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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




  • Sequencing

  • Gene knock-out

  • Microarrays

  • etc.

















Parts List

  • Genetic interactions

  • Protein-Protein interactions

  • Protein-DNA interactions

  • Subcellular Localization


  • Microarrays

  • Proteomics

  • Metabolomics


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 DBs

Human PPI network

Filtering by subcellular localization

de Lichtenberg et al., Science, 2005





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


  • 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


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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