Plant Genomics and

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Plant Genomics and Mathematical modelling a
recipe for Systems Biology
Martin Kuiper Computational Biology
Division Department of Plant Systems
Biology VIB/UGent http//www.psb.ugent.be/cbd

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to exploit the revolution in plant genomics
by understanding the function of all genes of a
reference species within their cellular,
organismal and evolutionary context by the year
2010.
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Arabidopsis thaliana
Nuclear genome 125 Mb c. 29,000 genes
20 experimental data about function 48 predictab
le function 32 unknown function

• Need to
• - speed up gene function discovery
• - conduct genome-scale analyses
• - develop tools and resources

• Study gene network systems
• rather than single genes

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Why do we need Systems Biology?

• We are pretty good in identifying the parts of
  an organism
• Genomics, Functional Genomics genes, gene
  products
• An organism is more than the sum of its parts It
  is not the primary sequence that gives rise to
  biological forms and functions, but the dynamical
  behaviour of these parts.
• We need to record the dynamical behaviour of the
  parts
• We need to do this by systematically perturbing a
  biological system, and recording characteristic
  changes of the parts
• Mathematical modelling should reconcile an in
  silico gene interaction model with the observed
  dynamics

Understanding the dynamics of a biological system
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Plant Systems Biology
Biology
Systems Biology
Functional Genomics
Bioinformatics
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Topics

• Plant Functional Genomics (at PSB)
• CATMA, CAGE, AGRIKOLA
• Modelling
• Analysis of Compendium data
• SIM-plex mathematical modeller

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CATMAComplete Arabidopsis Transcriptome
MicroArray
Goal Construction of a collection of Gene-
specific Sequence Tags (GSTs)
representing most Arabidopsis genes
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CATMA covers a segment of clone-based Functional
Genomics
Promoter
ORF
GST
Reporter fusion Transactivation Molecular
interaction ChIP-chip
ORFeome Protein interaction Fluorescence
tagging HTP biochemistry Activation
screening Complementation
Transcript profiling RNAi-based gene silencing
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CATMA
Gene Specific Tag (GST) Design
primer3
BLASTn
SPADS
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• gene models
• Eugene
• TIGR3.0, 5.0

GST collection today 24,576 On CATMA v2 array
22,366 v3 under construction Currently 6000 new
GSTs
http//www.catma.org
Thareau et al (2003) Bioinformatics 19 2191-2198
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CATMA Benchmarking Dose-response curves
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CAGE Compendium of Arabidopsis Gene Expression

• EU FP5 Demonstration Project
• Started 1 November 2002
• Aims
• Exploit CATMA v2, v3 arrays for Arabidopsis
  transcriptome analysis
• Process a total of 2000 samples on 4000 arrays
• Implement common standards for sample growth and
  preparation, datarecording and processing, across
  laboratories
• Deliver a prototype Compendium reference database
  in ArrayExpress
• Supplement Compendium data with precomputed
  results (gene-specific significance, clustering
  results, etc)

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CAGE Standards samples
Large redundancy in samples
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Types of samples
Ecotypes 560 28 Stress
378 19 Mutants 482 24 Research
580 29 Total 2000
Today 30 done (hybridised, pre-processed
and uploaded)
http//www.cagecompendium.org
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CATMA Consortium Complete Arabidopsis
Transcriptome MicroArray
Department of Plant Systems Biology Pierre
Hilson, Pierre Ghent University - VIB,
Belgium Rouzé, Marc Zabeau Unité de Recherche
en Génomique Végétale (Génoplante) Jean-Pierre
Renou INRA/CNRS - Evry, France Michel
Caboche VIB Microarray Facility Paul Van
Hummelen Leuven, Belgium Max Planck Institut für
Moleculare Genetik (GABI) Wilfried
Nietfeld Berlin, Germany Hans
Lehrach Genomic Arabidopsis Resource Network
(GARNET) Jim Beynon, Mark United
Kingdom Crow, Martin Trick NWO Program
Functional Genomics of A. thaliana Peter
Weisbeek University of Utrecht - The
Netherlands Microarray Core Facility Philippe
Reymond University of Lausanne -
Switzerland Ed Farmer Departamento de Genetica
Molecular de Planta Javier Paz-Ares Centro
Nacional de Biotecnologica - Madrid, Spain Umeå
Plant Science Center Rishi Bhalerao Umeå
Sweden Goran Sandberg
http//www.catma.org
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AGRIKOLA Arabidopsis genomic RNAi knock-out line
analysis
Introns
GST
GST
Constitutive (35S)
inducible
at least 20,000 genes
Transform Arabidopsis with 4,000 of these plasmids
http//www.agrikola.org
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Targeted gene silencing using RNAi
RNAi
hpRNA
only a few transformants per gene required
phenotypes can easily be studied in different
ecotypes/genotypes
silencing of essential genes can be studied using
conditional promoters
plants with a range of phenotypes can be obtained
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Preliminary results

• Over 20,000 hairpin RNA expression vectors were
  produced via Gateway (Invitrogen) recombinational
  cloning technology
• pAGRIKOLA/GST-induced phenotypes
• can copy known knockout mutants
• can be obtained for essential genes
• can give insight into the functions of
• unstudied genes

Hilson et al (2004) Genome Research 14, 2176-2189
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Magdalena Weingartner, Karin Köhl, Melanie Lück,
Thomas Altmann Universität Potsdam, Institut für
Biochemie und Biologie, -Genetik-, c/o
Max-Planck-Institut für molekulare
Pflanzenphysiologie, Am Mühlenberg 1, 14476 Golm,
Germany Rebecca De Clercq, Ryan Whitford,
Mansour Karimi, Caroline Buysschaert, Rudy
Vanderhaeghen , Raimundo Villarroel, Pierre
Hilson Department of Plant Systems Biology, VIB,
Ghent, Belgium Alexandra Tabrett, Jennie Rowley,
Sharon Hall, Jim Beynon Warwick HRI,
Wellesbourne, Warwick CV359EF, UK Vasil
Chardakov, Wendy Byrne, Mark Bennet, Murray
Grant Department of Agricultural Science,
Imperial College London, Wye Campus, Ashford TN25
5AH, UK Andéol Falcon de Longevialle, Alexandra
Avon, Beate Hoffmann, Céline Léon, Anne Marmagne,
Fanny Marquer, Claire Lurin, Ian Small UMR
Génomique Végétale (INRA/CNRS/UEVE), Evry,
France Antonio Leyva, Maria Dolores Segura,
Yolanda Fernandez, Javier Paz-Ares Department of
Plant Molecular Genetics, Centro Nacional de
Biotecnología, 28049-Madrid, Spain With special
thanks to the CATMA consortium Chris Helliwell,
Peter Waterhouse (CSIRO, Canberra) Ian Moore
(Oxford University)
AGRIKOLA is funded by the FP5 grant
QLRT-2001-01741
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The Cycle of Systems Biology
Functional Genomics and Biology
Biocomputing
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Top-down and bottom-up modelling
top-down
bottom-up
Biological Process
Predictive mathematical model
Genome-scale functional genomics data
Knowledge Mathematics
Statistics Mining
Gene network components
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Computational Biology

• Top-down modelling

• Bottom-up modelling

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Yeast Microarray Data Compendium
Genes
Experiments
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Combinatorial statistic
experiments
Gene A
Gene B
Discretise up/down/undecided (based on ratios
or p-values)
Gene A
Gene B
similarity
Similarity between profiles can be measured
either by Pearson correlation coefficient or
considered a combinatorial problem What is the
chance that partial identity between two patterns
occurs by chance? p-values
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Clustering strategy
Genes

• correlation over subset of conditions
• p-values
• overlapping clusters
• networks, hubs
• natural visualisation

Experiments
Gene profiles
Comb. p-value (corrected) lt 0.01
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CS - responsive genes
BiNGO Maere et al., 2005
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Some examples

• Top-down modelling

• Bottom-up modelling

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http//www.psb.ugent.be/cbd/papers/sim-plex/
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Mathematical model
Approximation gene activation is simplified
to a step function
Piecewise Linear Differential Equation
(PLDE) summation of step-ups
step-downs (plus 1 degradation term)
activation threshold
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(No Transcript)
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www.psb.ugent.be/cbd
/papers/sim-plex
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Mathematical model of the cell division cycle of
fission yeast
Novak, Pataki, Ciliberto, Tyson (2000), Chaos
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KRP2 transition of mito. to endo.
Show-case in study of KRP2 involvement
in transition from mitotic division to endocycle.
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Wild-type Arabidopsis
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Dominant negative CDKB11
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KRP2 overexpression
Verkest et al., Plant Cell 17 2005
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Conclusions

• Functional Genomics data and resources are
  essential for systems biology
• Information extraction and integration needs to
  be further facilitated
• Mathematical modelling doesnt have to be
  rigorously accurate, it is already great if it
  can extend the capability to hypothesize

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Acknowledgements

• Computational Biology Division
• Steven Maere
• Steven Vercruysse
• Gert Sclep
• Functional Genomics
• Pierre Hilson
• Cell Cycle
• Lieven De Veylder, Dirk Inze
• Leaf Growth and Development
• Gerrit Beemster
• ESAT KU Leuven
• Joke Allemeersch, Steffen Durinck