Loading in 5 sec....

Biological networks: Types and sourcesPowerPoint Presentation

Biological networks: Types and sources

- By
**baby** - Follow User

- 212 Views
- Updated On :

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

Related searches for Biological networks: Types and sources

Download Presentation
## PowerPoint Slideshow about 'Biological networks: Types and sources' - baby

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### Biological networks:Types and sources

### Network Properties spectrometry (APMS)

Protein-protein interactions,

Protein complexes,

and network properties

Networks in electronics

Lazebnik, Cancer Cell, 2002

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

Protein-DNA interactions

Genetic interactions

Metabolic reactions

Co-expression interactions

Text mining interactions

Association networks

Interaction networks in molecular biologyApproaches 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 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 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 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 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

Growth of PPI data: IntAct Statistics spectrometry (APMS)

IntAct Statistics spectrometry (APMS)

IntAct Statistics spectrometry (APMS)

iRefIndex integration of PPI DBs spectrometry (APMS)http://irefindex.uio.no/wiki/iRefIndex

Human PPI network spectrometry (APMS)

Filtering by subcellular localization spectrometry (APMS)

de Lichtenberg et al., Science, 2005

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 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:
- NAB is the number of purifications containing both proteins, i.e. the intersection of experiments that find them,
- NAB 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 spectrometry (APMS)

Graphs, paths, topology

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 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 spectrometry (APMS)

- G(V,E)
- |V| = 69
- |E| = 71

Connected Components spectrometry (APMS)

- G(V,E)
- |V| = 69
- |E| = 71
- 6 connected components

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 spectrometry (APMS)

Shortest-Path between nodes spectrometry (APMS)

Longest Shortest-Path spectrometry (APMS)

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 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 spectrometry (APMS)

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

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 coefficientHierarchical Networks high clustering coefficient

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004

Detecting hierarchical organization high clustering coefficient

Barabási AL, Oltvai ZN. Nat Rev Genet. 2004

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 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)

Download Presentation

Connecting to Server..