Download Rigidity - SciLifeLab

Network biology in cancer
Prof.
www.linkgroup.hu
[email protected]
Peter Csermely
LINK-Group,
Semmelweis University,
Budapest, Hungary
Traditional view
cause
effect
(Paul Ehrlich’s magic bullet)
Recently changed view
100 causes
100 effects
Networks may help!
major causes
major effects
Advantages of the network approach
Networks have general properties
• small-worldness
• hubs (scale-free degree distribution)
• nested hierarchy
• stabilization by weak links
Karinthy, Watts & Strogatz,
1929
1998
Barabasi & Albert, 1999
Csermely, 2004; 2009
Generality of network properties offers
• judgment of importance
• innovation-transfer across different layers of complexity
Influential nodes in different systems:
example to break conceptual barriers
Aging is an early warning signal
of a critical transition: death
ecosystem, market, climate
• slower recovery from perturbations
• increased self-similarity of behaviour
• increased variance of fluctuation-patterns
Nature 461:53
Prevention: nodes with less
predictable behaviour
• omnivores, top-predators
• market gurus
• stem cells
Farkas et al., Science Signaling 4:pt3
Adaptation of complex systems
homeostasis
stress
Norbert
Wiener
Conrad Waddington
homeorhesis
Ludwig
von Bertalanffy
cybernetics
A possible adaptation mechanism
Plasticity
Rigidity
Plasticity-rigidity cycles
form a general
adaptation mechanism.
Plasticity and rigidity:
two key, but ill-defined concepts
stability
complexity
robustness
emergent property
degeneracy
Plasticity
Rigidity
[functional &
structural]
[functional &
structural]
learning
memory
evolution
evolvability
canalization
scientific
revolution
exploration (diversify)
creativity
exploitation (focus)
aging
Plasticity and rigidity:
two key, but ill-defined concepts
~100
~100
years
years?
structural rigidity:
Maxwell, 1864
2-dimension proof:
Laman, 1970
Nature Rev. Genet. 5, 826
plasticity ??? flexibility
3-dimension proof:
XXX, 2070?
Definition of functional
plasticity and rigidity
large number
of responses
small number
of responses
Functional plasticity and rigidity
and system stability
plastic systems:
smooth state space
simple
systems
complex
systems
rigid systems:
rough state space
small – large
Lyapunov stability
small ← large → small
structural stability
local
minimum
rigid  plastic 
rigid transition
smooth perturbation
(not necessarily small)
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
Properties of plastic and rigid systems
extremely structurally extremely
plastic stable, robust
rigid
+
dissipation
memory
competent
(exploitation)
+
+
possibility of adaptation
+
signaling
learning
competent
(exploration)
effect of adaptation
Gáspár & Csermely, Brief. Funct. Genom. 11:443
Gyurkó et al. Curr. Prot. Pept. Sci. 15:171
Example 1: Molecular mechanisms
of protein structure optimization
Hsp60 chaperone
unfolded
substrate
(plastic)
folded
substrate
(rigid)
chaperone
cycle
substrate
release
(plastic)
substrate
expansion
(rigid)
extended
peptide
bonds
Hsp70
chaperone
Hsp60: iterative annealing:
pull/release of folding protein
Hsp70: push/release of
extended peptide bonds
Todd et al, PNAS 93:4030
Csermely BioEssays 21:959
Lin & Rye, Mol. Cell 16:23
Bukau & Horwich, Cell 92:351
Example 2: cell differentiation
cancer attractors
progenitor
Sui
Huang
differentiated cells
Ingemar
Ernberg
Stuart
Kauffman
Huang, Ernberg, Kauffman,
Semin. Cell Developm. Biol. 20:869
Example 3:
cell differentiation
progenitor
cells
more rigid
rigid
plastic
Rajapakse et al., PNAS 108:17257
gene expression correlation networks
chromatin networks
differentiated
cells
Example 4:
disease progression
Scientific
Reports
2:342; 813
rigid
plastic
rigid
phosgene inhalation-induced lung injury,
chronic hepatitis B/C, liver cancer
Example 5:
cancer stem cells
Csermely et al., Seminars in Cancer Biology
doi: 10.1016/j.semcancer.2013.12.004
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
Network-dependent mechanisms
of plasticity-rigidy cycles
soft spots
creative nodes, prions (Q/Nrich proteins), chaperones
• extended, fuzzy core
• fuzzy modules
• no hierarchy
• source-dominated
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
complexity
star network
random
graph
scale-free
network
subgraphs
stress
Topological
phase transitions:
plastic  rigid
networks with
diminished resources
resources
Derényi et al., Physica A 334:583
edge-length
contributes
to its cost
Brede, PRE 81:066104
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
Drug design strategies
for plastic and rigid cells
e.g.: antibiotics
Csermely et al, Pharmacol & Therap 138: 333-408
e.g.: rapamycin
Central hit + network-influence: cancer
cancer
stem
cells
Gyurkó et al, Seminars in
Cancer Biology 23:262-269
most test systems
are in this stage
most patients
are in this stage
network entropy
low
high
János
Hódsági,
MSc thesis
Network entropy increases than
decreases in cancer propagation
plastic
adenoma
rigid
colon
carcinoma
János Hódsági
MSc thesis
network entropy
of cancer stem cells
is larger than that of
their parental cells
Drug design strategies
for plastic cells
e.g.: antibiotics
Csermely et al, Pharmacol & Therap 138: 333-408
e.g.: rapamycin
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
Bridges are key nodes
of social regulation
hispanic
old
union leaders: strike
BC
BC
sociogram leaders: work
BC
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
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
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
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
network
hierachy
Szalay-Bekő et al.
Bioinformatics
28:2202
extensive
overlaps +
available as
Cytoscape
plug-in
communities
as landscape hills
Kovacs et al, PLoS ONE 5:e12528
www.modules.linkgroup.hu
network of network scientists; Newman PRE 74:036104
centre of
modules +
bridges
Drug design strategies
for rigid cells
e.g.: antibiotics
Csermely et al, Pharmacol & Therap 138: 333-408
e.g.: rapamycin
Network-influence: Allo-network drugs
hit of intracellular paths
Examples: BRAF inhibition
restoring MEK inhibition
• rapamycin effects on
mTOR complexes
Nussinov et al,
Trends Pharmacol
Sci 32:686
• atomic resolution interactome
of allosteric protein complexes
• identification of allosteric paths
Network influence: Multi-target drugs
Csermely et al, Trends Pharmacol Sci 26:178
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
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
Attractors of T-LGL network
using Turbine::Attractor
apoptosis
proliferation
Multi-drug design with Turbine::Designer
T-LGL survival signaling network: leukemia specific edges
Starting state: IL7-activation; target-state: all black
Turbine::Designer solution to reach target state
Phospholipase Cϒ1
(inhibition; Cancer Res. 68:10187)
apoptosis
starting state
Interferon α1
(activation; CA Cancer J Clin 38:258)
Inactive protein
Activated protein
Network:
CD45
(activation; Blood 119:4446)
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.
Take-home messages
1.
When you build up your network
(or use other’s networks) be EXTREMELY
cautious how you define your nodes and edges
2.
Plasticity-rigidity cycles
form a general adaptation
mechanism
3.
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
Acknowledgment: the LINKGroup + the associated talent-pool
India
Sevilla
Nashville
St. Paul
San Francisco
South Africa
Zürich
Sanghai
Bethesda
Hong Kong
A core of 8 people + a multidisciplinary group of
+34 people with a background of +100 members
and a HU/EU-talent support network