Network biology : protein – protein interactions מנחה : פרופסור אורה פורמן מציגים: גבריאל פיאלקוף וברוך ויצמן

Outline Introduction - why study biological networks? Three biology network researches - Biological networks are 'scale-free' networks (Barabasi) - Modularity in the yeast protein-protein interaction network (Han) - Evolutionary insights provided by 3-D structures of protein networks (Kim) Conclusion

Outline Introduction - why study biological networks? Three biology network researches - Biological networks are 'scale-free' networks (Barabasi) - Modularity in the yeast protein-protein interaction network (Han) - Evolutionary insights provided by 3-D structures of protein networks (Kim) Conclusion

Outline Introduction - why study biological networks? Three biology network researches - Biological networks are 'scale-free' networks (Barabasi) - Modularity in the yeast protein-protein interaction network (Han) - Evolutionary insights provided by 3-D structures of protein networks (Kim) Conclusion

Network biology: understanding the cell's functional organization (Barabasi AL et al 2004) .

Biological networks – the graph representation Introduction to graphs protein link Just change the title to the title of the previous slide, and put a sub-title “introduction to graphs” as a bullet – this will save you a slide with text only, mentioning things that are much better understood once you see it.

Biological networks – the graph representation Network measures Regular networks (each node connects to a fixed number of nodes) Small world networks Random networks (nodes connect randomly) Average path length (L) Clustering coefficient (C) I would change again the title to the one in the previous slide, and but the current title as bullet. Also, move the text so it does not overlap with the background- this is confusing Random rewiring procedure for interpolating between a regular ring lattice and a random network, without altering the number of vertices or edges in the graph. We start with a ring of n vertices, each connected to its k nearest neighbours by undirected edges. (For clarity, n 20 and k 4 in the schematic examples shown here, but much larger n and k are used in the rest of this Letter.) We choose a vertex and the edge that connects it to its nearest neighbour in a clockwise sense. With probability p, we reconnect this edge to a vertex chosen uniformly at random over the entire ring, with duplicate edges forbidden; otherwise we leave the edge in place. We repeat this process by moving clockwise around the ring, considering each vertex in turn until one lap is completed. Next, we consider the edges that connect vertices to their second-nearest neighbours clockwise. As before, we randomly rewire each of these edges with probability p, and continue this process, circulating around the ring and proceeding outward to more distant neighbours after each lap, until each edge in the original lattice has been considered once. (As there are nk/2 edges in the entire graph, the rewiring process stops after k/2 laps.) Three realizations of this process are shown, for different values of p. For p 0, the original ring is unchanged; as p increases, the graph becomes increasingly disordered until for p 1, all edges are rewired randomly. One of our main results is that for intermediate values of p, the graph is a small-world network: highly clustered like a regular graph, yet with small characteristic path length, like a random graph. Watts & Strogatz, Nature 1998

I suggest to keep the coloring as in the next slide, in particular also for slides 10 and 11 (you can even take the circle above and add it as small item, so they know in the next slides what you are talking about – how to connect what they hear with the previous slides.

Random network Complex networks were thought to be random networks: Be clearer with “were modeled” – mention erdos here Maybe better: “Node degrees follow Poisson distribution centered around <k>” Correct “or” to “are” in the last line Complex networks were thought to be random networks: Node degrees follow Poisson distribution centered around <K>. Nodes with significantly high degree are rare.

Scale free network No characteristic degree of distribution: k follows Power law distribution Most nodes with few links Few nodes with very large number of links – hubs Hubs hold the network together “ * No characteristic degree of distribution: k follows Power law distribution Most nodes with few links Few nodes with many links (hubs) Hubs hold the network together Don’t forget to emphasize the log scale of the axes.

Scale – free properties Small world effect: paths of only three to four reactions can link most pairs of metabolites. Biological significance- quick access.

Scale–free evolution The rich get richer I think you need the “richer gets richer” term already on this slide

Scale-free evolution in biological networks - growth - preferential attachment Earliest nodes have highest degrees (coenzyme A, NAD, GTP) Gene duplication produce identical proteins that interact with the same protein partners I would change the title to reflect that now you talk about biology: Maybe “scale-free evolution in biological networks”?

Scale –free properties – robustness and vulnerability Adaptation and robustness are inherent network properties Robustness is inevitably accompanied by vulnerabilities

Yeast protein interaction network red = lethal green = non-lethal orange = slow growth yellow = unknown Don’t forget here to mention where the red, green dots are expected to be located…

What is the problem with the network model presented so far? No consideration of the timing and structure of the proteins! - Jing Dong Han et al researched protein timing - Philip M Kim et al researched protein structure

Outline Introduction - why study biological networks? Three biology network researches - Biological networks are 'scale-free' networks (Barabasi) - Modularity in the yeast protein-protein interaction network (Han) - Evolutionary insights provided by 3-D structures of protein networks (Kim) Conclusion I suggest to add the figure of date and party (slide 19) to the title slide 22, and to remove 19 and 21. this will improve the flow. In any case you need the figure of current slide 19 just before you talk about correlation…

Han JD et al. (2004). Evidence for dynamically organized modularity in the yeast protein-protein interaction network.

Bimodal distribution of hubs Probability density Black – random Red – hubs Cyan – non-hubs Data points Cell condition 174 Stress response 77 Cell cycle 45 Pheromone treatment 10 Unfolded protein 9 sporulation indicate the names of the subsets (and numbers) as a list Include a header that states something about the “bimodal distribution of hubs” – this is the important point here. Average PCC

Date/Party hubs

Influence of hub removal on network connectivity Removing date hubs Removing party hubs

Network connectivity as function of hub removal (main component disintegration threshold) Red – date Green – random Blue – party Brown - hubs

Network component attributes as result of hub removal This is the first time you talk about FYI – I would replace with “network” so you don’t need to mention explicitly FYI at all

Modularity of biological network

Outline Introduction - why study biological networks? Three biology network researches - Biological networks are 'scale-free' networks (Barabasi) - Modularity in the yeast protein-protein interaction network (Han) - Evolutionary insights provided by 3-D structures of protein networks (Kim) Conclusion

Relating three dimensional structures to protein networks provides evolutionary insights (Philip M Kim et al. Science 2006)

SIN –structural interaction network - Consensus yeast interaction network from various sources - Filtering out low confidence interactions (statistical methodologies) It is helpful to have the text from previous slide here to explain what your aim is.

SIN –structural interaction network Annotations of many edges based on sequence similarity to known complexes

SIN –structural interaction network 3D-structural exclusion to distinguish the interfaces of each interaction

SIN –structural interaction network

SIN –structural interaction network

SIN – final result : 837 nodes (proteins) 1269 edges (interactions) 438 mutually exclusive interactions 147 complexes

Simultaneously possible interactions - properties Likely to share same functions (molecular functions, biological process designations) Expressed at the same time Permanent associations

Simultaneously possible interactions vs Simultaneously possible interactions vs. Mutually exclusive interactions Make sure you correct the table!

Simultaneously possible interactions vs Simultaneously possible interactions vs. Mutually exclusive interactions Make sure you correct the table!

Simultaneously possible interactions vs Simultaneously possible interactions vs. Mutually exclusive interactions Make sure you correct the table!

Simultaneously possible interactions vs Simultaneously possible interactions vs. Mutually exclusive interactions Make sure you correct the table!

Singlish interface hubs vs. multi interface hubs I suggest to cover first the results (i.e. the values in last three columns) with a white rectangle. Talk about it and then show the results

Singlish interface hubs vs. multi interface hubs I suggest to cover first the results (i.e. the values in last three columns) with a white rectangle. Talk about it and then show the results

Singlish interface hubs vs. multi interface hubs I suggest to cover first the results (i.e. the values in last three columns) with a white rectangle. Talk about it and then show the results

Singlish interface hubs vs. multi interface hubs

Singlish interface hubs vs. multi interface hubs

Network evolution by gene duplication

Summary – simultaneous interactions vs. mutually exclusive interactions

Outline Introduction - why study biological networks? Three biology network researches - Biological networks are 'scale-free' networks (Barabasi) - Modularity in the yeast protein-protein interaction network (Han) - Evolutionary insights provided by 3-D structures of protein networks (Kim) Conclusion

Conclusions Network biology helps understanding the cell's functional organization The cell is organized in a modular way. Date hubs connect modules together and party hubs function inside modules Three-dimensional protein structure provides evolutionary insights   Biology network research is anticipated to provide better understanding of the cell functionality as a whole  

Jerusalem – the universal hub "זאת ירושלם בתוך הגוים שמתיה וסביבותיה ארצות" (יחזקאל ה) - "ארץ ישראל באמצעיתו של עולם, וירושלים - באמצע ארץ ישראל" (מדרש תנחומא פרשת קדושים י) - "שררך - זו סנהדרין, ולמה נקרא שמה שררך? שהיא יושבת בטיבורו של עולם" (תלמוד בבלי סנהדרין לז,א) "ירושלים הבנויה כעיר שחוברה לה יחדיו – עיר שהיא עושה כל ישראל חברים" (תלמוד ירושלמי – חגיגה ג, ו) This is actually a very good example for an hierarchical network! You could mention here that there is an additional form of network that you did not talk about…