Redes de regulación génica - CSICcsbg.cnb.csic.es › master2012 › keynote ›...
Transcript of Redes de regulación génica - CSICcsbg.cnb.csic.es › master2012 › keynote ›...
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Redes de regulación génica
David Ochoa García
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1RUN
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1RUN
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1LBG
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1LBG
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http://phet.colorado.edu/sims/gene-network/gene-machine-lac-operon_en.jnlp
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Represor Activador
Redes de regulación génica
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RegulonDBBPP evidences 2012
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RegulonDBBPP evidences 2012
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Obteniendo Evidencias
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Evidencias experimentales (genome-wide)
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Evidencias experimentales (genome-wide)
• Chip-on-Chip / Chip-Seq
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Evidencias experimentales (genome-wide)
• Chip-on-Chip / Chip-Seq
• STAGE/SABE
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Evidencias experimentales (genome-wide)
• Chip-on-Chip / Chip-Seq
• STAGE/SABE
• DNA arrays
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SABE
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DNA arrays
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Predicciones computacionales
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Predicciones computacionales
• Pattern Discovery
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Predicciones computacionales
• Pattern Discovery
• Pattern Matching
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Reverse engineering (genome-wide)
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Reverse engineering (genome-wide)
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Reverse engineering (genome-wide)
Hartemink, A. J. Reverse engineering gene regulatory networks. Nat Biotechnol 23, 554–555 (2005).
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Bases de Datos
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Bases de Datos
http://regulondb.ccg.unam.mx/
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Bases de Datos
http://regulondb.ccg.unam.mx/
http://www.biobase-international.com/product/transcription-factor-binding-sites
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Propiedades topológicas de las Redes de Regulación génica
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Degree distribution
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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
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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
p(k)=CekDistribución exponencial
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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
Genes reguladores
p(k)=Ck-gDistribución “Ley de Potencias”
p(k)=CekDistribución exponencial
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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).
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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).
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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).
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Motivos
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Motivos
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Motivos
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).
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Motivos
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).
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Motivos en redes de regulación
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Motivos en redes de regulación
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).
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Motivos solapantes
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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
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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
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Motivos en otros tipos de redes
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Motivos en otros tipos de redes
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).
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Perfiles de Motivos
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Perfiles de Motivos
Milo, R. et al. Superfamilies of evolved and designed networks. Science 303, 1538–1542 (2004)
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Tipos de FFLs
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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).
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Tipos de FFLs
FFL coherentes
FFL incoherentes
Mangan, S. & Alon, U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100, 11980–11985 (2003).
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Operators
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Operators
ANDORSUM
Mangan, S. & Alon, U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100, 11980–11985 (2003).
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Propiedades de los motivos
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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)
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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)
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Propiedades de los motivos
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Propiedades de los motivos
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)
AND
ope
rato
r
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Propiedades de los motivos
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)
AND
ope
rato
rO
R op
erat
or
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Propiedades de los motivos
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Propiedades de los motivos
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)
FFL incoherentes
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Propiedades de los motivos
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)
FFL incoherentes
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Propiedades de los motivos
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007)
FFL incoherentes
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Motivos importantes en el desarrollo
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Motivos importantes en el desarrollo
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007).
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Motivos importantes en el desarrollo
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007).
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Motivos importantes en el desarrollo
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007).
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Motivos SIM (Single Output Models)
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Motivos SIM (Single Output Models)
Alon, U. Network motifs: theory and experimental approaches. Nat Rev Genet 8, 450–461 (2007).
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Optimización temporal
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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).
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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).
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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).
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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. & Alon, U. Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell 117, 713–720 (2004).
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Optimización en gasto
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Optimización en gasto
Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nat Genet 36, 486–491 (2004).
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Dinámica de las Redes de Regulación
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Dinámica de la red
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Dinámica de la red
Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).
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Dinámica de la red
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Dinámica de la red
Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).
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Dinámica de la red - Hubs
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Dinámica de la red - Hubs
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Evolución de las redes de regulación
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¿Evolución convergente?
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¿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).
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¿Evolución convergente?
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¿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).
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Evolución por duplicación
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Evolución por duplicación
Teichmann, S. A. & Babu, M. M. Gene regulatory network growth by duplication. Nat Genet 36, 492–496 (2004).
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Simulando la evolución
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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
s
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).
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Simulando la evolución
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Simulando la evolución
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).
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Conclusiones
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Conclusiones
• Las redes de regulación son redes dirigidas
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Conclusiones
• Las redes de regulación son redes dirigidas
• El número de genes regulados por un FT sigue una distribución de potencias
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Conclusiones
• Las redes de regulación son redes dirigidas
• El número de genes regulados por un FT sigue una distribución de potencias
• Presenta Motivos específicos FFL y Bi-Fan
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Conclusiones
• Las redes de regulación son redes dirigidas
• El número de genes regulados por un FT sigue una distribución de potencias
• Presenta Motivos específicos FFL y Bi-Fan
• Los motivos se seleccionan por sus propiedades cinéticas
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Conclusiones
• Las redes de regulación son redes dirigidas
• El número de genes regulados por un FT sigue una distribución de potencias
• Presenta Motivos específicos FFL y Bi-Fan
• Los motivos se seleccionan por sus propiedades cinéticas
• Los sistemas de regulación están optimizados para la mejor expresión temporal y el menor gasto energético
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Conclusiones
• Las redes de regulación son redes dirigidas
• El número de genes regulados por un FT sigue una distribución de potencias
• Presenta Motivos específicos FFL y Bi-Fan
• Los motivos se seleccionan por sus propiedades cinéticas
• Los sistemas de regulación están optimizados para la mejor expresión temporal y el menor gasto energético
• La red cambia para adaptarse a el tipo de estímulo, y esto se consigue mediante el uso combinatorio de FT.
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Conclusiones
• Las redes de regulación son redes dirigidas
• El número de genes regulados por un FT sigue una distribución de potencias
• Presenta Motivos específicos FFL y Bi-Fan
• Los motivos se seleccionan por sus propiedades cinéticas
• Los sistemas de regulación están optimizados para la mejor expresión temporal y el menor gasto energético
• 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