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How Scale-Free are Biological Networks. PowerPoint PPT Presentation

How Scale-Free are Biological Networks. Raya Khanin and Ernst Wit Department of Statistics University of Glasgow Biological Networks Protein interaction network : proteins that are (or might be) connected by physical interactions

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How scale free are biological networks l.jpg

How Scale-Free are Biological Networks.

Raya Khanin and Ernst Wit

Department of Statistics

University of Glasgow


Biological networks l.jpg

Biological Networks

Protein interaction network: proteins that are (or might be) connected by physical interactions

Metabolic network: metabolic products and substrates that participate in one reaction

Gene regulatory network: two genes are connected if the expression of one gene modulates expression of another one by either activation or inhibition


Biological networks3 l.jpg

Biological networks

  • Node represents a gene, protein or metabolite

  • Edge represents an association, interaction, co-expression

  • Directed edge stands for the modulation (regulation) of one node by another: e.g. arrow from gene X to gene Y means gene X affects expression of gene Y


Network measures l.jpg

Network measures

Degree (or connectivity)

of a node, k, is the number

of links (edges) this node has.

The degree distribution,

P(k), is the probability that

a selected node has exactly

k links. Networks are classified by

their degree distributions.

(Barabasi and Oltvai, Nature, 2004)


Random network l.jpg

Random Network

  • A fixed number of nodes are connected randomly to each other: start with N nodes and connect each pair with probability p creating a graph with pN(N-1)/2 randomly placed links.

  • The degrees follow a Poisson distribution: most nodes have roughly the same number of links, approximately equal to the network’s average degree, <k>; nodes that have significantly more or less links than <k> are very rare.


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Scale-Free Network

  • Scale-free networks have a few nodes with a very large number of links (hubs) and many nodes with with only a few links.

  • It indicates the absence of a typical node in the network

  • Scale-free networks are characterized by a power-law distribution:


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Comparing Random and Scale-free distribution

  • In the random network, the five nodes with the most links (in red) are connected to only 27% of all nodes (green). In the scale-free network, the five most connected nodes (red) are connected to 60% of all nodes (green) (source: Nature)


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Examples of scale-free networks

Network of citations between scientific papers

Network of collaborations, movie actors

Electrical power grids, airline traffic routes, railway networks

World Wide Web and other communication systems

Biological Networks

"These laws, applying equally well to the cell and the ecosystem, demonstrate how unavoidable nature's laws are and how deeply self-organization shapes the world around us.“(A.Barabasi, 2002).


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Google: scale-free networks

“Scale-free networks are everywhere. They can be seen in … terrorist networks. What this means for counter-terrorism experts?”

Global guerrillas (network organization, infrastructure disruption, and the emerging marketplace of violence)

http://globalguerrillas.typepad.com/globalguerrillas/2004/05/scalefree_terro.html

Scale|free : http://www.scalefree.info (Social Networking, Communities of Practice and Knowledge Management: publish articles such as “love and knowledge”, “what lovers tell us about persuasion”, “how ‘social contagion’ affects consumer behaviour)


Scale free networks l.jpg

Scale-free Networks

Properties of scale-free systems are invariant to changes in scale. The ratio of two connectivities in a scale-free network is invariant under rescaling:

This implies that scale-free networks are self-similar, i.e. any part of the network is statistically similar to the whole network and parameters are assumed to be independent of the system size.


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Scale-free Networks

Scale-free (self-similarity) properties of a common cauliflower plant: it is virtually impossible to determine whether one is looking at a photograph of a complete vegetable or its part, unless an additional scale-dependent object (a match) is added. (A) Complete vegetable; (B) a small segment of the same vegetable; (C) small part of the segment shown in B. The same match was used in all three photographs to provide a sense of scale for an otherwise scale-free structure (Gomez et al, 2001).

Note: vegetable was purchased in Sloan supermarket in Manhattan’s Upper West Side.


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Biological networks are reported to be scale-free

Metabolic networks

Protein interaction networks

Protein domain networks

Gene co-expression networks

Distribution of gene expression and spot intensities on microarrays

Frequency of occurrence of generalized parts in genomes of different organisms


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Example of gene network

  • To find indication of a power-law

    the data is usually graphically fitted

    by a straight line on a log-log scale

  • Log contract data

  • Fit to a few points is not particularly goodNumber of regulated genes per regulating protein: Guelzim et al, Nature, 2002


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Estimating the power exponent by maximum-likelihood

  • the power-law distribution

  • Observed values: xi is a connectivity of node i

  • the likelihood function for N observed connectivities

  • the log-likelihood is maximized by finding zeros of its derivative using the Newton-Raphston method.


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Goodness-of-fit testing

  • E(k) - expected(k) with estimated ; O(k) – observed(k)

  • Consider a chi-squared statistic,

    (approximately) under H0: network is scale-free

  • Pool for connectivity values over k*, for which the expected number of connections is less than 5. As a result, the chi-squared statistic is approximately chi-squared distributed with k*-2 degrees of freedom, if the data come truly from a power-law distribution.


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Goodness-of-fit testing

  • The p-value for each of the networks can be calculated by the exceedence probability of a chi-squared distribution:

    t is calculated (observed) values of T with estimated

    p-value is the probability a network has such connectivities if they were drawn from the power-law distribution.

  • If p<0.05, H0 (network is scale-free) is rejected.


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Exponents of the power-law and scale-free p-values

Datasets p-value

Uetz 25 2.050.00004

Schwikowski 26 1.8650

Ito 27 2.020

Li 28 2.30

Rain 29 2.10

Giot 21 1.530

Tong 9 1.440

Lee 20 1.990

Guelzim 17 1.490

Spellman/Cho 1.27(1.06) 0


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Truncated power-law

is the cut-off, s. t. the number of connections is less than expected for pure scale-free networks for

and the behaviour is approximately scale-free within the range


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Parameters of the truncated power-law and truncated power-law p-values

Datasets p-value

Uetz 25 1.6 8.67 0.37

Schwikowski 26 1.26 6.2 0.105

Ito 27 1.79 26 0

Li 28 2.1 19.5 0.0178

Rain 1.12 11.5 0.2

Giot 21 1.09 20 0.0013

Tong9 0.96 23.7 0

Lee 20 1.96 294 0

Guelzim17 1.18 15 0.00001

Spellman/Cho 1.07(0.78) 73(99) 0.7(0.1)


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Scale-free or not:why is it important?

  • Network architecture

  • Evolutionary models

  • Scalability (self-similarity) criterion does not hold; one has to exercise caution while applying the features of an already studied part of the network to investigate properties of the unknown part of the same network, or of other networks that seem similar


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Example of gene network

  • Guelzim and co-authors (Nature, 2002)

    conclude that connectivity distributions

    of gene transcriptional network for yeast

    and E-coli are scale-free, and thus

    “bacterial and fungal genetic networks are

    free of characteristic scale with respect to

    the distributions of both regulating and

    departing connections”.

    Number of regulated genes per regulating protein

    We found, however, that the scale-free property does not even hold and therefore such biological conclusions are invalid


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Qualitative properties of biological networks

  • Existence of hubs

  • Many nodes with a few connections

  • “Lethality and centrality” (the more essential gene/protein is the more connections it has)

  • Small-world property (short average path)

  • High clustering of nodes


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Alternative non-scale free distributions

  • generalized truncated power-law

  • stretched exponential distribution

  • geometric random graph: a geometric graph with n independently and uniformly distributed points in a metric space


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