1. Toward gene based crop simulation models for use in climate change studies SM Welch, A Wilczek, L Burghardt, JL Roe, B Moyer, R Petipas, M Cooper, J Schmitt, S Das, P Koduru, X Cai Template © www.brainybetty.com

2. 2 Species & Changing Climate I General warming advances spring & retards fall, altering the timing of many life cycle events; Few timing changes will be proportional; Prior inter-species synchronies will be broken and new ones formed.

3. 3 Day length varies by latitude in complex, seasonal ways; Day length sensitivity will vary by species; Effects may reinforce or offset temperature influences; Prior inter-species synchronies will be broken and new ones formed. Species & Changing Climate II

4. 4 Climate models take plant physiology into account; They allow the distribution of plants to vary according to plant competition; But plant response to the environment remains unaltered; There is no genetic change. Climate & Changing Species III

5. 5 Modeling a single gene Amount of gene product at time t Controlled by levels of upstream regulatory gene products Some fraction of M degrades per unit time Temperature

6. 6 Simplified Network Model SOC1 LFY FT “Autonomous Pathway” Floral Commitment Switch AP1 FLC Vernalization Pathway FRIVIN3 CO Clock Photoreceptors Photoperiod Pathway GI FVELD

7. 7 Simplified Network Model SOC1 LFY FT “Autonomous Pathway” Floral Commitment Switch AP1 FLC Vernalization Pathway FRIVIN3 CO Clock Photoreceptors Photoperiod Pathway GI FVELD

8. 8 Model fit to OsCO mRNA data 15 h 9 h 15 h 9 h Kojima et al. 2002

9. 9 Decoding Development Rate

10. 10 Data on Arabidopsis thaliana Reanalyzed by S. Welch Data from A. Giakountis and G. Coupland. Wilczek, et al, 2009.

11. 11 Assembling the Pieces: Gene Meta-mechanism Models Hour-by-hour Days Wilczek et al, Science, 13 Feb 2009

12. Accumulation to a Common Threshold Wilczek, et al, 2009. Copyright restrictions may apply

13. 13 Actual vs. Predicted Bolting Dates Copyright restrictions may apply Wilczek, et al, 2009.

14. 14 Sensitivity to Germination Timing Wilczek, et al, 2009. Copyright restrictions may apply

15. 15 Two Questions Can the path from basic phenotype and genomic data to meta-mechanisms and/or the corresponding networks be automated? Can incomplete/imperfect network models predict crosses well enough to enable “network assisted selection” outperform traditional methods?

16. 16 “Real” network One solution The method does not find just one solution but rather a set of plausible ones. The solutions may add/omit real genes, have them in the wrong orders or with the wrong functions. But how good are they?? Cai et al. Int. Jour. Bioinformatics Res. and Appl. (in press)

17. 17 Can network-assisted selection with approximate networks outperform phenotype and marker assisted selection based on the real network? Perhaps so But example is limited Next step: Real data

18. 18 Take-away Messages It is possible to quantify the combined effects of individual pathways in complex natural settings There are significant opportunities to synergize ecophysiological and gene network modeling to describe gene meta-mechanisms Phenology gene meta-mechanisms seem likely to be broadly applicable to across plant taxa. Gene meta-mechanisms may be machine- learnable and perhaps able to support efficient new crop improvement strategies.

19. 19 Thanks FIBR “Post bacs”: Lindsey Albertson, J. Franklin Egan, Laura Martin, Chris Muir, Sheina Sim, Alexis Walker, Jillian Anderson, Deren Eaton, Robert Schaeffer Clint Oakley Cristina Lopez-Gallego (UNO), Eric Von Wettberg Rosie Dent, Lisa Mandle, Emily Josephs NSF FIBR PROGRAM NSF FIBR collaborators: Michael Purugganan, Ian Ehrenreich, Yoshie Hanzawa, Megan Hall, Kitty Engelmann, Ana Caicedo, Christina Richards, A. Stathos (NYU) Rick Amasino, Chris Schwartz (Wisc.) C. Dean, Amy Strange, C. Lister (JIC), H. Kuittinen O. Savolainen (Oulu), G. Coupland, A. Giakountis, M. Koornneef (MPI, Cologne), M. Hoffmann (Martin Luther U.), M. Blázquez (Valencia), D. Weigel (MPI, Tübingen)