Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network

1. Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network Authors Avi Ma’ayan, Sherry L. Jenkins, Susana Neves, Anthony Hasseldine, Elizabeth Grace, Benjamin Dubin-Thaler, Narat J. Eungdamrong, Gehzi Weng, Prahlad T. Ram, J. Jeremy Rice, Aaron Kershenbaum, Gustavo A. Stolovitzky, Robert D. Blitzer, Ravi Iyengar

2. Overview • Basic Terminology • Bird’s Eye View • Motifs in the network • Measures Used • Statistics (Results) • Future Work • Future Research Questions

3. Bio Chemical Reactions form Signaling pathways • Bio-Chemical Reaction • Signaling Pathways

4. Ligands • Ligands – ion, atom or molecule that binds with a receptor(protein) • Full agonist • Partial agonist • Antagonist

5. Cellular Machines • Cellular Machines are responsible for phenotypic functions. • Types of Cellular Machines are: • Transcription – The process of converting DNA to RNA. • Translation – The process of converting RNA to Proteins. • Secretory – to carry proteins from 1 point to another. • Motility – motion of the cell itself.

6. Bird’s Eye View • A model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron was developed. • Depth first search was used generate Cellular machines . • Networking resulted in the emergence of regulatory motifs. These motifs were responsible for processing information. • It was found that highly connected nodes were needed in order to form regulatory motifs and were key regulators of plasticity. • They found that motifs can play a major role in the cellular choices between homeostasis and plasticity.

7. Bird’s Eye View (Network) Visualized using the ‘Pajek’ Software Block Diagram

8. It’s a Small World • In this case, the network formed is a small-world network. • A small world network tends to be highly clustered. • Small world networks can be characterized by their characteristic path length and clustering coefficient. • Types of small world networks: • Broad-Scale networks • Single-Scale Networks • Scale-Free networks

9. Scale – free Networks A scale-free network is a connected graph with the property that the number of links k originating from a given node exhibits a power law distribution Where, ki – number of existing links

10. Shuffled Networks • Shuffled networks are networks in which the edges of real networks are systematically randomized while keeping intact some general properties of the original topology such as the connectivity degree. • Shuffled networks were used for statistical control. • In this study, At least 100 shuffled networks were generated.

11. Links • Links can be of Three types: • Activating (Positive) • Neutral • Inhibitory (Negative)

12. Motifs - Types of loops A motif is a group of interacting components, capable of signal processing. Scaffold

13. Motifs found in the network

14. Measures Used – Motif Location Index (MLI) • MLI measures the concentration of motifs and various locations within the network. • CPLM - Characteristic path length from a node within the motif • to all other nodes in the cellular machine. • CPLL - Characteristic path length from a node to all extracellular ligands. • n – Size of the motif. • i – step

15. Characteristic Path Length • Characteristic path length can be obtained by: • Calculating the average distance from a certain vertex to all the other vertices • After doing this for all every vertex v ∈V(G), the median of all the previously calculated ‘dv’ is calculated. • The resultant value obtained is the characteristic path length.

16. Clustering Coefficient • It represents the interconnectedness of nodes in a graph. • Clustering Coefficient is the average of all the local clustering Coefficients of all the Vertices ‘n’. • Local Clustering Coefficient is the proportion of links between the vertices within its neighborhood divided by the number of links that could possibly exist between them • Where, Neighborhood, ki= Number of nodes in the neighborhood. • Quantifies how close it’s neighbors are from being a clique.

17. Density of Information Processing (DIP) • DIP is a measure of the local density of motifs and their interconnectedness within the interaction space of the network. • L - Total number of links • i – Step • GC – Grid Coefficient (extensions of the clustering coefficient which considers Rectangles) • Grid Coefficient represents the interconnected ness of the motifs.

18. Statistics- Number of steps to links for each ligand

19. Islands

20. Role of highly connected nodes

21. Hot Zones – Information Processing hubs

22. Distribution of Interactions

23. Cellular Networks Vs. Shuffled Networks

24. Clustering & Grid Coefficients

25. More Graphs • Within the sub networks. • Calculation of expected values using combinatorial probability.

26. Future Work • The network is organized solely in terms of the chemical space. It is necessary to integrate this with the physical compartmentalization of the cell. That is, the chemical space and the physical space have not been mapped accurately. • Combinations if ligands will probably produce more patterns of connectivity

27. Future Research Questions • Can this model be applied to multiple neurons? This larger model can be used to simulate the pathways in other specific regions of the brain, with appropriate changes to understand the functioning. • Based on this paper, is there scope for future research wherein, artificial devices, which can perform various neurological functions, be constructed? • Since the motifs occurring in the hippocampal CA1 neuron are now known by virtue of this report, does this open gates to future research on abnormal function of a human body part, due to abnormalities in the motif(s) found in the hippocampal CA1 neuron?

28. References http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032439/ http://www.pnas.org/content/97/21/11149.long http://edgeperspectives.typepad.com/edge_perspectives/2007/05/the_power_of_po.html http://www.scielo.br/img/fbpe/bjmbr/v38n3/html/5683i01.htm http://mathworld.wolfram.com/Scale-FreeNetwork.html http://www.alexeikurakin.org/main/lecture4Ext.html http://en.wikipedia.org/wiki/Clustering_coefficient#cite_note-WattsStrogatz1998-2 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2645810/ Thankyou!