System%20Structures%20Identification

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System Structures Identification

• Contents of research on gene regulatory networks
  
• all components of the network, the function of
  each component, Interactions
• all associated parameters
• prediction of unknown genes and interactions

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System Structures Identification

• 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)
• network structure identification
• parameter identification

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System Structures Identification

• network structure identification
• two approaches
• bottom-up approach based on the compilation of
  independent experimental data (through literature
  searches and some specific experiments) KEGG
  EcoCyc
• top-down approach tries to make use of
  high-throughput data infer network structures
  from expression profiles and extensive gene
  disruption data

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System Structures Identification

• parameter identification ( the parameter set has
  to be estimated based on experimental data)
• parameter optimization methods
• brute force exhaustive search,
• genetic algorithms,
• simulated annealing, etc.

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System Behavior or Function Analysis

• Contents of research on system behavior analysis
• functionalities of the circuits
• the robustness and stability of the system

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System Behavior or Function Analysis

• functionalities of the circuits (a possible
  evolutionary family of circuits as well as a
  periodic table for functional regulatory
  circuits)
• Simulation
• Analysis Methods
• bifurcation analysis
• metabolic control analysis
• sensitivity analysis

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Simulation tools
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System Behavior or Function Analysis

• Analysis Methods
• bifurcation analysis Xenopus cell cycle analysis
  based on a set of equations describing the
  essential process of the Xenopus cell cycle
• metabolic control analysis and sensitivity
  analysis provides a useful method to understand
  system-level behaviors of metabolic circuits
  under various environments and internal
  disruptions

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System Behavior or Function Analysis

• the robustness and stability of the system
• adaptation, which denotes the ability to cope
  with environmental changes
• parameter insensitivity, which indicates a
  systems relative insensitivity to specific
  kinetic parameters
• graceful degradation, which reflects the
  characteristic slow degradation of a systems
  functions after damage, rather than catastrophic
  failure.

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System Behavior or Function Analysis

• robustness is attained by
• System control such as negative-feedback and
  feed-forward control
• Redundancy whereby multiple components with
  equivalent functions are introduced for backup
• Structural stability where intrinsic mechanisms
  are built to promote stability
• Modularity where subsystems are physically or
  functionally insulated so that failure in one
  module does not spread to other parts and lead to
  system-wide catastrophe

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• 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.

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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?

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Test system for systems biology

• Four distinct types of global datasets were
  generated and analyzed
• Genetic perturbations
• Testing network hypothesis
• Proteome analysis in wild-type yeast with the
  system turned on and off
• Kinetic analysis of global mRNA concentrations
  change

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Test system for systems biology

• 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
• most of these perturbations behaved in accordance
  with the model, some discrepancies tested with
  double knockout perturbations
• 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).

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Test system for systems biology

• Four distinct types of global datasets were
  generated and analyzed
• Testing network hypothesis the galactose
  utilization module was interconnected to these
  other modules and perturbations of it perturbed
  the other modules
• Cytoscape was developed to integrate global mRNA
  concentrations, protein concentrations,
  protein/protein and protein/DNA interactions.
• 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.