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The Power of Networks in Cancer Biology

Join Prof. Peter Csermely and the LINK-Group at Semmelweis University in Budapest, Hungary as they explore the role of network biology in understanding cancer. Discover how the network approach provides new insights into the causes and effects of cancer, and learn about the advantages of this approach in predicting importance and transferring innovation across different layers of complexity. Dive into topics like plasticity and rigidity, adaptation mechanisms, influential nodes, and more. Don't miss this opportunity to explore the fascinating world of network biology in cancer research.

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The Power of Networks in Cancer Biology

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  1. Network biology in cancer Prof. Peter Csermely LINK-Group,Semmelweis University,Budapest, Hungary www.linkgroup.hu csermelynet@gmail.com

  2. Traditional view cause effect (Paul Ehrlich’s magic bullet)

  3. Recently changed view 100 causes 100 effects

  4. Networks may help! major causes major effects

  5. Advantages of the network approach • 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

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

  7. Adaptation of complex systems homeostasis stress Norbert Wiener Ludwig von Bertalanffy Conrad Waddington cybernetics homeorhesis

  8. Plasticity Rigidity Plasticity-rigidity cycles form a general adaptation mechanism. A possible adaptation mechanism

  9. Plasticity and rigidity: two key, but ill-definedconcepts stability stability complexity complexity robustness robustness emergent property emergent property degeneracy Plasticity [functional & structural] Rigidity [functional & structural] learning learning memory memory evolution evolution evolvability evolvability canalization scientific revolution exploitation(focus) exploration(diversify) aging creativity creativity aging

  10. ~100 years ~100 years? structural rigidity: Maxwell, 1864 2-dimension proof: Laman, 1970 3-dimension proof: XXX, 2070? Nature Rev. Genet. 5, 826 plasticity ??? flexibility Plasticity and rigidity: two key, but ill-defined concepts

  11. Definition of functional plasticity and rigidity large number of responses small number of responses

  12. small – large Lyapunov stability small – large Lyapunov stability small – large Lyapunov stability small ← large → small structural stability complex systems local minimum rigid  plastic  rigid transition Functional plasticity and rigidity and system stability plastic systems: smooth state space rigid systems: rough state space simple systems smooth perturbation (not necessarily small)

  13. Plasticity-rigidity cycles form a general adaptation mechanism Plasticity Rigidity alternating changes of plasticity- and rigidity-dominance allow the recalibration of the system to find the maximal structural stability in a changed environment

  14. signaling dissipation memory competent (exploitation) learning competent (exploration) Properties of plastic and rigid systems extremely plastic structurally stable, robust extremely rigid + + possibility of adaptation + + effect of adaptation Gáspár & Csermely, Brief. Funct. Genom. 11:443 Gyurkó et al. Curr. Prot. Pept. Sci. 15:171

  15. extended peptide bonds Hsp70 chaperone Hsp70: push/release of extended peptide bonds Bukau & Horwich, Cell 92:351 Example 1: Molecular mechanismsof protein structure optimization Hsp60 chaperone unfolded substrate (plastic) folded substrate (rigid) chaperone cycle substrate expansion (rigid) substrate release (plastic) Hsp60: iterative annealing: pull/release of folding protein Todd et al, PNAS 93:4030 Csermely BioEssays 21:959 Lin & Rye, Mol. Cell 16:23

  16. Example 2: cell differentiation cancer attractors progenitor Sui Huang Ingemar Ernberg differentiated cells Stuart Kauffman Huang, Ernberg, Kauffman, Semin. Cell Developm. Biol. 20:869

  17. Example 3: cell differentiation more rigid differentiated cells rigid progenitor cells plastic Rajapakse et al., PNAS 108:17257 gene expression correlation networks chromatin networks

  18. rigid plastic rigid Example 4: disease progression Scientific Reports 2:342; 813 phosgene inhalation-induced lung injury, chronic hepatitis B/C, liver cancer

  19. Example 5: cancer stem cells Csermely et al., Seminars in Cancer Biology doi: 10.1016/j.semcancer.2013.12.004

  20. Socialism: shortage economy  rigid Capitalism: surplus economy  plastic Network-independent mechanisms of plasticity-rigidy cycles 1. noise: reaching hidden attractors coloured noise, node-plasticity 2. medium-effects: water, chaperones membrane-fluidity, volume transmission as neuromodulation, money

  21. Network-dependent mechanisms of plasticity-rigidy cycles soft spots creative nodes, prions (Q/N-rich proteins), chaperones rigidity seeds rigidity promoting nodes • extended, fuzzy core • fuzzy modules • no hierarchy • source-dominated • small, dense core • disjunct, dense modules • strong hierarchy • sink-dominated Csermely et al., Seminars in Cancer Biology doi: 10.1016/j.semcancer.2013.12.004

  22. edge-lengthcontributes to its cost Brede, PRE 81:066104 Topologicalphase transitions:plastic  rigid networks with diminished resources star network scale-free network random graph complexity subgraphs stress resources Derényi et al., Physica A 334:583

  23. Yeast stress induces module condensation of the interactome • Stressed yeast cell: • nodes belong to less modules • modules have less contacts • more condensed modules = • = more separated modules • yeast protein-protein interaction • network: 5223 nodes, 44314 links • + several other conditions • stress: 15 min 37°C heat shock • + other 4 stresses • link-weight changes: mRNA • expression level changes Mihalik & Csermely PLoS Comput. Biol. 7:e1002187

  24. Drug design strategiesfor plastic and rigid cells e.g.: antibiotics e.g.: rapamycin Csermely et al, Pharmacol & Therap 138: 333-408

  25. Central hit + network-influence: cancer cancer stem cells most patients are in this stage most test systems are in this stage Gyurkó et al, Seminars in Cancer Biology 23:262-269

  26. network entropy low high János Hódsági, MSc thesis

  27. network entropy of cancer stem cells is larger than that of their parental cells Network entropy increases than decreases in cancer propagation plastic adenoma colon rigid carcinoma János Hódsági MSc thesis

  28. Drug design strategiesfor plastic cells e.g.: antibiotics e.g.: rapamycin Csermely et al, Pharmacol & Therap 138: 333-408

  29. 3 novel network centralities reveal influential nodes • perturbation centrality • (www.Turbine.linkgroup.hu) • community centrality • (www.modules.linkgroup.hu) • game centrality • (www.NetworGame.linkgroup.hu) PLoS ONE 5:e12528 Bioinformatics 28:2202 Science Signaling 4:pt3 PLoS ONE 8:e67159 PLoS ONE 8:e78059

  30. prediction of key amino acids in allosteric signaling Bridges are key nodes of social regulation 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; Simko & Csermely: PLoS ONE 8: e67159 www.linkgroup.hu/NetworGame.php Michael’s strike network; Michael, Forest Prod. J. 47:41

  31. 3 novel network centralities reveal influential nodes • perturbation centrality • (www.Turbine.linkgroup.hu) • community centrality • (www.modules.linkgroup.hu) • game centrality • (www.NetworGame.linkgroup.hu) PLoS ONE 5:e12528 Bioinformatics 28:2202 Science Signaling 4:pt3 PLoS ONE 8:e67159 PLoS ONE 8:e78059

  32. ModuLand method family: module centres & bridges community landscape influence zones of all nodes/links community centrality: a measure of the influence of all other nodes communities as landscape hills network hierachy Szalay-Bekő et al. Bioinformatics 28:2202 extensive overlaps + centre of modules +bridges available asCytoscape plug-in Kovacs et al, PLoS ONE 5:e12528 www.modules.linkgroup.hu network of network scientists; Newman PRE 74:036104

  33. Drug design strategiesfor rigid cells e.g.: antibiotics e.g.: rapamycin Csermely et al, Pharmacol & Therap 138: 333-408

  34. Network-influence: Allo-network drugs hit of intra- cellular paths • Examples: BRAF inhibition • restoring MEK inhibition • rapamycin effects on • mTOR complexes • atomic resolution interactome • of allosteric protein complexes • identification of allosteric paths Nussinov et al, Trends Pharmacol Sci 32:686

  35. Network influence: Multi-target drugs Csermely et al, Trends Pharmacol Sci 26:178

  36. 3 novel network centralities reveal influential nodes • perturbation centrality • (www.Turbine.linkgroup.hu • community centrality • (www.modules.linkgroup.hu) • game centrality • (www.NetworGame.linkgroup.hu PLoS ONE 5:e12528 Bioinformatics 28:2202 Science Signaling 4:pt3 PLoS ONE 8:e67159 PLoS ONE 8:e78059

  37. Turbine: general network dynamics tool any real networks can be added, modified normalizes the input network any perturbation types (communicating vessel model, multiple, repeated, etc.) any models of dissipation, teaching and aging Matlab compatible www.Turbine.linkgroup.hu Szalay & Csermely, Science Signaling 4:pt3 PLoS ONE 8:e78059

  38. Attractors of T-LGL network usingTurbine::Attractor apoptosis proliferation

  39. Multi-drug design with Turbine::Designer Phospholipase Cϒ1 (inhibition; Cancer Res. 68:10187) Interferon α1 (activation; CA Cancer J Clin 38:258) CD45 (activation; Blood 119:4446) T-LGL survival signaling network: leukemia specificedges Starting state: IL7-activation; target-state: all black Turbine::Designer solution to reach target state apoptosis starting state Inactive protein Inactive protein Activated protein Activated protein Network: Zhang R, Shah MV, Yang J, Nyland SB, Liu X, Yun JK, Albert R, Loughran TP Jr. (2008) Network model of survival signaling in large granular lymphocyte leukemia. PNAS 105: 16308–13.

  40. When you build up your network (or use other’s networks) be EXTREMELY cautious how you define your nodes and edges 1. Influential nodes of plastic networks are their central nodes; influential nodes of rigid networks are their neighbours and can be efficiently predicted by network topology and dynamics methods Plasticity-rigidity cycles form a general adaptation mechanism 2. 3. Take-home messages

  41. India Sevilla Nashville St. Paul San Francisco South Africa Hong Kong Zürich Sanghai Bethesda Acknowledgment: the LINK-Group + the associated talent-pool A core of 8 people + a multidisciplinary group of +34 people with a background of +100 members and a HU/EU-talent support network

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