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System Structures Identification • 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 • 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) • parameter optimization methods ① brute force exhaustive search, ② genetic algorithms, ③ simulated annealing, etc. System Behavior or Function Analysis • 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 • 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 • 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 • 1. 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 • 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 • • • • • • • 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.