network biology presentation by: ansuman sahoo 10 th semester 20 january 2015
TRANSCRIPT
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Network Biology
Presentation by:Ansuman sahoo10th semester20 January 2015
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GENETIC INTERACTION NETWORK IN YEAST
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Model Generation
Interactions
Lazebnik, Cancer Cell, 2002
Parts List
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• Sequencing• Gene knock-out• Microarrays• etc.
Interactions
• Genetic interactions• Protein-Protein
interactions • Protein-DNA
interactions• Subcellular
Localization
Dynamics
• Microarrays• Proteomics• Metabolomi
cs
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Types of networks
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Interaction networks in molecular biology
• Protein-protein interactions
• Protein-DNA interactions
• Genetic interactions
• Metabolic reactions
• Co-expression interactions
• Text mining interactions
• Association networks
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Approaches by interaction/method type• Physical Interactions
• Yeast two hybrid screens (PPI)• Affinity purification mass spectrometry, APMS (PPI)• Protein complementation assays (PPI)• ChIP-Seq, ChIP-Chip (protein-DNA)• CLIP-Seq, RIP-Seq, HITS-CLIP, PAR-CLIP (protein-RNA)
• Other measures of ‘association’• Genetic interactions (double deletion mutants)• Co-expression• Functional associations• STRING (which includes many of the above and more)
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Evolutionary origin of scale-free networks
Two fundamental processes have a key role in the development of real networks
• Growth process • Preferential attachment
Both are jointly responsible for the emergence of the scale-free property in complex networks
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Growth Process
The network emerges through the subsequent addition of new nodes
• World Wide Web
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Preferential attachment (rich gets richer)
Nodes prefer to connect to more connected nodes.
If a node has many links, new nodes will tend to connect to it with a higher probability.
This node will therefore gain new links at a higher rate than its less connected peers and will turn into a hub.
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Gene duplication
• Common origin of growth and preferential attachment in protein networks.
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Evidence of preferential attachment
Highly connected proteins have a natural advantage: • More likely to have a link to a duplicated protein. • More likely to gain new links if a randomly selected protein is duplicated.
Origin of the scale free topology traces back to gene duplication
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Evidence of network growth
The nodes that appeared early in the history of the network are the most connected ones.
• Among the most connected substrates of the metabolic networks: •Coenzyme A, NAD, GTP
Cross-genome comparisons:
• the evolutionarily older proteins have more links to other proteins than their younger counterparts.
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Other interesting features
• Cellular networks are assortative, i.e. hubs tend not to interact directly with other hubs.
• Hubs have been claimed to be “older” proteins (so far claimed for protein-protein interaction networks only)
• Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)
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Modules
Modularity: group of physically or functionally linked molecules (nodes) that work together to achieve a distinct function:
• protein-protein & protein-RNA complexes. • Temporally coregulated groups of molecules: •governing cell cycle. •Signal amplification in a signaling pathway.
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Module Identification Methods:
Using the network’s topological description. •Identification of functional modules from the genomic association.
Combining the topology with integrated functional genomic data
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Network robustness
The system’s ability to respond to changes in the external conditions or internal organization while maintaining relatively normal behaviour.
•Topological robustness. •Functional & dynamical robustness.
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Topological robustness
disabling a substantial number of nodes will result in an inevitable functional disintegration of a network. This is certainly true for a random network:
• if a critical fraction of nodes is removed, the network breaks down into tiny, non-communicating islands of nodes
Scale-free networks are amazingly robust against accidental failures:
• even if 80% of randomly selected nodes fail, the remaining 20% still form a compact cluster with a path connecting any two nodes. • Random failure affects mainly the numerous small degree nodes.
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Topological robustness
There is a strong relationship between the hub status of a molecule and its role in maintaining the viability and/or growth of a cell.
•S. cerevisiae only ~10% of the proteins with less than 5 links are essential, but this fraction increases to over 60% for proteins with more than 15 interactions.
The protein’s degree of connectedness has an important role in determining its deletion phenotype.
• Only ~ 18.7% of S. cerevisiae genes (~14.4% in E. coli) are lethal when deleted individually.
• Simultaneous deletion of many E. coli genes is without substantial phenotypic effect.
Highly interconnected hubs are evolutionary conserved in higher life forms.
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Functional & dynamical robustness
In a cellular network, each node has a slightly different biological function.
The effect of a perturbation cannot depend on the node’s degree only.
Functional role of the whole complex determines the deletion phenotype of the individual proteins.
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Similar to topological robustness, dynamical and functional robustness are also selective:
• some important parameters remain unchanged under perturbations, but others may vary widely.
• For example, the adaptation time or steady-state behaviour in chemo-taxis show strong variations in response to changes in protein concentrations.
Functional & dynamical robustness
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Determinants of actual interactions:
Cell’s internal state position in the cell cycle Intracellular environment 3D shape Anatomical architecture Compartmentalization State of cells cytoskeleton
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Thank You