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Reconstruction of regulatory modules
based on heterogeneous data sources
Karen Lemmens
PhD Defence
September 29th 2008
Outline
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
1. Introduction & objectives
2. Strategy
– Data integration
– Association rule mining algorithms
3. Main achievements
– ReMoDiscovery: Unraveling the yeast transcriptional
network
– DISTILLER: Condition-dependent combinatorial regulation
in E. coli
4. Conclusions and perspectives
29 September 2008
PhD defence
Karen Lemmens
DNA
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
DNA & genes
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC
GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA
GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT
GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA
AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC
ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA
TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG
GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG
CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC
TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG
GENE 1
GENE 2
DNA
mRNA
protein
29 September 2008
PhD defence
Karen Lemmens
DNA & genes
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC
GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA
GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT
GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA
AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC
ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA
TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG
GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG
CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC
TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG
GENE 1
GENE 2
DNA
GENE 1
GENE 2
mRNA
protein
29 September 2008
PhD defence
Karen Lemmens
DNA & genes
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC
GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA
GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT
GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA
AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC
ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA
TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG
GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG
CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC
TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG
GENE 1
GENE 2
DNA
GENE 1
GENE 2
mRNA
protein
29 September 2008
PhD defence
Karen Lemmens
DNA & genes
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC
GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA
GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT
GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA
AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC
ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA
TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG
GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG
CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC
TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG
GENE 1
GENE 2
DNA
GENE 1
GENE 2
TRANSCRIPTION
mRNA
protein
29 September 2008
PhD defence
Karen Lemmens
DNA & genes
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC
GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA
GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT
GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA
AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC
ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA
TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG
GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG
CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC
TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG
GENE 1
GENE 2
DNA
GENE 1
GENE 2
TRANSCRIPTION
mRNA
TRANSLATION
protein
29 September 2008
PhD defence
Karen Lemmens
Condition-dependent transcription
1. Introduction
DNA
2. Strategy
3. Achievements
4. Conclusions
GENE 1
mRNA
protein
29 September 2008
PhD defence
Karen Lemmens
Condition-dependent transcription
1. Introduction
DNA
2. Strategy
3. Achievements
4. Conclusions
GENE 1
mRNA
protein
29 September 2008
PhD defence
Karen Lemmens
Condition-dependent transcription
1. Introduction
DNA
2. Strategy
3. Achievements
4. Conclusions
GENE 1
TRANSCRIPTION
mRNA
TRANSLATION
protein
29 September 2008
PhD defence
Karen Lemmens
Condition-dependent transcription
1. Introduction
DNA
2. Strategy
3. Achievements
GENE 1
4. Conclusions
GENE 1
TRANSCRIPTION
mRNA
TRANSLATION
protein
29 September 2008
PhD defence
Karen Lemmens
Condition-dependent transcription
1. Introduction
DNA
2. Strategy
3. Achievements
GENE 1
4. Conclusions
GENE 1
TRANSCRIPTION
mRNA
TRANSLATION
protein
29 September 2008
PhD defence
Karen Lemmens
Condition-dependent transcription
1. Introduction
DNA
2. Strategy
3. Achievements
GENE 1
4. Conclusions
GENE 1
TRANSCRIPTION
mRNA
TRANSLATION
protein
29 September 2008
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Regulatory motifs
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Regulatory motifs
GENE 1
Regulators
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Regulatory motifs
GENE 1
Regulators
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Regulatory motifs
GENE 1
Regulators
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Regulatory motifs
GENE 1
Regulators
GENE 1
29 September 2008
GENE 1
PhD defence
Karen Lemmens
Transcriptional regulation
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Regulatory motifs
GENE 1
Regulators
GENE 1
29 September 2008
GENE 1
PhD defence
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Transcriptional modules
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
Outline
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
1. Introduction & objectives
2. Strategy
– Data integration
– Association rule mining algorithms
3. Main achievements
– ReMoDiscovery: Unraveling the yeast transcriptional
network
– DISTILLER: Condition-dependent combinatorial regulation
in E. coli
4. Conclusions and perspectives
29 September 2008
PhD defence
Karen Lemmens
Data integration
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Data integration
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
ChIP-chip data
GENE 1
29 September 2008
PhD defence
Karen Lemmens
Data integration
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
ChIP-chip data
GENE 1
Regulatory motifs
29 September 2008
PhD defence
Karen Lemmens
Data integration
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
ChIP-chip data
Microarray data
GENE 1
Regulatory motifs
29 September 2008
PhD defence
Karen Lemmens
Network reconstruction
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Several methods for reconstruction of the transcriptional network
exist
Expression data
Individual
interactions
Transcriptional
modules
Boolean
ODE
Bayesian
Association (CLR, ARACNE)
Clustering
Biclustering
Query-driven biclustering
Method of Segal et al.
LeMoNe
Data integration
Bayesian
SEREND
GRAM
SAMBA
COGRIM
MA-Networker
Inferelator
Not all aspects of transcription taken into account by these
methods
** Van den Bulcke T., Lemmens K., Van de Peer Y., Marchal K. (2006) Inferring Transcriptional Networks by Mining Omics Data. Current
Bioinformatics, vol. 1, no. 3, pp. 301-313.
** Dhollander T., Sheng Q., Lemmens K., De Moor B., Marchal K., Moreau Y. (2007) Query-driven module discovery in microarray data.
Bioinformatics, vol. 23, no. 19, pp. 2573-2580.
29 September 2008
PhD defence
Karen Lemmens
Association rule mining
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Association rule mining algorithms
– Advantages:
•
•
•
•
Enable exhaustive search
Elegant and concurrent data integration
No co-expression assumption between regulator and target
Overlapping modules
– Problems
• Binary or discretized data
• Filtering method necessary
29 September 2008
PhD defence
Karen Lemmens
Outline
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
1. Introduction & objectives
2. Strategy
– Data integration
– Association rule mining algorithms
3. Main achievements
– ReMoDiscovery: Unraveling the yeast transcriptional
network
– DISTILLER: Condition-dependent combinatorial regulation
in E. coli
4. Conclusions and perspectives
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
29 September 2008
2. Strategy
3. Achievements
PhD defence
4. Conclusions
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Represent data in a mathematical way
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Transcriptional module
– Genes are regulated by a minimum number of regulators
– Genes share minimum number of common regulatory
motifs
– Genes are co-expressed
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Transcriptional module
– Genes are regulated by a minimum number of regulators
– Genes share minimum number of common regulatory
motifs
– Genes are co-expressed
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Transcriptional module
– Genes are regulated by a minimum number of regulators
– Genes share minimum number of common regulatory
motifs
– Genes are co-expressed
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Transcriptional module
– Genes are regulated by a minimum number of regulators
– Genes share minimum number of common regulatory
motifs
– Genes are co-expressed
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Regulatory program:
Regulators:
Motifs:
MBP1
SWI4
SWI6
STB1
• Co-expressed genes:
YDL003W
YGR221C
YNL300W
YMR199W
YKL113C
29 September 2008
YER001W YGR109C YGR189C
YHR149C YER070W YPL256C
YPL163C YPL267W YPR120C
YMR199W YMR179W YML027W
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
• ReMoDiscovery outperforms
module detection
related
4. Conclusions
methods
for
– GRAM
– SAMBA
• Conclusions
– Meaningful biological results
– Better performance than related methods
association rule mining algorithms are well suited for
identification of regulatory modules through data integration
Lemmens K., Dhollander T., De Bie T., Monsieurs P., Engelen K., Smets B., Winderickx J., De Moor B., Marchal K. (2006) Inferring
transcriptional module networks from ChIP-chip-, motif- and microarray data. Genome Biology, vol. 7, no. 5, pp. R37.
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Many aspects of transcription into account:
– Regulatory motifs
– Regulators
– Co-expression of genes
Condition dependency of the interactions is missing
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Many aspects of transcription into account:
– Regulatory motifs
– Regulators
– Co-expression of genes
Condition dependency of the interactions is missing
29 September 2008
PhD defence
Karen Lemmens
ReMoDiscovery:
Unraveling the yeast transcriptional network
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Many aspects of transcription into account:
– Regulatory motifs
– Regulators
– Co-expression of genes
Condition dependency of the interactions is missing
29 September 2008
PhD defence
Karen Lemmens
Outline
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
1. Introduction & objectives
2. Strategy
– Data integration
– Association rule mining algorithms
3. Main achievements
– ReMoDiscovery: Unraveling the yeast transcriptional
network
– DISTILLER: Condition-dependent combinatorial regulation
in E. coli
4. Conclusions and perspectives
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
• ReMoDiscovery:
3. Achievements
4. Conclusions
• DISTILLER:
– Co-expression in all
conditions by correlation
– Condition dependency:
bandwidth concept
– Apriori algorithm
– CHARM algorithm
– No filtering procedure
– Filtering procedure to
identify the most
interesting modules
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
Pastor D., Cortes-Calabuig A., Lemmens K., De Moor B., Marchal K., Denecker M. (2007) GeneReg: Integration of Experimental Data on
the DNA Transcription Process. Proceedings of BNAIC 2007.
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Example: FNR module
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Example: FNR module
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Example: FNR module
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• FNR = one of the most extensively studied regulators
• Experimental validation of novel FNR targets
– High confidence:
•
•
•
•
ydhY (b1674)
yfgG (b2504)
hscC (b0650)
treF (b3519)
Partridge et al, 2008
– Medium confidence:
•
•
•
•
•
•
•
29 September 2008
yjhB (b4279)
ydjX (b1750)
yjtD (b4403)
ydaT (b1358)
yehD (b2111)
yhjA (b3518)
ftnB (b1902)
Partridge et al, 2007
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Condition dependency
– Arrays were grouped into
conditional categories
– Colors show to what extent the
conditions of the modules of a
particular regulator are
enriched for a specific category
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Combinatorial regulation
– Static
– Highly combinatorial:
• 42 regulons
• 66 complex regulons
• 70 complex regulons
29 September 2008
one regulator
two regulators
three or more regulators (maximum
of 8)
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Combinatorial regulation at the module level
• Lower combinatorial complexity
• 25/150 modules
at least two regulators (maximum of 3)
• 24 modules involve at least one global regulator such as CRP,
FNR or ArcA
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Combinatorial regulation at connector gene level
One regulator may be sufficient to alter the expression of a
connector gene upon a specific environmental cue
29 September 2008
PhD defence
Karen Lemmens
DISTILLER:
Condition-dependent combinatorial regulation in E. coli
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Conclusions
– Reliable predictions
– Dynamic view on the network
– Combinatorial regulation
** Lemmens K., De Bie T., Dhollander T., Monsieurs P., De Moor B., Collado-Vides J., Engelen K., Marchal K. (2008) The conditiondependent transcriptional network in Escherichia coli. Accepted for publication in Annals of NYAS, DREAM2.
** Lemmens K., De Bie T., Dhollander T., De Keersmaecker S., Thijs I., Schoofs G., De Weerdt A., De Moor B., Vanderleyden J., ColladoVides J., Engelen K., Marchal K. (2008) DISTILLER: a data integration framework to reveal condition dependency of complex regulons in
Escherichia coli. Submitted to Genome Biology.
29 September 2008
PhD defence
Karen Lemmens
Outline
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
1. Introduction & objectives
2. Strategy
– Data integration
– Association rule mining algorithms
3. Main achievements
– ReMoDiscovery: Unraveling the yeast transcriptional
network
– DISTILLER: Condition-dependent combinatorial regulation
in E. coli
4. Conclusions and perspectives
29 September 2008
PhD defence
Karen Lemmens
Conclusions
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Main contributions of this thesis:
– Automated collection of data
– ReMoDiscovery
– DISTILLER
• Goals obtained via:
– Data integration
– Association rule mining algorithms
well suited for
data integration and reconstruction of transcriptional
network
• Several algorithmic problems were solved
• Novel biological findings
29 September 2008
PhD defence
Karen Lemmens
Perspectives
1. Introduction
2. Strategy
3. Achievements
4. Conclusions
• Conceptual extensions:
– Inclusion of other data sources
• Additional motifs from de novo motif detection
• Small RNAs
– Comparison of networks
• Implementation-related and algorithmic improvements:
–
–
–
–
User-friendly interface
Microarray compendium
Filtering step
Motif detection algorithms
29 September 2008
PhD defence
Karen Lemmens
Acknowledgements
1. Introduction
2. Strategy
• CMPG - BioI
3. Achievements
• CMPG
– Prof. Dr. K. Marchal
– BioI group
• ESAT/SCD – BioI
4. Conclusions
– Prof. Dr. J. Vanderleyden
– Dr. S. De Keersmaecker
• Computer Sciences
– Prof. Dr. B. De Moor
– Prof. Dr. Y. Moreau
– BioI group
– Prof. Dr. M. Denecker
– A. Cortés Calabuig
– Prof. Dr. T. De Bie
– Prof. Dr. J. Collado-Vides
29 September 2008
PhD defence
Karen Lemmens