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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 l.jpg

Biological networks:Types and sources

Protein-protein interactions,

Protein complexes,

and network properties


Networks in electronics l.jpg
Networks in electronics

Lazebnik, Cancer Cell, 2002


Interactions l.jpg

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



Interaction networks in molecular biology l.jpg

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 l.jpg
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 l.jpg
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 l.jpg
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 interactions by immunoprecipitation followed by mass spectrometry apms l.jpg
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 l.jpg
Affinity Purification Mass Spec spectrometry (APMS)

  • 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 l.jpg
Recent binary PPI network spectrometry (APMS)

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 l.jpg
Other characterizations of physical interactions spectrometry (APMS)

  • 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



Intact statistics l.jpg
IntAct Statistics spectrometry (APMS)


Intact statistics16 l.jpg
IntAct Statistics spectrometry (APMS)


Irefindex integration of ppi dbs http irefindex uio no wiki irefindex l.jpg
iRefIndex integration of PPI DBs spectrometry (APMS)http://irefindex.uio.no/wiki/iRefIndex


Human ppi network l.jpg
Human PPI network spectrometry (APMS)


Filtering by subcellular localization l.jpg
Filtering by subcellular localization spectrometry (APMS)

de Lichtenberg et al., Science, 2005


An example binary interaction score l.jpg

D spectrometry (APMS)

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 l.jpg
An example spectrometry (APMS)“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



Network properties l.jpg

Network Properties spectrometry (APMS)

Graphs, paths, topology


Graphs l.jpg
Graphs spectrometry (APMS)

  • 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 l.jpg
Sparse vs Dense spectrometry (APMS)

  • 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 l.jpg
Connected Components spectrometry (APMS)

  • G(V,E)

  • |V| = 69

  • |E| = 71


Connected components27 l.jpg
Connected Components spectrometry (APMS)

  • G(V,E)

  • |V| = 69

  • |E| = 71

  • 6 connected components


Paths l.jpg
Paths spectrometry (APMS)

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 l.jpg
Shortest-Path between nodes spectrometry (APMS)


Shortest path between nodes30 l.jpg
Shortest-Path between nodes spectrometry (APMS)


Longest shortest path l.jpg
Longest Shortest-Path spectrometry (APMS)


Degree or connectivity l.jpg
Degree or connectivity spectrometry (APMS)

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 l.jpg
Random spectrometry (APMS)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 l.jpg
Essentiality vs node degree spectrometry (APMS)


Clustering coefficient l.jpg
Clustering coefficient spectrometry (APMS)

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


Protein complexes have a high clustering coefficient l.jpg

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 l.jpg
Hierarchical Networks high clustering coefficient

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004


Detecting hierarchical organization l.jpg
Detecting hierarchical organization high clustering coefficient

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004


Scale free networks are robust l.jpg
Scale-free networks are robust high clustering coefficient

  • 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 l.jpg
Other interesting features high clustering coefficient

  • 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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