs were estimated

21. Summary • ORIGONS are responsible for Environmental perturbation Processing • Origons are rooted at the TF’s, sensitive to the signal • Based on complexity, Environmental perturbations can be elementary(Change of a single factor on constant environment) or complex(simultaneous changes of two or more factors) • ORIGONS concept suggest that Transcriptional regulatory level cells perceive the environmental signal by first breaking down it into elementary perturbation by individual origons, followed by reassembling these origons near output • Given idea about Dimensionality Reduction and Singular Value Decomposition , while dealing with large complex data.

22. Thank you