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Xinguang Zhu 1,2

Combining evolutionary algorithm and plant systems models to identify new targets to engineer higher photosynthetic efficiency. Xinguang Zhu 1,2 1.Plant Systems Biology Group, Partner Institute of Computational Biology, Chinese Academy of Sciences/Max Planck Society

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Xinguang Zhu 1,2

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  1. Combining evolutionary algorithm and plant systems models to identify new targets to engineer higher photosynthetic efficiency Xinguang Zhu1,2 1.Plant Systems Biology Group, Partner Institute of Computational Biology, Chinese Academy of Sciences/Max Planck Society 2. Shanghai Institute of Plant Physiology and Ecology, SIBS, CAS

  2. Road Map • Improving photosynthesis is the key to solve many burning problems in our society • Systems biology combined with systems model can help identify new targets to engineer higher photosynthesis. • ePlant will dramatically increase our capacity to improve crop productivity. • ePlant can be used to merge various concentrations of computational biology.

  3. World Population Growth Currently: 6.6 bn Year 2100: 10 bn The United Nations

  4. 水稻产量的变化趋势 China 113 kg/ha/yr World 52 kg/ha/yr (FAO, 2008)

  5. Total solar energy Conversion efficiency  S i c Partitioning efficiency Interception efficiency What determines harvested yield? Wh = For modern cultivars of the major food crops i = 90% and  = 60%; but c = ca. 0.5% Harvested yield Monteith (1977) Philosophical Transactions of the Royal Society of London, 281 277-294

  6. 6% 4.6% Zhu et al (2008) Current Opinion in Biotechnology

  7. What ec is achieved in the field? • The highest ec over a whole growing season: • C3: 2.4% • C4: 3.7% • Common ec over a whole growing season: • < 0.5% Reviewed in: Zhu et al (2008) Current Opinion in Biotechnology (In press)

  8. How to increase efficiency? Systems approach!

  9. Systems Biology “Systems biology is the science of discovering, modeling, understanding and ultimately engineering at the molecular level the dynamic relationships between the biological molecules that define living organisms.” Leroy Hood, Ph. D, M.D. President, Institute of Systems Biology

  10. http://www.biologycorner.com/resources/photosynthesis-overview.gifhttp://www.biologycorner.com/resources/photosynthesis-overview.gif http://www.bio.umass.edu/biology/conn.river/photosyn.html Biomass Photosynthesis CO2+ H2O  Carbohydrate

  11. Model of carbon metabolism 13 Ru5P Ru5P Ru5P Ru5P Ru5P Ru5P ATP ATP ADP ADP 11 11 12 12 Ri5P Ri5P Xu5P Xu5P 10 10 Starch Starch S7P S7P 12 12 Pi Pi 9 9 25 25 PPi PPi Pi Pi RUBP RUBP SBP SBP Stroma 23 23 CO CO 8 8 2 2 ATP ATP E4P E4P Xu5P Xu5P ADPG ADPG O O 2 2 1 1 7 7 G1P G1P 111 111 22 22 21 21 PGCA PGCA F6P F6P G6P G6P PGA + PGA PGA + PGA Pi Pi 6 6 PGA PGA ATP ATP 112 112 FBP FBP 2 2 ADP ADP 5 5 GAP GAP DHAP DHAP GAP GAP NADPH NADPH Pi Pi NADP+Pi NADP+Pi +H +H 113 113 ADP ADP DHAP DHAP GAP GAP DHAP DHAP DPGA DPGA ATP ATP GAP GAP GCA GCA 4 4 3 3 GCEA GCEA Pi Pi Pi Pi Pi Pi 101 31 31 33 32 101 Pi Pi GCEA GCEA GCA GCA Pi Pi Pi Pi O O Sink DHAP DHAP GAP GAP 2 2 PGA PGA NADH NA 121 121 123 123 H H O O 2 2 2 2 + NAD OP GOA GOA HPR HPR 57 F6P SUCP SUC GLY GLY GLU GLU 122 122 124 124 62 56 KG KG GOA GOA 52 53 54 Sink FBP F6P G6P G1P UDPGlu SER SER GLY GLY UDP 131 131 UDP + + GLY + NAD GLY + NAD CO CO + NADH + NADH OP 2 2 ATP 55 55 58 59 60 61 61 OPOP 2OP UTP ADP Cytosol, mitochondria, and peroxisome F26BP Drawn based on Zhu et al (2007) Plant Physiology 145: 513-526

  12. Validations Enzyme activities Mechanistic Model Physiology Zhu et al (2007) Plant Physiology

  13. Rubisco SBPase Aldolase Flux Control Coefficient FBPase Flux control coefficient 400 [CO2] 700 200 Response coefficient Elasticity coefficient Concentration control coefficient Which enzyme is “limiting” photosynthesis? Enzyme

  14. More nitrogen fertilizer to overcome enzyme limitations? • Environmental cost • Economic cost Not Sustainable!

  15. An evolutionary algorithm for nitrogen optimization • Mimics the process of natural selection • Selection pressure: higher photosynthesis rate

  16. Theoretical optimal concentrations of enzymes in carbon metabolism Zhu et al (2007) Plant Physiology

  17. Raines (2003)Photosynthesis Research

  18. Wild plants versus designed crops (1) The Calvin Cycle Photo-respiratory pathway Photo-respiratory pathway The Calvin Cycle Designed final leaf Beginning leaf 25 oC Well watered conditions

  19. Wild plants versus designed crops (2) The Calvin Cycle Photo-respiratory pathway Photo-respiratory pathway The Calvin Cycle Designed final leaf Beginning leaf 45 oC Drought

  20. Wild plants versus designed crops (3)

  21. IPCC 2001 Global Change

  22. Higher Photosynthesis Selection Pressure Domestication Habitat change /www.agronomy.ucdavis.edu/gepts/pb143/lec08

  23. Systems Biology and Synthetic Biology Systems Biology:Resource use efficiency, optimality, plasticity, environmental stochasticity and heterogeneity, genetic constraints … … Mathematical Models + Evolutionary algorithms Synthetic Biology:New pathway design, new genetic regulatory network design , redesign existing parts, devices, systems etc … …

  24. Engineering photorespiratory bypass leading to substantial increase in photosynthesis. Dr. Vincent Devloo • The saving of ATP from decreased release of NH4+ release did not contribute to the increase in photosynthesis. • Releasing CO2 in chloroplast is key to engineer photorespiratory bypass Devloo et al (In Prep)

  25. Metabolism Model 3D Reaction Diffusion Photosynthesis Model http://www.olympusfluoview.com/theory/images/theoryheader.jpg Anatomy and photosynthesis Dr. Danny Tholen • Mesophyll cell arrangement • Stomata arrangement and patchiness of openness • Chloroplast arrangements and movements • Biochemical differentiation across these different layers • Adaptation under diverse environments and for different species

  26. ePlant How can we systematically identify all the potential options to increase photosynthesis of different crops under different conditions?

  27. Photosynthesis, respiration and nitrogen metabolism are closely linked. Foyer et al 2009 ARPB

  28. Strong interactions exist between source sink tissues. Moore et al 1999 PCE

  29. Feedback control of photosynthesis Rolland et al 2006 ARPB

  30. The stomata aperture is regulated through a complex network of signaling pathways, representing a key mechanism of mediating plant responses to environmental change. O3 CO2 Temperature Shimazaki et al 2007 ARPB Shimazaki et al 2007 ARPB http://www.borg.com/~lubehawk/lflayrs.jpg

  31. 1 1 2 2 3 3 Canopy microclimates are highly heterogeneous.

  32. We need a mechanistic model • The model includes: • The plant primary metabolism and its key regulation, water movement through the soil-stem-leaf-atmosphere continuum, nitrogen assimilation etc. • H2O and assimilate transport between plant organs • Microenvironments inside canopy

  33. e-Plant concept

  34. e-Plant concept PICB

  35. Features of the ePlant model • Limited to the plant primary metabolism (photosynthesis, respiration, nitrogen and water uptake and transport) and its key regulation • Multiscale (Spatial and temporal) • Multiphysics • Discrete and continuous processes occur simultaneously

  36. C4 RICE A C4 rice could increase rice yield by 30% -50%, double water-use efficiency and use much less fertilizer to achieve those improvements. No other evolutionary mechanism exists that could be added to a C3 rice so as to deliver that superior combination of benefits.

  37. Source of Scientific Optimism: Evolutionary Addition to C3 Produces C4? C3 Anatomy Change Biochem Change Fine Tuning C4 + + + = C4 evolved independently >30 times in 18 taxonomic families of angiosperms, and occurs in 8,000 - 10,000 species(*) (*) Hibberd & Quick, Nature, 2002 Our projects: C4 photosynthesis model, regulation, and enzyme evolution

  38. Conclusions • Improving photosynthesis is the key to solve many burning problems in our society • Systems biology combined with systems model can help identify new targets to engineer higher photosynthesis. • ePlant will dramatically increase our capacity to improve crop productivity. • ePlant can be used as a model system to study various concentrations of computational biology.

  39. ACKNOWLEDGEMENTS Vincent Devloo Danny Tholen GuiLian Zhang FuQiao Xu Yu Wang XianBin Yu LinYing Lu Carolina Boom ChangPeng Xin Xin Yan YiJing Sun BeiFei Zhou Wei Zhou RiQi Liao Dan Li Collaborators: Andreas Dress (PICB) Atul Jain (UIUC) LuoNan Chen (SHU) Martin Vingron (MPIMG) Qin Liu (SHJT) RongFu Gao (BFU) Stephen P Long (UIUC) Su-Shing Chen (PICB) ZengRong Liu (SHU) Zhenbing Zeng (ECNU)

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