Download The logic of plant signalling

Biological clocks in theory and experiments
Current:
Megan Southern
Laszlo Kozma-Bognar
Kieron Edwards
Vacancy !
Paul Brown
James Locke
Domingo Salazar
Ozgur Akman
Vacancy !
Past:
Simon Thain
Kamal Swarup
Ruth Bastow
Harriet McWatters
Shigeru Hanano
Seth Davis
Mandy Dowson-Day
Giovanni Murtas
Neeraj Salathia
Maria Eriksson
Anthony Hall
Alex Morton
Boris Shulgin
Nickiesha Bromley
Victoria Hibberd
www.amillar.org
Collaborators
IPCR Matthew Turner (Physics)
David Rand (Maths)
Bärbel Finkenstädt (Statistics)
Mark Muldoon and David
Broomhead (Manchester)
Lorenz Wernisch (Birkbeck)
Antony Dodd, Alex Webb,
Julian Hibberd
(Cambridge)
Ferenc Nagy (Szeged)
Eberhard Schäfer, Stefan
Kircher (Freiburg)
Mark Doyle, Scott Michaels,
Rick Amasino (Madison)
Graham King (HRI), Mike
Kearsey (Birmingham)
Funding: BBSRC, Gatsby, EPSRC, DTI
Genome-wide circadian rhythms
0h
Time in LL (h) 26 30 34 38 42 46 50 54 58 62 66 70 74
~ 12% RNA transcripts
rhythmic in white light
~ 3,000 genes of 22,000
on array
• Functional clustering
• 68% of rhythmic
transcripts also
stress-regulated,
(Kreps et al. 2002)
Edwards et al.
unpublished
Mutant plants identify genes in the clockwork
?
Alabadi et
al., 2001
• Negative regulation during the day - CCA1/LHY
• Positive regulation at night
• Mathematical model to test potential for regulation
Luciferase (LUC) reporter
CAB/ LHCB 
LUC protein code
camera
+
• Luminescence reflects transcription rate of promoter
• Unstable activity reports dynamic regulation
• Spatial resolution, high throughput
LUC: identifies mutants in clock genes
elf3-1
WT
elf3-7
1000
Photons/
seedling/
25mins
800
EARLY-FLOWERING 3
(elf3): arhythmic in light
CAB:LUC
rhythm
600
Hicks et al, Science, 1996
McWatters et al., Nature, 2000
Reed et al., Plant Phys. 2000
400
200
0
0
24
48
72
96
120
30000
Time (hours)
CCA1:LUC
rhythm
Counts/
sdlg/ 20000
second
EARLY-FLOWERING 4
(elf4): arhythmic in all conditions,
Fails to express CCA1
Doyle et al., Nature, 2000
WT
10000
elf4
0
0
12
24
36
Time (hours)
48
60
The circadian clock in Arabidopsis
Acute light response
cry1, 2
CAB
TOC1
phyA, B, D
+ 3600 genes
LHY
CCA1
morphology
Oscillator
Input
Overt
Rhythms
ELF3 zeitnehmer
(PHY/CRY rhythms)



Modelling
Design principles
Extension of network
Projects in circadian rhythms - Current
Data
Data analysis
Reporter genes
RNA (PCR, arrays)
Mutant plants
Data preparation
Rhythm detection
Parameter estimation
(MCMC, cost functions)
Clock mechanism.
Functions of
interlocking loops,
multiple light inputs
Understanding
Models
Central loop:
ODE, SDE,
Simplified, etc.
Predictions
Software
IRCs,
Flexibility
Model analysis
dLHYm =
dt
vT
TOCn4
kT + TOCn4
- vLCLHYm k1 + LHYm
kd1 LHYm
dLHYc = kLLHYm – kLCLHYc + kLPLHYn - vDCLHYc
dt
kLC + LHYc
dLHYn = kLCLHYc – kLPLHYn + vDNLHYn
dt
kLN + LHYn
dTOCm
dt
=
vTOC
kLHY + (LHYn)4
-
vDTTOCm - kd TOCm
kT + TOCm
dTOCc = kTOCTOCm – kTCTOCc + kTPTOCn - vDTTOCc
dt
kTC + TOCc
dTOCn = kTCTOCc – kTPTOCn + vDTPTOCn
dt
kTP + TOCn
Single-loop network model
Locke, Millar and Turner, J Theor Biol, 2005. Global parameter search.
Random
Sobol
Interlocking loop model for Arabidopsis clock
TOC1
cca1;lhy
TOC1
Y
Y
Time (h)
TOC1 mRNA
LHY
X
WT
TOC1 mRNA
LHY
LHY mRNA
X
LHY mRNA
• Model- J. Locke
• Hypothetical
components X, Y
• Cost function fit to
WT and mutant
behaviour
• Predicts X and Y
expression
Experiments to identify Y turn up a good candidate
• Prediction = dashed line
WT
• M. Southern tested
candidate genes by qRTPCR. Data = crosses
cca1;lhy
• Unexpected light
response of GIGANTEA
RNA matches prediction
• GI also matches other
predictions for Y from
literature
Projects in circadian rhythms - Current
Data
Data analysis
Data preparation
Rhythm detection
Parameter estimation
(MCMC, cost functions)
Understanding
Models
Central loop:
ODE, SDE,
Simplified, etc.
Model analysis
Data analysis: CAB:LUC in 16h L:8h D
Morton, Finkenstadt
raw
prepared
synthesis rate
Parameter estimation: simple model for synthesis rate
dY
  (t)   Y (t)
dt
Model: sde version of
p
IT e

%(t)  a0   ak cos  kt  bk sin  kt
k 1

 (t)  %(T )  cIT e where T  t  d mod L
Comparison of WT and elf3 mutant waveforms
WT
elf3
mutant
clock effect
• Quantify distinct features within the timeseries
• Now apply to parameters of simple clock models
Projects in circadian rhythms - Current
Data
Data analysis
Reporter genes
RNA (PCR, arrays)
Mutant plants
Data preparation
Rhythm detection
Parameter estimation
(MCMC, cost functions)
Clock mechanism.
Functions of
interlocking loops,
multiple light inputs
Understanding
Models
Central loop:
ODE, SDE,
Simplified, etc.
Predictions
Software
IRCs,
Flexibility
Model analysis
Projects in circadian rhythms – Future collaboration
Data
Data analysis
Reduced/synthetic
Fitting directly
systems
to multiple data types
Protein data
Network inference
(2-D gels)
Database (dynamic Bayes nets)
Biochemistry
(parameters)
Models
Photoreceptors,
secondary loops
Software
Stochastic processes
Noise (internal and external)
Understanding
Adding to model
Inverse problem
Model analysis