Lecture 10 Flashcards

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1
Q

Networks - Abstraction of Cell Regulation

A
  • Construct Gene Regulatory Network
  • Networks in general large

    => need computers
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2
Q

Network Topology - All Networks are Equal

A
  • All networks have same components: Nodes & Edges
  • But dierent topology: dierences in incoming/outgoing edges per node
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3
Q

Network Biology

A
  • Genes and proteins interact in a complex network
  • Can we gain knowledge from the network analysis?
  • All proteins equally important
  • Imagine: proteins interact on a regular grid. All proteins have same # of interactors
  • All proteins equally important
  • True biological Networks look “modular”
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4
Q

The Königsberger Bridge Problem

A
  • Recent paper:
  • Leonhard Euler, Solutio problematis ad geometriam situs pertinentis,
    in Commentarii academiae scientiarum Petropolitanae 8, 1741, pp. 128-140
  • Question:
  • Can you walk through the whole city and cross each bridge only once given that
  • You cannot swim to the island, bridges must be crossed completely
  • Start and end points of the walk need not be the same.
  • Solution: Abstract the problem
  • The paths on the island do not matter, then
  • Consider land mass as vertex or node
  • Each bridge as connection, called edge, between nodes
  • Graph with 4 nodes, and 7 edges
  • Degree: the numbers of edges touching a node
    Solution: graph must be connected and have 0 or 2 nodes of odd degree
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5
Q

Networks and Graphs in Biology

A
  • 1000s of genes/proteins interacting in Eucaryotes/Procaryotes
  • Disregard kinetics of interaction for now => depict as network
    -> Always possible, if complex system reducible to discrete elements and their relations => possible for most complex system
  • Describe dierent things: causal or mechanistic eects
    -> Chemical, logic, functional relatedness
  • Inferred from
    -> screening data, such as Yeast 2 Hybrid
    -> text mining
  • Highlights topology => might be more important than dynamics!

Network examples:
- The Social Network
- gene Interaction network
- transcription network in E.coli
- protein-protein interaction in yeast
- the human disease network

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6
Q

Yeast 2 Hybrid Screening

A
  • Y2H detects the physical interactions of proteins through downstream activation of a reporter gene.
  • Modular TFs: DNA binding & activating domain
    -> Tag bait to DBD & prey to activating domain => reside on plasmids in yeast
    -> If proteins interact => gene necessary for survival expressed,
    -> e.g. leucine/ histidine/tryptophan biosynthesis genes
    -> Separate QCs for plasmid transfection/interaction
    -> Finally sequence surviving strains => Interactions
  • Drawback: high false positive/negative rates, dierent proteins in yeast and human
  • Qualitative data only
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7
Q

Erlös-Rényi Network

A
  • A model network, proposed 50 years ago
  • Explains certain properties of real networks
  • Benchmark real networks Null-model for networks (no such network really exists)
  • Dumbest model:
    -> n nodes & draw edges with probability p between pairs
    -> For each n(n-1)/2 edges (can you see this?):
    -> draw edge with prob. p, skip edge with prob. (1-p)
  • Elementary random network G(n, p)
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8
Q

Network diameter

A
  • Diameter: longest sorgtest path between pairs of nodes
  • Equivalently: average distance between two random nodes
    -> n nodes, fully connected: diameter d = 1
    -> n nodes, linear chain d = n-1
  • ER network: increase p from 0 -> 1
    -> becomes fully connected
    -> Diameter finite, becomes 1 for p = 1
  • How does diameter d depend on p?
  • Assume network with identical degrees z=p(n-1)
  • How many nodes reached in l steps? 19
  • Step 1: z
  • Step 2: z(z-1) * Step 3: z(z-1)2
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9
Q

Facebook - A Small World Network

A
  • 6 edges between any person in the world
  • How close are you to Bill Gates?
  • Shortcuts between nodes:
  • all nodes are close to each other
  • 6 edges between any person in the world
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10
Q

Consequences for Cancer Biology

A
  • Few genes frequently mutated
  • Oncogenes & Tumor Suppressor Genes
  • Mostly hub-proteins
  • Rationale on diagnosis & treatment
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11
Q

Preferential Attachment

A
  • Structure related to formation process
  • Network growth & preferential attachment (The rich get richer) * New nodes are attached to nodes proportional to their degree
  • Both processes are necessary for scale-free topology
  • Biological Explanations
  • Gene Duplication
  • High prob. that random new protein attached to a hub node
  • Hubs are evolutionary older e.g.
  • coenzyme A: oxydation of fatty acids
  • Guanosine triphosphate (GTP): signal transmitters
  • Nicotinamide adenine dinucleotide (NAD): redox reactions
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12
Q

Hierarchy and Modularity

A
  • How to resolve hierarchy in a scale-free network?
  • Hubs break layered architecture
  • Instead: modularize network into clusters
  • Hierarchy: number of modules a node belongs to
    -> Low hierarchy: few functional tasks
    -> High hierarchy: many tasks
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