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Biological networks: Types and sources. Protein-protein interactions, Protein complexes, and network properties. Networks in electronics. Lazebnik, Cancer Cell, 2002. Model Generation. Interactions. Sequencing Gene knock-out Microarrays etc. YER001W YBR088C YOL007C YPL127C

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Biological networks: Types and sources

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Biological networks:Types and sources

Protein-protein interactions,

Protein complexes,

and network properties


Networks in electronics

Lazebnik, Cancer Cell, 2002


Model

Generation

Interactions

  • Sequencing

  • Gene knock-out

  • Microarrays

  • etc.

YER001W

YBR088C

YOL007C

YPL127C

YNR009W

YDR224C

YDL003W

YBL003C

YDR097C

YBR089W

YBR054W

YMR215W

YBR071W

YBL002W

YNL283C

YGR152C

Parts List

  • Genetic interactions

  • Protein-Protein interactions

  • Protein-DNA interactions

  • Subcellular Localization

Interactions

  • Microarrays

  • Proteomics

  • Metabolomics

Dynamics

Lazebnik, Cancer Cell, 2002


Types of networks


Protein-protein interactions

Protein-DNA interactions

Genetic interactions

Metabolic reactions

Co-expression interactions

Text mining interactions

Association networks

Interaction networks in molecular biology


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)


Yeast two-hybrid method

Y2H assays interactions in vivo.

Uses property that transcription factors generally have separable transcriptional activation (AD) and DNA binding (DBD) domains.

A functional transcription factor can be created if a separately expressed AD can be made to interact with a DBD.

A protein ‘bait’ B is fused to a DBD and screened against a library of protein “preys”, each fused to a AD.


Issues with Y2H

  • Strengths

    • Takes place in vivo

    • Independent of endogenous expression

  • Weaknesses: False positive interactions

    • Detects “possible interactions” that may not take place under physiological conditions

    • May identify indirect interactions (A-C-B)

  • Weaknesses: False negatives interactions

    • Similar studies often reveal very different sets of interacting proteins (i.e. False negatives)

    • May miss PPIs that require other factors to be present (e.g. ligands, proteins, PTMs)


Protein complementation assay (PCA)


Protein interactions by immunoprecipitation followed by mass spectrometry (APMS)

  • Start with affinity purification of a single epitope-tagged protein

  • This enriched sample typically has a low enough complexity to be fractionated by electrophoresis techniques


Affinity Purification Mass Spec

  • Strengths

    • High specificity

    • Well suited for detecting permanent or strong transient interactions (complexes)

    • Detects real, physiologically relevant PPIs

  • Weaknesses

    • Lower sensitivity: Less suited for detecting weaker transient interactions

    • May miss complexes not present under the given experimental conditions (low sensitivity)

    • May identify indirect interactions (A-C-B)


Recent binary PPI network

Y2H by Yu et al. 2008 : 2018 proteins, 2930 interactions

PCA by Tarassov et al. 2008 : 1124 proteins, 2770 interactions


Other characterizations of physical interactions

  • Obligation

    • obligate (only found/function together)

    • non-obligate (can exist/function alone)

  • Time of interaction

    • permanent (complexes, often obligate)

    • strong transient (require trigger, e.g. G proteins)

    • weak transient (dynamic equilibrium)

  • Location/compartmentalization constraints

    • Same/different cellular compartment

    • Tissue specificity


Growth of PPI data: IntAct Statistics


IntAct Statistics


IntAct Statistics


iRefIndex integration of PPI DBshttp://irefindex.uio.no/wiki/iRefIndex


Human PPI network


Filtering by subcellular localization

de Lichtenberg et al., Science, 2005


D

A

B

C

High confidence

(1 unshared interaction partners)

Low confidence

(4 unshared interaction partners)

An example binary-interaction score

  • For the yeast two-hybrid experiments, the reliability of an interaction has been found to correlate well with the number of non-shared interaction partners for each interactor [6]. This can be summarized in the following raw quality score

  • where NA and NB are the numbers of non-shared interaction partners for an interaction between protein A and B.


An example “pull-down” interaction score

  • For APMS or other IP pull-down experiments, the reliability of the inferred binary interactions has been found to correlate better with the number of times the proteins were co-purified vs. purified individually.

  • where:

    • NAB is the number of purifications containing both proteins, i.e. the intersection of experiments that find them,

    • NAB is the total number of purifications that find either A or B, i.e. the union of experiments that find them,

    • NA is the number of purifications containing A, and

    • NB is the numbers of purifications containing B


Filtering reduces coverage and increases specificity


Network Properties

Graphs, paths, topology


Graphs

  • Graph G=(V,E) is a set of vertices V and edges E

  • A subgraph G’ of G is induced by some V’V and E’ E

  • Graph properties:

    • Connectivity (node degree, paths)

    • Cyclic vs. acyclic

    • Directed vs. undirected


Sparse vs Dense

  • G(V, E)

    • Where |V|=n the number of vertices

    • And |E|=m the number of edges

  • Graph is sparse if m ~ n

  • Graph is dense if m ~ n2

  • Complete graph when m = (n2-n)/2 ~ n2


Connected Components

  • G(V,E)

  • |V| = 69

  • |E| = 71


Connected Components

  • G(V,E)

  • |V| = 69

  • |E| = 71

  • 6 connected components


Paths

A path is a sequence {x1, x2,…, xn} such that (x1,x2), (x2,x3), …, (xn-1,xn) are edges of the graph.

A closed path xn=x1 on a graph is called a graph cycle or circuit.


Shortest-Path between nodes


Shortest-Path between nodes


Longest Shortest-Path


Degree or connectivity

Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13


Random vs scale-free networks

P(k) is probability of each degree k, i.e fraction of nodes having that degree.

For random networks, P(k) is normally distributed.

For real networks the distribution is often a power-law:

P(k) ~ k-g

Such networks are said to be scale-free

Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13


Essentiality vs node degree


Clustering coefficient

The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity

k: neighbors of I

nI: edges between node I’s neighbors

The center node has 8 neighbors (green)

There are 4 edges between these neighbors

C = 1/7


Proteins subunits are highly interconnected and thus have a high clustering coefficient

There exists algorithms, such as MCODE, for identifying subnetworks (complexes) in large protein-protein interaction networks

Protein complexes have a high clustering coefficient


Hierarchical Networks

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004


Detecting hierarchical organization

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004


Scale-free networks are robust

  • Complex systems (cell, internet, social networks), are resilient to component failure

  • Network topology plays an important role in this robustness

    • Even if ~80% of nodes fail, the remaining ~20% still maintain network connectivity

  • Attack vulnerability if hubs are selectively targeted


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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