Redes de regulación génica - CSICcsbg.cnb.csic.es › master2012 › keynote ›...

Redes de regulación génica

David Ochoa García

http://phet.colorado.edu/sims/gene-network/gene-machine-lac-operon_en.jnlp







Represor Activador

Redes de regulación génica

RegulonDBBPP evidences 2012

Obteniendo Evidencias

Evidencias experimentales (genome-wide)


• Chip-on-Chip / Chip-Seq



• STAGE/SABE



• STAGE/SABE

• DNA arrays

DNA arrays

Predicciones computacionales


• Pattern Discovery


• Pattern Discovery

• Pattern Matching

Reverse engineering (genome-wide)

Reverse engineering (genome-wide)

Hartemink, A. J. Reverse engineering gene regulatory networks. Nat Biotechnol 23, 554–555 (2005).

Bases de Datos

Bases de Datos

http://regulondb.ccg.unam.mx/

http://regulondb.ccg.unam.mx


Bases de Datos

http://regulondb.ccg.unam.mx/

http://www.biobase-international.com/product/transcription-factor-binding-sites







Propiedades topológicas de las Redes de Regulación génica

Degree distribution

Degree distribution

Guelzim, N., Bottani, S., Bourgine, P. & Képès, F. Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet 31, 60–63 (2002)

Genes regulados

Degree distribution


Genes regulados

p(k)=CekDistribución exponencial

Degree distribution


Genes regulados

Genes reguladores

p(k)=Ck-gDistribución “Ley de Potencias”

p(k)=CekDistribución exponencial

Autoregulación

• El 70% de los reguladores de E.coli regulan su propia expresión

Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007).

Motivos

Motivos

Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

Motivos en redes de regulación

Motivos solapantes

Motivos solapantes

Dobrin, R., Beg, Q. K., Barabasi, A.-L. & Oltvai, Z. N. Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics 5, 10 (2004).

FFL

Motivos solapantes

Dobrin, R., Beg, Q. K., Barabasi, A.-L. & Oltvai, Z. N. Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics 5, 10 (2004).

FFL Bi-fan

Motivos en otros tipos de redes

Perfiles de Motivos

Perfiles de Motivos

Milo, R. et al. Superfamilies of evolved and designed networks. Science 303, 1538–1542 (2004)

Tipos de FFLs

Tipos de FFLs

FFL coherentes

Mangan, S. & Alon, U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100, 11980–11985 (2003).

Tipos de FFLs

FFL coherentes

FFL incoherentes


Operators

Operators

ANDORSUM


Propiedades de los motivos


Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)Shen-Orr, S. S., Milo, R., Mangan, S. & Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31, 64–68 (2002)


Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)

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FFL incoherentes

Motivos importantes en el desarrollo

Motivos SIM (Single Output Models)

Optimización temporal


Kalir, S. et al. Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science 292, 2080–2083 (2001).


Kalir, S. et al. Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science 292, 2080–2083 (2001).

Kalir, S. & Alon, U. Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell 117, 713–720 (2004).

Optimización en gasto

Optimización en gasto

Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nat Genet 36, 486–491 (2004).

Dinámica de las Redes de Regulación

Dinámica de la red

Dinámica de la red

Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).

Dinámica de la red

Dinámica de la red

Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).

Dinámica de la red - Hubs

Evolución de las redes de regulación

¿Evolución convergente?


van Noort, V., Snel, B. & Huynen, M. A. The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Rep 5, 280–284 (2004).

Evolución por duplicación

Evolución por duplicación

Teichmann, S. A. & Babu, M. M. Gene regulatory network growth by duplication. Nat Genet 36, 492–496 (2004).

Simulando la evolución


Existing network-evolution models cannot account for thecombination of the architecture of the coexpression network and

the correlation between coexpression and sequence similarity inparalogues. The network model of Barabasi & Albert (1999), basedon the concept of preferential attachment (Simon & Bonini, 1958),produces scale-free networks, but not small-world networks(cEcrandom; in a small-world network cbcrandom), even whenintroducing constraints to the number of connections per node orto the ageing of nodes (Amaral et al, 2000). The algorithm ofRavasz et al (2002) to realize a small-world, scale-free networkinvolves hierarchical duplication of complete modules andattachment to the central node of the existing module. This modeldoes not lead to a high likelihood of attachment betweenduplicated nodes, and is therefore not explanatory for theevolution of our network. Moreover, in contrast to the predictionsof this model, the explicit testing of the age of genes (see Methods)and the number of their connections did not reveal any positivecorrelation (Pearson correlation!"0.04, P-value that there is nopositive correlation! 0.98). The duplication model of Bhan et al(2002) assumes duplication of genes with partial conservation ofconnections. When seeding this model with a scale-free network,most of the structure persists for a few iterations; however,simulating this model for a higher number of iterations results inan exponential degree distribution of N versus k (Pastor-Satorraset al, 2003). In this model, there is no relation between the timingof a duplication event and the likelihood of attachment of theresulting paralogues. This is because the connections are fixedonce established, as in all previous models. This is not anevolutionarily sound assumption, given the observation thatconnectivity between paralogues is dependent on the timing ofthe duplication event and that coexpression is only partlyconserved between species (Teichmann & Babu, 2002; van Noortet al, 2003).

A’A

1 Gene duplication

A

A

4

A

TFBS deletion

A A

TFBS duplication3

B B

A

2 Gene deletion

Genome evolutionGenome initiation

2

25

1

3...

100 x

.

.

.

.... . .

. . ....

.

.

.

etc.

A B CNetwork determination

Fig 3 | Evolutionary model of transcription regulation. The evolutionary model consists of a few simple mechanisms. (A) A genome is initiated with 25 genes

with random TFBSs, represented by the small coloured shapes. (B) Possible events are as follows: (1) Gene A is duplicated, gene A0 has the same TFBS as its

duplicate gene A; the duplicates are coexpressed. (2) Gene deletion. (3) Gene A acquires a new TFBS from gene B. The probability of obtaining a specific

TFBS is proportional to its frequency in the genome. The probability of a novel TFBS is (150 " total number of different TFBSs present)/(150# total

number of TFBSs). (4) One of the TFBSs of gene A is deleted. (C) A network is constructed by connecting genes that share TFBSs.

0 20 40 60 80 100Percentage protein identity

0.0

0.2

0.4

0.6

0.8

Frac

tion

coex

pres

sed

A

0 20 40 60 80 100Percentage protein identity

0

1

2

3

Avg

. sha

red

bind

ing

site

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B

Fig 2 | Coexpression between paralogues in experiments. (A) Fractions of

coexpressed paralogues calculated by correlation in coexpression in the

data set of Hughes et al (2000). (B) Average number of shared regulatory

elements between paralogues in the data set of Lee et al (2002).

Yeast coexpression network

V. van Noort et al.

&2004 EUROPEAN MOLECULAR BIOLOGY ORGANIZATION EMBO reports VOL 5 | NO 3 | 2004

scientificreport

3

Teichmann, S. A. & Babu, M. M. Gene regulatory network growth by duplication. Nat Genet 36, 492–496 (2004).

Conclusiones

Conclusiones

• Las redes de regulación son redes dirigidas

Conclusiones


• El número de genes regulados por un FT sigue una distribución de potencias

Conclusiones



• Presenta Motivos específicos FFL y Bi-Fan

Conclusiones




• Los motivos se seleccionan por sus propiedades cinéticas

Conclusiones





• Los sistemas de regulación están optimizados para la mejor expresión temporal y el menor gasto energético

Conclusiones






• La red cambia para adaptarse a el tipo de estímulo, y esto se consigue mediante el uso combinatorio de FT.

Conclusiones






• La red cambia para adaptarse a el tipo de estímulo, y esto se consigue mediante el uso combinatorio de FT.

• El modelo de evolución mediante duplicación y deriva parece el más plausible

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