Transcript Slide 1

Literature
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Barabasi AL, Oltvai ZN: Network biology: understanding the cell's functional
organization. Nat Rev Genet 2004, 5:101-113.
Sharan R, Ideker T: Modeling cellular machinery through biological network
comparison. Nat Biotechnol 2006, 24:427-433.
Gimona M: Protein linguistics - a grammar for modular protein assembly? Nat Rev
Mol Cell Biol 2006, 7:68-73.
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into insulin action. Nat Rev Mol Cell Biol 2006, 7:85-96.
•essential
•allows divergence
•facilitate crosstalk
•fine tune the response stimuli
NATURE REVIEWS |
MOLECULAR CELL BIOLOGY
VOLUME 7 | FEBRUARY 2006 |
Identification of components, or nodes, within the network
Quantitative approaches
Integrate biochemical and computational data to identify essential
nodes in signalling networks, as well as to describe how these nodes
might interact with other signalling cascades.
Genetic approach
Using data that has been derived from in vitro and
in vivo genetic knockout studies
Identification of `critical nodes by three criteria
1. The node must constitute a group of related proteins (for example, gene
isoforms) that are essential for the receptor-mediated signal, and in which two
or more of these related proteins might have unique biological roles within a
signalling network and therefore serve as a source of divergence within the
signalling system
2. The node is highly regulated, both positively and negatively
3. The node is a junction for potential crosstalk with other signalling
systems.
INSULIN SIGNALLING NETWORK
1
1
2
3
~1000 combinatorial
possibilities
Signalling in a nutshell
Cells respond to external cues using a limited number of signalling pathways that are
activated by plasma membrane receptors, such as G protein-coupled receptors
(GPCRs) and receptor tyrosine kinases (RTKs).
These pathways do not simply transmit, but they also process, encode and integrate
internal and external signals.
Recently, it has become apparent that distinct spatio-temporal activation profiles of the
same repertoire of signalling proteins result in different gene-expression patterns and
diverse physiological responses.
For any individual receptor pathway, there is no single protein or gene that is responsible
for signalling specificity. Instead, specificity is determined by the temporal and spatial
dynamics of downstream signalling components.
Upon stimulation, RTKs undergo dimerization (for example, the epidermal growth factor
receptor (EGFR)) or allosteric transitions (insulin receptor) that result in the activation of
the intrinsic tyrosine-kinase activity.
Subsequent phosphorylation of multiple tyrosine residues on the receptor transmits a
biochemical signal to numerous cytoplasmic proteins, thereby triggering their mobilization
to the cell surface.
The resulting cellular responses occur through complex biochemical circuits of
protein–protein interactions and covalent-modification cascades.
Intracellular components
The human genome contains
~25,000 genes
1014 cells that are comprised of more than
200 different cell types
The human cellular signalling network
includes genes for 1,543 signalling
receptors
Modifications
Alternative splicing: Assuming an
average of 2.5 splice variants per gene
across the entire genome.
Post-translational modifications (PTMs)
on average, at least 2.5 modifications per
protein
518 protein kinases and
~150 protein phosphatases.
Proteins are also subject to proteolytic
events that further regulate their activity.
Activation (or inhibition) of transcription
factors (of which there are estimated to be
more than 1,850 in the human genome
Three independent PTMs would correspond
to eight distinct states of a given protein
(each of the 3 PTMs could be present or
absent, so 23 = 8)
Links and connectivity.
Interactions between the network
components allow for an even
greater degree of combinatorial control.
Initial estimates of the number of
interactions in the yeast proteome
indicated that there are an average of five
interacting partners per protein
IF 1% of the total receptors (15 receptor
proteins) can be independently expressed
in any given cell type, then the cell could
potentially respond to 32,768 different
ligand combinations.
For example, in metabolic networks,
the addition of a single reaction to a
network could increase the number of
functional pathways by several-fold.
Reconstructions of highly connected ‘NODES’in
networks.
Such reconstructions involve comprehensively listing the
compounds and reactions that are associated with a given
protein, ion or metabolite.
Identifying SIGNALLING ‘MODULES’.
Such modules historically consist of groups of compounds and
proteins that function together under certain conditions on the
basis of phenomenological reasoning
Forming linear ‘pathways’ that connect signalling
inputs to signalling outputs.
Data collection for the network reconstruction process.
Universal motifs of cell-signalling networks.
One-site phosphorylation cycle.
A cascade of cycles.
A cycle of a small GTPase (Ran).
Negative feedback provides
robustness to noise, increases
resistance to disturbances
inside the feedback loop, but
causes oscillations if it is too
strong and the cascade is
ultrasensitive.
Positive feedback greatly increases the sensitivity
of the target to the signal and might also lead to
bistability and relaxation oscillations.
Protein linguistics — a grammar for modular protein assembly?
Linguistic concepts to define a basic set of grammatical rules for genes, based on the
idea that mutating a piece of genetic information was similar to modifying words. If the
mutation is recognized by an existing automaton, then the structure is preserved, in spite
of the mutation.
Otherwise, this new structure needs to be recognized by a new automaton or it remains
meaningless (a semantic null).
A key theoretical principle for understanding an unknown language is the recognition
of syntactic patterns. For proteins, these patterns might be similarities in sequence, or
structure, or both.
NATURE REVIEWS | MOLECULAR CELL BIOLOGY
VOLUME 7 | JANUARY 2006 | 69
From the particular to the universal.
The bottom of the pyramid shows the
traditional representation of the cell’s
functional organization: genome,
transcriptome, proteome, and
metabolome (level 1). There is
remarkable integration of the
various layers both at the
regulatory and the structural
level. Insights into the logic
of cellular organization
can be achieved when
we view the cell as a
complex network in
which the components are connected by functional
links. At the lowest
level, these components form
genetic-regulatory
motifs or metabolic
pathways (level 2), which in turn are
the building blocks of functional
modules (level 3). These modules are nested, generating
a scale-free hierarchical
architecture (level 4).
Although the individual
components are unique
to a given organism,
the topologic properties of cellular networks share surprising similarities
with those of
natural and
social
network
and the
World
Wide
Web.
Although molecular biology offers many spectacular successes, it is clear that the
detailed inventory of genes, proteins, and metabolites is not sufficient to understand the
cell’s complexity.
According to the basic dogma of molecular biology, DNA is the ultimate depository of
biological complexity. Indeed, it is generally accepted that information storage,
information processing, and the execution of various cellular programs reside
in distinct levels of organization: the cell’s genome, transcriptome, proteome, and
metabolome.
These elementary building blocks organize themselves into small recurrent patterns,
called pathways in metabolism and motifs in genetic regulatory networks. In turn,
motifs and pathways are seamlessly integrated to form functional modules groups of
nodes (for example, proteins and metabolites) that are responsible for discrete cellular
functions. These modules are nested in a hierarchical fashion and define the cell’s largescale functional organization.
They identified small subgraphs that appear more frequently in a real network than in its
randomized version. This enabled them to distinguish coincidental motifs from recurring
significant patterns of interconnections.
An important attribute of the complexity pyramid is the gradual transition from the
particular (at the bottom level) to the universal (at the apex).
Lately, we have come to appreciate the power of maps—reliable depositories of
molecular interactions. Yet existing maps are woefully incomplete; key links between
different organizational levels are missing.
The node degrees follow a Power-law degree distribution.
Relatively small number of
Poisson distribution
highly connected nodes that
are known as hubs.
Account for the
coexistence of modularity,
local clustering and scalefree topology
The origin of the scale-free topology and hubs
in biological networks.
Growth and preferential attachment.
Growth means that the network emerges
through the subsequent addition of new
nodes.
Preferential attachment means that new
nodes prefer to link to more connected nodes.
Growth and preferential attachment
generate hubs through a ‘rich-gets-richer’
mechanism: the more connected a node is,
the more likely it is that new nodes will link
to it, which allows the highly connected
nodes to acquire new links faster than their
less connected peers.
Yeast protein interaction network.
red = lethal, green = nonlethal, orange = slow growth,
yellow = unknown.
INSULIN SIGNALLING NETWORK
1
1
2
3
~1000 combinatorial
possibilities
Structure of insulin receptor substrates
Complementary roles of isoforms (IRS)
Though IRS protein structure are highly homologous,
they serve complementary rather than redundant roles
based on knock out phenotypes
Irs1-knockout mice have defective insulin action
primarily in the muscle, and a generalized defect in
body growth due to IGF1 resistance.
Irs1-knockout pre-adipocytes have
defects in differentiation
Irs2-knockout mice have greater defects in
insulin signalling in the liver, and show altered
growth in only a few tissues, such as certain
neurons and pancreatic β-cells.
Whereas Irs2-knockout preadipocytes
differentiate normally, but fail to respond to
insulin-stimulated glucose transport.
Biochemical studies have revealed several molecular mechanisms
by which the IRS proteins could exert their specific effects.
Different expression pattern
IRS1 and IRS2 are widely distributed, whereas
IRS3 is largely limited to the adipocytes and brain,
and IRS4 is expressed primarily in embryonic
tissues or cell lines.
IRS1 and IRS2 have been shown to differ in their
ability or affinity to bind to various SH2 partners.
Allows selective channelling to downstream
effectors
IRS1 is found to more closely regulate glucose
uptake, whereas IRS2 seems to be more closely
linked to MAPK activation.
Other variants are modifiers by blocking or
attenuating the signalling
IRS3 and IRS4 probably modify the actions of
IRS1 and IRS2, as they cannot activate MAPK
and PI3K to the same degree as IRS1 and IRS2,
and might actually antagonize some of their
functions when expressed at high levels.
Isoform-specific functions of IRS proteins.
PI3K as a critical node.
AKT/PKB as a critical node.