Utopian exploration of global
patterns of plant metabolism.
James Furze1, Quan Min Zhu1, Feng Qiao2, Jennifer Hill1
1Facul...
Introduction
• ‘Utopia’ - defined as a hyperplane /
combination of objectives
• ‘Global’ – expression of the antecedent
en...
Photosynthetic types
• 3 Carbon (C3)
• Plants which contain 3 carbons (3-
Phosphoglyceric acid) as the first product of
ph...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
U...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
L...
Photosynthetic types
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Eg...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
S...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
E...
Photosynthetic types
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Eg...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
A...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
C...
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
T...
Fig. 1. ‘GTOPO30’ Digital Elevation Model 1Km resolution framework from
United States Geological Survey.
Presented in 2013...
Water – Energy Dynamic
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, ...
Fig. 2. Mean Annual Precipitation (1961-90).
Presented in 2013 at 5th International Conference on Modelling, Identificatio...
Fig. 3. Mean Annual Temperature (1961-90).
Presented in 2013 at 5th International Conference on Modelling, Identification ...
Table 2 Variable Partitioning.
Presented in 2013 at 5th International Conference on Modelling, Identification and Control,...
Fig. 4. Approximation of Plant Strategy Ordination.
Presented in 2013 at 5th International Conference on Modelling, Identi...
Fig. 5. Digital elevation model mapping of candidate area E3, Cuba (1 Km
resolution).
Presented in 2013 at 5th Internation...
Fig. 6. Cuba quarterly mean precipitation (1961-90) at 18.5km resolution.
Presented in 2013 at 5th International Conferenc...
Fig. 7. Cuba quarterly mean temperature (1961-90) at 18.5km resolution.
Presented in 2013 at 5th International Conference ...
Equation 1. Fuzzy Control Algorithm for Cuba, E3
Presented in 2013 at 5th International Conference on Modelling, Identific...
Fig. 8. 3-D Surface view of Fuzzy Control Algorithm for E3, variables
precipitation and temperature.
Presented in 2013 at ...
Table 3. 20 Photosynthetic solutions and their quantification.
Presented in 2013 at 5th International Conference on Modell...
Genetic dispersion of plant characteristics
Presented in 2013 at 5th International Conference on Modelling, Identification...
Fig. 9. Plant photosynthetic evolutionary strength pareto
Presented in 2013 at 5th International Conference on Modelling, ...
Definitions of metabolic utopia
Presented in 2013 at 5th International Conference on Modelling, Identification and Control...
Summary
• Basic definitions have been given.
• The method by which a closed loop of plant characters may be created has be...
References
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31...
Author Contact details:
Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo,...
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J. furze, q. m. zhu, f. qiao and j. hill, (2013), utopian exploration of global patterns of plant metabolism, icmic 2013, cairo, egypt

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  • Ribulose 1,5 bis phosphate carboxylaseoxygenasePhosphoenolPyruvate
  • https://lta.cr.usgs.gov/GTOPO30 available for download.
  • ws = woody stem, fs = fleshy stem, hs = hairy stem, fl = fleshy leaves, tl = thin leaves, nll = needle-like leaves, tr = tap root, crb = compactroot ball, drb = dispersed root ball, gpsod = greater proportion of stomatal pores open during day, gpspon =greater proportion of stomatal pores open during night, pka = presence of Kranz anatomy, ppep = presence ofphosphoenolpyruvate, sspc = spatial separation of photosynthetic compounds, tspc = temporal separation ofphotosynthetic compounds, sac = storage of acidic compounds, soc = storage of carbohydrate, c = competitor, s= stress tolerant, r = ruderal. Ideal quantification is shown in brackets in the table.
  • J. furze, q. m. zhu, f. qiao and j. hill, (2013), utopian exploration of global patterns of plant metabolism, icmic 2013, cairo, egypt

    1. 1. Utopian exploration of global patterns of plant metabolism. James Furze1, Quan Min Zhu1, Feng Qiao2, Jennifer Hill1 1Faculty of Environment and Technology University of the West of England Frenchay Campus, Coldharbour Lane Bristol, BS16 1QY, UK 2Faculty of Information and Control Engineering Shenyang Jianzhu University 9 Hunnan East Road, Hunnan New District Shenyang, 110168, China Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    2. 2. Introduction • ‘Utopia’ - defined as a hyperplane / combination of objectives • ‘Global’ – expression of the antecedent environmental conditions which all primary producers are present within across our planet • ‘Metabolism’ – referring here to the fundamental process in plants, photosynthesis Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    3. 3. Photosynthetic types • 3 Carbon (C3) • Plants which contain 3 carbons (3- Phosphoglyceric acid) as the first product of photosynthesis Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    4. 4. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Ulmus minor subsp. minor - Elm (East Somerset, UK 2013)
    5. 5. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Leymuschinensis - Chinese Lyme Grass (Eastern Mongolia 2013)
    6. 6. Photosynthetic types Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © • 4 Carbon (C4) • Plants which contain 4 carbons (eg. Malate) as the first product of photosynthesis
    7. 7. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Sesuviumportulacastrum – Fig Marigold (Cuba 2013)
    8. 8. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Euphorbia pulcherrima – Poinsetta Spurge (Cuba 2013)
    9. 9. Photosynthetic types Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © • Crassulacean Acid Metabolism (CAM) • First seen in the Crassulafamily • Plants which store acidic compounds (e.g. Malate) in vacuoles for breakdown during the day, photosynthesis occurs at night
    10. 10. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Agave americana - Century plant (Greece 2013)
    11. 11. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Crassulaovata – Jade plant (Macedonia 2013)
    12. 12. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Table 1 Fundamental differences between photosynthetic metabolic types. C3 C4 CAM Stomata open during the day Yes Yes No Photosynthetic enzyme RUBISCO PEPCO PEPCO Inhibition of photosynthesis by oxygen Yes No Yes during the day No at night Seperation of photosynthetic process None Spatial Temporal Adapted climate Cool, Moist Warm, Moist-Dry Hot, Dry
    13. 13. Fig. 1. ‘GTOPO30’ Digital Elevation Model 1Km resolution framework from United States Geological Survey. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    14. 14. Water – Energy Dynamic Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © • The distribution of water and energy on a global scale. • At low latitudes (south of the equator) water is seen to be more important for high species numbers. • At higher latitudes energy is seen to be more important for high species numbers. • We examine the importance of this dynamic for plants as both water and energy are involved in physiological (and metabolic) processes in plants.
    15. 15. Fig. 2. Mean Annual Precipitation (1961-90). Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Jan Apr Jul Oct
    16. 16. Fig. 3. Mean Annual Temperature (1961-90). Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Jan Apr Jul Oct
    17. 17. Table 2 Variable Partitioning. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © Linguistic expression % Quantification / Notation Range Mean temperature (o C) A1 Mean precipitation (kg m2 ) A2 Altitude (m) A3 Low 0 – 20 / (1) -75 - -51 0 – 100 0 – 1000 Low- Medium 20 – 40 / (2) -51 - -27 100 – 200 1000 – 2000 Medium 40 – 60 / (3) -27 - -3 200 – 300 2000 – 3000 Medium- High 60 – 80 / (4) -3 - 21 300 – 400 3000 – 4000 High 80 – 100 / (5) 21 - 45 400 – 500 4000 – 5000
    18. 18. Fig. 4. Approximation of Plant Strategy Ordination. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    19. 19. Fig. 5. Digital elevation model mapping of candidate area E3, Cuba (1 Km resolution). Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    20. 20. Fig. 6. Cuba quarterly mean precipitation (1961-90) at 18.5km resolution. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    21. 21. Fig. 7. Cuba quarterly mean temperature (1961-90) at 18.5km resolution. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    22. 22. Equation 1. Fuzzy Control Algorithm for Cuba, E3 Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © IF 0.25A1(4) - A1(5) 0.75A1(5) - A1(5) AND 0.5A2(1) - A2(1) 0.25A2(1) - A2(3) 0.25A2(2) - A2(3) AND A3(1) – A3(3) THEN B(50700) = E3 The antecedent expression is broken into variables as stated in Table 2 Thus categorising the node of E3. The statement contains 12 rules when expanded, the 3-D surface view of the algorithm is an efficient visualisation of the relations expressed.
    23. 23. Fig. 8. 3-D Surface view of Fuzzy Control Algorithm for E3, variables precipitation and temperature. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    24. 24. Table 3. 20 Photosynthetic solutions and their quantification. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    25. 25. Genetic dispersion of plant characteristics Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © 1. Define 20 vectors for plant metabolism. 2. Randomly generate an initial population of 20 solutions (chromosomes). 3. Evaluate each solution according to how well it fits into the desired environment (as defined in equation (1)). 4. Select chromosomes randomly (tournament selection), keep those with the highest fitness function to improve the population, discard those with too low (value may be previously calculated) fitness. 5. Create new chromosomes by crossing selected solutions to produce new individual chromosomes. 6. Mutate a previously determined proportion of the populations’ chromosomes. 7. Go back to step 3. The genetic algorithm stops when the desired population number is met.
    26. 26. Fig. 9. Plant photosynthetic evolutionary strength pareto Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    27. 27. Definitions of metabolic utopia Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © • Temperature and precipitation are n objective functions, from Fig. 9 we may state Z ⊆ Rn(Equation 2) • The design variable (D) determines the spread of vectors within the Z space. • Hence the MOGA for the dispersion of elements may be stated to be: Min F = {F1(x), F2(x), . . . , Fn(x)}, subject to x ∈ D (Equation 3) The error of D was seen to be 0.040371
    28. 28. Summary • Basic definitions have been given. • The method by which a closed loop of plant characters may be created has been given. • Efficient visualisation of an algorithm for Cuba (E3) has been shown. • Photosynthetic characters have been identified and quantified. • Dispersal of elements may be shown using a MOGA • Climatic data is enhanced as entering the value of objective 1 (i.e. precipitation) into the algorithm gives a prediction of objective 2 (i.e. temperature). • The resultant pareto enables the distribution of elements to be approximated in successive generations. • Metabolic patterns of plants have stochastic distribution, shown in this study using a hybrid fuzzy-genetic approach. • Further studies may include patterning of secondary metabolites via fast computational algorithms. Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 ©
    29. 29. References Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © [1] S. Niu, Z. Yuan, Y. Zhang, W. Liu, L. Zhang, J. Huang and S. Wan,“Photosynthetic responses of C3 and C4 species to Water availability and competition,” J. Experimental Botany, Vol. 56, No. 421, October 2005, pp. 2867-2876. [2] F. B. Salisbury and C. W. Ross, Plant Physiology Fourth Edition, Wadsworth Publishing Company, California, USA, 1992. [3] J. E. Keeley and P. W. Rundel, “Evolution of CAM and C4 carbon concentrating mechanisms,” Int. J. Plant Sci., Vol. 164, 2003, S.55- S.77. [4] C. Wang, L. Guo, Y. Li and Z. Wang, “Systematic Comparison of C3 and C4 Plants Based on Metabolic Network Analysis,” BMC SystemsBiology, Vol. 6, S.2, 2012, pp. 1-14. [5] U. Lüttge, “Ecophysiology of Crassulacean Acid Metabolism,” Annalsof Botany, Vol. 93, No. 6, 2003, pp. 629-652. [6] W. Barthlott, J. Mutke, D. Rafiqpoor, G. Kier and H. Kreft, “Global Centers of Vascular Plant Diversity,” Nova ActaLeopoldinaNF, Vol. 92, No. 342, 2005, pp. 61-83. [7] B. A. Hawkins, R. Field, H. V. Cornell, D. J. Currie, J. F. Guégan, D. M. Kaufman, J. T. Kerr, G. G. Mittelbach, T. Oberdorff, E. M. O’Brien, E. E. Porter and J. R. G. Turner, “Energy, water and broadscale geographic patterns of species richness,” Ecology, Vol. 84, No. 12, 2003, pp. 3105-3117. [8] J. N. Furze, Q. M. Zhu, J. Hill and F. Qiao, “Species area relations and information rich modelling of plant species variation”, Automationand Computing (ICAC), 2011 17th International Conference on , vol., no., 10 Sept. 2011, pp.63-68. [9] J. H. Sommer, H. Kreft, G. Kier, W. Jetz, J. Mutke and W. Barthlott,“Projected impacts of climate change on regional capacities for global plant species richness,” Proc. R. Soc. B, Vol.277, 2010, pp.2271-80. [10] J. N. Furze, Q. M. Zhu, F. Qiao and J. Hill, "Facilitating description of fuzzy control algorithms to ordinate plant species by linking online models," Modelling, Identification & Control (ICMIC), 2012Proceedings of International Conference on, Vol., No., 24-26 June 2012, pp. 933-938. [11] M. New, M. Hulme, and P. Jones, “Representing twentieth century space- time climate variability. Part I- Development of a 1961-90 mean monthly terrestrial climatology,” J. Climate, Vol. 12, 1999, pp. 829- 856. [12] J. Furze, J. Hill, Q. M. Zhu, F. Qiao, "Algorithms for the Characterisation of Plant Strategy Patterns on a Global Scale,“ American Journal of Geographic Information System, Vol. 1, No. 3, 2012, pp. 72-99. [13] J. N. Furze, Q. Zhu, F. Qiao and J. Hill, “Linking and implementation of fuzzy logic control to ordinate plant strategies,” Int. J. ModellingIdentification and Control, 2013, (In Press). [14] O. Cordón, F. Gomide, F. Herrera, F. Hoffmann and L. Magdalena, “Ten years of genetic fuzzy systems: current framework and new trends,” Fuzzy Sets and Systems, Vol. 141, 2004, pp. 5-31. [15] P. P. Angelov and D. P. Filev, “An approach to online identification of Takagi-Sugeno fuzzy models,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 43, No. 1, 2004, pp. 484-898. [16] T. Erfani and S. V. Utyuzhnikov, “Directed search domain: a method for even generation of the Pareto frontier in multiobjective optimization,” Engineering Optimization, Vol. 43, No. 5, 2011, pp. 467- 484. [17] Zhao, L., “Application of interval type-2 fuzzy neural networks to predict short term traffic flow,” Int. J. Computer Applications in Technology, Vol. 43, No. 1, 2012, pp.67–75. [18] C. Raunkier, The Life Forms of Plants and Statistical Plant Geography, Oxford University Press, Oxford, UK, 1934. [19] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, 1965, pp. 338-353. [20] W. Liao, E. Pan and L. Xi, “A heuristics method based on ant colony optimization for redundancy allocation patterns,” Int. J. Computer Applications in Technology, Vol. 40, Nos. 1-2, 2011, pp. 71-78. [21] C. B. P. Notario, R. Baert, and M. D’Hont, “Muti Objective genetic algorithm for task assignment on heterogenous nodes,” Int. J. Digital Multimedia Broadcasting, Vol. 2012, 2012, pp. 1-12. [22] G. Marsaglia and W. W. Tsang, “The Ziggurat method for generating random variables,” J. Statistical Software, Vol. 5, No. 8, 2000, pp. 1- 7. [23] C. Schölzel and P. Friedrichs, “Multivariate non normally distributed random variables in climate science – Introduction to the copular approach,” Non lin. Processes Geophys., Vol. 15, 2008, pp. 761-772.
    30. 30. Author Contact details: Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 2 © James. N. Furze Faculty of Environment and Technology University of the West of England Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, UK Email: James.Furze@uwe.ac.uk , james.n.furze@gmail.com
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