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Prof. Peter Csermely LINK-Group, Semmelweis University, Budapest, Hungary Network biology in cancer

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Presentation on theme: "Prof. Peter Csermely LINK-Group, Semmelweis University, Budapest, Hungary Network biology in cancer"— Presentation transcript:

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

2 Traditional view causeeffect (Paul Ehrlich’s magic bullet)

3 Recently changed view 100 causes100 effects

4 Networks may help! major causes major effects

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

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

7 Adaptation of complex systems homeostasisstress homeorhesis cybernetics Conrad Waddington Norbert Wiener Ludwig von Bertalanffy

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

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

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

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

12 plastic systems: smooth state space rigid systems: rough state space Functional plasticity and rigidity and system stability rigid  plastic  rigid transition local minimum small – large Lyapunov stability small – large Lyapunov stability small – large Lyapunov stability simple systems small ← large → small structural stability complex 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 Properties of plastic and rigid systems extremely plastic structurally stable, robust extremely rigid effect of adaptation possibility of adaptation memory competent (exploitation) learning competent (exploration) Gáspár & Csermely, Brief. Funct. Genom. 11:443 Gyurkó et al. Curr. Prot. Pept. Sci. 15:171 ++ + + dissipation signaling

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

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

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

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

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

20 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 Socialism: shortage economy  rigid Capitalism: surplus economy  plastic

21 Network-dependent mechanisms of plasticity-rigidy cycles extended, fuzzy core fuzzy modules no hierarchy source-dominated soft spots creative nodes, prions (Q/N- rich proteins), chaperones rigidity seeds rigidity promoting nodes 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 Topological phase transitions: plastic  rigid networks with diminished resources resources stress complexity scale-free network subgraphs star network Derényi et al., Physica A 334:583 Brede, PRE 81:066104 edge-length contributes to its cost random graph

23 Mihalik & Csermely PLoS Comput. Biol. 7:e1002187 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

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

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

26 János Hódsági, MSc thesis network entropy low high

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

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

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

30 Bridges are key nodes of social regulation hispanic old young BC union leaders: strike sociogram leaders: work 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 Hawk-dove game (PD game: same) Start: all-cooperation = strike Strike-breaker: defects BC-s are the best strike-breakers prediction of key amino acids in allosteric signaling

31 PLoS ONE 5:e12528 Bioinformatics 28:2202 Science Signaling 4:pt3 PLoS ONE 8:e67159 PLoS ONE 8:e78059 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)

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

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

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

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

36 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 3 novel network centralities reveal influential nodes

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 using Turbine::Attractor proliferation apoptosis

39 Multi-drug design with Turbine::Designer 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. Inactive protein Activated protein Phospholipase Cϒ1 (inhibition; Cancer Res. 68:10187) CD45 (activation; Blood 119:4446) T-LGL survival signaling network: leukemia specific edges Starting state: IL7-activation; target-state: all black Turbine::Designer solution to reach target state starting state apoptosis Inactive protein Activated protein Interferon α1 (activation; CA Cancer J Clin 38:258)

40 Take-home messages 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 3. When you build up your network (or use other’s networks) be EXTREMELY cautious how you define your nodes and edges 1. 2. Plasticity-rigidity cycles form a general adaptation mechanism

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


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