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Prof. Csermely Péter Semmelweis Egyetem, LINK-csoport

Prof. Csermely Péter Semmelweis Egyetem, LINK-csoport. Nagyméretű, ritka gráfok moduláris, játékelméleti és perturbációdinamikai elemzése biológiai és más valós hálózati problémák megoldásában. www.linkgroup.hu csermely@eok.sote.hu. Advantages of network multi-disciplinarity.

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Prof. Csermely Péter Semmelweis Egyetem, LINK-csoport

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  1. Prof. Csermely PéterSemmelweis Egyetem, LINK-csoport Nagyméretű, ritka gráfok moduláris, játékelméleti és perturbációdinamikai elemzése biológiai és más valós hálózati problémák megoldásában www.linkgroup.hu csermely@eok.sote.hu

  2. Advantages of network multi-disciplinarity • Networks have general properties • small-worldness • hubs (scale-free degree distribution) • nested hierarchy • stabilization by weak links Karinthy, 1929 Watts & Strogatz, 1998 Barabasi & Albert, 1999 Csermely, 2004; 2009 • Generality of network properties offers • judgment of importance • innovation-transfer across different layers of complexity

  3. ecosystem, market, climate • slower recovery from perturbations • increased self-similarity of behavior • increased variance of fluctuation-patterns • Nature 461:53 Example to break conceptual barriers Aging is an early warning signal of a critical transition: death • Prevention: central elements • with less predictable behavior • omnivores, top-predators • market gurus • stem cells Farkas et al, Science Signaling 4:pt3

  4. Less predictable (creative) elementshave a large dynamic centrality Creative:few links to hubs, unexpected re-routing, flexible, unpredictable Distributor:hub, specialized to signal distribution, predictable Problem solver:specialized to a task, predictable change of roles Csermely, Nature 454:5 TiBS 33:569 TiBS 35:539

  5. Creative elements are central and… Cyt-P450 (CYP2B4) • Creative amino acids • centre of residue-network • in structural holes • Creative proteins • stress proteins • signaling switches • Creative cells • stem cells • our brain oxidation drug-binding Csermely, Nature 454:5 TiBS 33:569 TiBS 35:539 • Creative persons • firms • societies mobile

  6. Creative elements of social networks Ron Burt, Structural holes, Harvard Univ. Press 1992 Robert: broker – netokrat James: looser

  7. 3 examples of network modelling • topological centrality is central in network perturbations • (Turbine: www.linkgroup.hu/Turbine.php) • community centrality: prediction of survival importance • (ModuLand: www.linkgroup.hu/modules.php) • game-centrality: prediction of biological regulators • (NetworGame: www.linkgroup.hu/NetworGame.php)

  8. Turbine algorithm assessing network perturbation dynamics any real networks can be added, modified normalizes the input network any perturbation types (multiple, repeated, etc.) any models of dissipation, teaching and aging Matlab compatible www.linkgroup.hu/Turbine.php Farkas et al, Science Signaling 4:pt3

  9. Hubs + inter-modular nodes are primary transmitters of network perturbations Start: module-center hub Starting node: bridge www.linkgroup.hu/Turbine.php Farkas et al., Science Signaling 4:pt3

  10. 3 examples of dynamic centrality • topological centrality is central in network perturbations • (Turbine: www.linkgroup.hu/Turbine.php) • community centrality: prediction of survival importance • (ModuLand: www.linkgroup.hu/modules.php) • game-centrality: prediction of biological regulators • (NetworGame: www.linkgroup.hu/NetworGame.php)

  11. Importance of modular overlapsof protein-protein interaction networks in stress • networks: • STRING 7 • (5329/190018) • BioGRID • (5329/91749) • Ekman • (2444/6271) stressed protein weight resting protein weight mRNA expression multiplication multiplication continuous weights discrete weights average average resting link-weight stressed link-weight unique stress average stress • weights: • equal units • proteins • (Nature 441:840) • mRNA-s • (Cell 95:717) 13 stress conditions, 65 experiments (Gasch et al, Mol. Biol. Cell 11:4241) 6 stress conditions, 32 experiments (Causton et al, Mol. Biol. Cell 12:323) analysis of overlapping network modules:ModuLand method Mihalik & Csermely PLoS Comput. Biol. 7:e1002187

  12. ModuLand method family detectsovelapping network communities community heaps of all nodes/links community landscape communities as landscape hills network hierachy Kovacs et al, PLoS ONE 5:e12528 www.linkgroup.hu/modules.php network of network scientists; Newman PRE 74:036104

  13. Community heaps: NodeLand method starting node influence zones community-44: 1127 schoolchildren, 5096 friendships; Add-Health

  14. ModuLand method family detectsovelapping network communities community landscape community centrality: a measure of the influence of all other nodes community heaps of all nodes/links communities as landscape hills network hierachy available as a Cytoscape plug-in Kovacs et al, PLoS ONE 5:e12528 www.linkgroup.hu/modules.php network of network scientists; Newman PRE 74:036104

  15. Changing community centralitiesreflect the importance of survival in stress energy distribution protein synthesis • yeast protein-protein interaction • network: 5223 nodes, 44314 links • stress: 15 min 37°C heat shock • link-weight changes: mRNA • expression level changes protein degradation energy distribution survival processes Mihalik & Csermely PLoS Comput. Biol. 7:e1002187

  16. Changes of yeast interactome in crisis:a model of systems level adaptation • BioGrid yeast interactome: • 5223 nodes, 44314 links • stress: 15 min 37°C heat shock • + Gasch et al. MBC 11:4241 • link-weight changes: mRNA • expression level changes • Stressed yeast cell: • nodes belong to less modules • modules have less intensive contacts • smaller overlaps between modules Mihalik & Csermely PLoS Comput. Biol. 7:e1002187

  17. 2. Topological phase transitions reflect the overlap-decrease at one level higher network diameter degrees of freedom stress apoptosis assembly > disassembly disassembly > assembly Derenyi et al. Physica A 334:583 Csermely: Weak Links (Springer 2009) • 1. Stress-induced overlap decrease is general • proteins: water, stretch • brain: disease states, such as Alzheimer • animal compementary coop.: alpha-male • social dimensions: dissociate in stress, firms • ecosystems: patch separation in desiccation

  18. cell death network desintegration creative elements* • increased network flexibility • spared links • noise and damage localization • modular independence:larger • response-space and better conflict • management Crisis survival: creative elements Consequences of network crisis stress *Schumpeterian creative destruction Szalay et al, FEBS Lett. 581:3675; Palotai et al. IUBMB Life 60:10 Mihalik & Csermely. PLoS Comput. Biol. 7:e1002187

  19. Bridges of Met-tRNA-synthase network identify signaling amino acids Ghosh et al. PNAS 104:15711 signaling amino acids other amino acids Szalay-Bekő et al. in preparation

  20. Overlaps of signaling pathways are most pronounced in humans growing prevalence of signaling cross-talks Korcsmaros et al, Bioinformatics 26:2042; PLoS ONE 8:e19240 www.SignaLink.org – 2010 version: 646 proteins, 991 links in humans

  21. 3 examples of dynamic centrality • topological centrality is central in network perturbations • (Turbine: www.linkgroup.hu/Turbine.php) • community centrality: prediction of survival importance • (ModuLand: www.linkgroup.hu/modules.php) • game-centrality: prediction of biological regulators • (NetworGame: www.linkgroup.hu/NetworGame.php)

  22. NetworGame program • any model or real networks can be added (weighted, directed) • any game types (prisoners’ dilemma, hawk-dove, stag hunt, etc.) • any strategy update rules • synchronized, asynchron, semi-synchron update • starting strategies can be set individually by elements Farkas et al., Science Signaling 4:pt3 www.linkgroup.hu/NetworGame.php

  23. PD-game Q-learning replacing 37 links from 900 18% cooperators 18% cooperators Q-learning helps cooperation small world network regular network PD-game best-takes over replacing 37 links from 900 3.5% cooperators 18% cooperators Wang et al. PLoS ONE 3:e1917

  24. Q-learning 1. helps cooperation even at high temptation levels 2. stabilizes cooperation at different network topologies 2 1 • SAME with • other short term strategy update rules • other network topologies • other games (extended-PD or Hawk-Dove) Wang, Szalay, Zhang & Csermely PLoS ONE 3:e1917

  25. Long-term learning 1. helps 2. BUT does not stabilize cooperation 1 2 • SAME with • other short term strategy update rules • other network topologies • other games (extended-PD or Hawk-Dove) Wang, Szalay, Zhang & Csermely PLoS ONE 3:e1917

  26. Long-term learning + innovation 1. helps cooperation even at high temptation levels 2. stabilizes cooperation at different network topologies 1 2 • SAME with • other short term strategy update rules • other network topologies • other games (extended-PD or Hawk-Dove) Wang, Szalay, Zhang & Csermely PLoS ONE 3:e1917

  27. Learning + innovation expand cooperative network topologies learning + innovation Q-learning learning imitation (best-takes-over) we are do not depend (that much…) on the network around us • SAME with • other short term strategy update rules • other network topologies • other games (extended-PD or hawk-dove) Wang, Szalay, Zhang & Csermely PLoS ONE 3:e1917

  28. Game-centrality: prediction of key nodes of social regulation I. hispanic old union leaders: strike sociogram leaders: work BC BC BC Hawk-dove game (PD game: same) Start: all-cooperation = strike Strike-breaker: defects BC-s are the best strike-breakers young Farkas et al., Science Signaling 4:pt3 www.linkgroup.hu/NetworGame.php Michael’s strike network; Michael, Forest Prod. J. 47:41

  29. BC BC instructor BC administrator BC prisoners’ dilemma/stag hunt games most influential single defective elements largest betweenness centrality BC Game-centrality: prediction of key nodes of social regulation II. doves hawks hawk-dove game: 50-50% random starting D/C recovers network modules Farkas et al., Science Signaling 4:pt3 www.linkgroup.hu/NetworGame.php Zachary karate club network; J.Anthropol. Res. 33:452

  30. Game-centrality: prediction of key nodes of biological regulation • Met-tRNA-synthase protein structure network • signaling amino acids (Ghosh, PNAS 104:15711) • largest game-centrality • yeast protein-protein interaction network • amino acids regulating evolution (Levy, PLoS • Biol 6:e264): large game centrality Farkas et al., Science Signaling 4:pt3 www.linkgroup.hu/NetworGame.php

  31. A könyvek innen tölthetőek le: Bemutatkozás www.csermelyblog.hu

  32. maybe You as a collaborator Acknowledgments Ágoston Mihalik Aging Stress Eszter Hazai Shijun Wang Gábor I. Simkó Turbine: perturbation NetworGame István A. Kovács Kristóf Z. Szalay ModuLand www.linkgroup.huinfo@linkgroup.hu Robin Palotai Máté Szalay-Bekő half from the BME…

  33. Turbine algorithm: perturbation model dissipation of perturbations ‘learning’ – ‘aging’: changes of link weights attenuation of other links, if a link gains weight Es, free energy of starting node; Ee, free energy of the other node on the link; l, degree; w, link weight; D0, dissipation constant; Cs, amplification constant; A, aging sensitivity; Cw, attenuation constant if perturbation exceeds a limit: links are exchanged to half as many random links Farkas et al., Science Signaling 4:pt3 www.linkgroup.hu/Turbine.php

  34. Game rules • canonical Prisoner’s Dilemma game: CC:R(3); CD:S/T(0/3-6); DD:P(1) • extended PD game: CC:R(1); CD:S/T(0/1-2); DD:P(0) • Hawk-Dove game: CC:G/2 (C>G); CD:G; DD:-(G-C)/2 • 2,500 agents are tied to network nodes (no evolution) • agents can play with their neighbors only • they can either defect or cooperate • 50-50% defectors and cooperators start • at a random distribution • 5,000 plays, % of cooperators on last 10 rounds • average of 100 games is displayed Wang, Szalay, Zhang and Csermely PLoS ONE 3:e1917

  35. Strategy update rules • short-term strategy update: best takes over(imitation, copy the richest neighbor) • replicator dynamics (pair-wise comparision dynamics; copy randomly selected • neighbor, if richer); proportional updating (spread to all neighbors, • if richer) • long-term learning, innovative strategy update: Q-learning • (a type of reinforcement learning • optimal strategy to maximize total discounted expected reward; • annealing temp 100, discount factor 0.5) • long-term learning:accumulated payoff not only last round • innovation:opposite of agent’s strategy with p (0.0001) probability • synchronized update, averaged payoffs Wang, Szalay, Zhang and Csermely PLoS ONE 3:e1917

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