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Biological clocks in theory and experiments amillar

Biological clocks in theory and experiments www.amillar.org. Past : Simon Thain Kamal Swarup Ruth Bastow Harriet M c Watters Shigeru Hanano Seth Davis Mandy Dowson-Day Giovanni Murtas Neeraj Salathia Maria Eriksson Anthony Hall Alex Morton Boris Shulgin Nickiesha Bromley

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Biological clocks in theory and experiments amillar

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  1. Biological clocks in theory and experiments www.amillar.org 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 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) Current: Megan Southern Laszlo Kozma-Bognar Kieron Edwards Vacancy ! Paul Brown James Locke Domingo Salazar Ozgur Akman Vacancy ! Funding: BBSRC, Gatsby, EPSRC, DTI

  2. 0h Time in LL (h) 26 30 34 38 42 46 50 54 58 62 66 70 74 Genome-wide circadian rhythms ~ 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 unpublished

  3. Mutant plants identify genes in the clockwork • Negative regulation during the day - CCA1/LHY • Positive regulation at night • Mathematical model to test potential for regulation ? Alabadi et al., 2001

  4. CAB/ LHCB  LUC protein code camera Luciferase (LUC) reporter • Luminescence reflects transcription rate of promoter • Unstable activity reports dynamic regulation • Spatial resolution, high throughput +

  5. CAB:LUC rhythm CCA1:LUCrhythm WT EARLY-FLOWERING 4(elf4): arhythmic in all conditions,Fails to express CCA1 elf4 Doyle et al., Nature, 2000 LUC: identifies mutants in clock genes EARLY-FLOWERING 3(elf3): arhythmic in light Hicks et al, Science, 1996 McWatters et al., Nature, 2000 Reed et al., Plant Phys. 2000

  6. Acute light response cry1, 2 phyA, B, D CAB morphology ELF3 zeitnehmer (PHY/CRY rhythms) The circadian clock in Arabidopsis TOC1 + 3600 genes OvertRhythms LHYCCA1 Oscillator Input • Modelling • Design principles • Extension of network

  7. Predictions IRCs, Flexibility Projects in circadian rhythms - Current Data Data analysis Models Data preparation Rhythm detection Parameter estimation (MCMC, cost functions) Reporter genes RNA (PCR, arrays) Mutant plants Central loop: ODE, SDE, Simplified, etc. Software Clock mechanism. Functions of interlocking loops, multiple light inputs Understanding Model analysis

  8. dLHYm =vTTOCn4 - vLCLHYm - kd1 LHYm dt kT + TOCn4 k1 + LHYm dLHYc = kLLHYm – kLCLHYc + kLPLHYn - vDCLHYc dt kLC + LHYc dLHYn = kLCLHYc – kLPLHYn + vDNLHYn dt kLN + LHYn dTOCm =vTOC - vDTTOCm - kd TOCm dt kLHY + (LHYn)4 kT + TOCm dTOCc = kTOCTOCm – kTCTOCc + kTPTOCn - vDTTOCc dt kTC + TOCc dTOCn = kTCTOCc – kTPTOCn + vDTPTOCn dt kTP + TOCn

  9. Random Sobol Single-loop network model Locke, Millar and Turner, J Theor Biol, 2005. Global parameter search.

  10. Model- J. Locke Hypothetical components X, Y Cost function fit to WT and mutant behaviour Predicts X and Y expression X LHY TOC1 mRNA LHY mRNA LHY X TOC1 TOC1 TOC1 mRNA LHY mRNA Y Y Time (h) Interlocking loop model for Arabidopsis clock WT cca1;lhy

  11. Experiments to identify Y turn up a good candidate • Prediction = dashed line • M. Southern tested candidate genes by qRT-PCR. Data = crosses • Unexpected light response of GIGANTEA RNA matches prediction • GI also matches other predictions for Y from literature WT cca1;lhy

  12. Projects in circadian rhythms - Current Data Data analysis Models Data preparation Rhythm detection Parameter estimation (MCMC, cost functions) Central loop: ODE, SDE, Simplified, etc. Understanding Model analysis

  13. Data analysis: CAB:LUC in 16h L:8h D Morton, Finkenstadt raw prepared synthesis rate

  14. Model: sde version of Parameter estimation: simple model for synthesis rate

  15. Comparison of WT and elf3 mutant waveforms • Quantify distinct features within the timeseries • Now apply to parameters of simple clock models WT elf3 mutant clock effect

  16. Predictions IRCs, Flexibility Projects in circadian rhythms - Current Data Data analysis Models Data preparation Rhythm detection Parameter estimation (MCMC, cost functions) Reporter genes RNA (PCR, arrays) Mutant plants Central loop: ODE, SDE, Simplified, etc. Software Clock mechanism. Functions of interlocking loops, multiple light inputs Understanding Model analysis

  17. Adding to model Inverse problem Projects in circadian rhythms – Future collaboration Data Data analysis Models Reduced/synthetic systems Protein data (2-D gels) Biochemistry (parameters) Fitting directlyto multiple data types Network inference (dynamic Bayes nets) Database Photoreceptors, secondary loops Software Stochastic processes Noise (internal and external) Understanding Model analysis

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