Transcript System Structures Identification

System Structures Identification
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Contents of research on gene regulatory
networks
1. all components of the network, the function of
each component, Interactions;
2. all associated parameters;
3. prediction of unknown genes and interactions
System Structures Identification
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two major tasks ( because there are
multiple networks and parameter values
that behave quite similar to the target
network. One must identify the true network
out of multiple candidates)
1. network structure identification
2. parameter identification
System Structures Identification
1. network structure identification
• two approaches
① bottom-up approach based on the
compilation of independent experimental data
(through literature searches and some specific
experiments) KEGG EcoCyc
② top-down approach tries to make use of highthroughput data infer network structures from
expression profiles and extensive gene disruption
data
System Structures Identification
2. parameter identification ( the parameter
set has to be estimated based on
experimental data)
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parameter optimization methods
① brute force exhaustive search,
② genetic algorithms,
③ simulated annealing, etc.
System Behavior
or Function Analysis
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Contents of research on system
behavior analysis
1. functionalities of the circuits
2. the robustness and stability of the
system
System Behavior
or Function Analysis
1. functionalities of the circuits (a possible
evolutionary family of circuits as well as a
“periodic table” for functional regulatory circuits)
a. Simulation
b. Analysis Methods
① bifurcation analysis
② metabolic control analysis
③ sensitivity analysis
a.
Simulation tools
System Behavior
or Function Analysis
b. Analysis Methods
• bifurcation analysis Xenopus cell cycle analysis
based on a set of equations describing the essential
process of the Xenopus cell cycle
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metabolic control analysis and
sensitivity analysis provides a useful method to
understand system-level behaviors of metabolic circuits
under various environments and internal disruptions
System Behavior
or Function Analysis
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the robustness and stability of the
system
1.
adaptation, which denotes the ability to cope with
environmental changes
parameter insensitivity, which indicates a system’s
relative insensitivity to specific kinetic parameters
graceful degradation, which reflects the
characteristic slow degradation of a system’s
functions after damage, rather than catastrophic
failure.
2.
3.
System Behavior
or Function Analysis
• robustness is attained by
1. System control such as negative-feedback and feedforward control
2. Redundancy whereby multiple components with
equivalent functions are introduced for backup
3. Structural stability where intrinsic mechanisms are
built to promote stability
4. Modularity where subsystems are physically or
functionally insulated so that failure in one module
does not spread to other parts and lead to systemwide catastrophe
• Organized modularity model. Date-hub/module network
representation of the filtered yeast interactome. Date
hubs are represented as red circles and modules are
represented as blue squares. The inset illustrates
modular organization in detail; the date hub Cmd1
connects four modules at ‘higher level’, whereas the
nearby party hub Sec22 connects to eight proteins within
an ‘endoplasmic reticulum’ module.
Test system for systems biology
• galactose utilization in yeast
how is the galactose utilization system
regulated and how is it interconnected
to other systems in the yeast cell?
Test system for systems biology
• Four distinct types of global
datasets were generated and
analyzed
1. Genetic perturbations
2. Testing network hypothesis
3. Proteome analysis in wild-type yeast
with the system turned on and off
4. Kinetic analysis of global mRNA
concentrations change
Test system for systems biology
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Four distinct types of global datasets were
generated and analyzed
nine knockouts and the wild-type yeast were interrogated
when the system was running in the presence of
galactose and when the system was shut down in the
absence of galactose how the expression patterns of all
6200 genes changed
① most of these perturbations behaved in accordance with
the model, some discrepancies tested with double
knockout perturbations
② 997 of 6200 genes had altered expression patterns in
these perturbations, they could be clustered into 16
groups, Each group contained one or more functional
biomodules for the yeast cell (e.g., cell cycle, amino acid
synthesis, synthesis of other carbohydrates, etc).
Test system for systems biology
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Four distinct types of global datasets were
generated and analyzed
2. Testing network hypothesis the galactose utilization
module was interconnected to these other modules
and perturbations of it perturbed the other modules
① Cytoscape was developed to integrate global mRNA
concentrations, protein concentrations,
protein/protein and protein/DNA interactions.
② The global datasets of the 997 perturbed mRNAs were
then joined to the global datasets of protein/protein
and protein/DNA interactions
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This network was
developed by combining
clusters of messenger
RNAs defined by the
knockout perturbation
experiments and the
protein/ protein and
protein/DNA interaction
data.
The yellow arrows
indicate protein/DNA
interactions (transcription
factor activity)
the blue bars indicate
protein/protein
interactions.
The red circle indicates
the galactose-4 gene has
been knocked out.
A grayscale indicates
levels of messenger RNA
expression
black equals high levels;
white equals low levels.