J. furze, q. m. zhu, f. qiao and j. hill, (2013), utopian exploration of global patterns of plant metabolism, icmic 2013, cairo, egypt

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 EngineeringShenyang Jianzhu University

9 Hunnan East Road, Hunnan New DistrictShenyang, 110168, China

Presented in 2013 at 5th International Conference on Modelling, Identification and Control, Cairo, Egypt Aug 31-Sept 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


Photosynthetic types

• 3 Carbon (C3)

• Plants which contain 3 carbons (3-Phosphoglyceric acid) as the first product of photosynthesis



Ulmus minor subsp. minor - Elm (East Somerset, UK 2013)


Leymus chinensis - Chinese Lyme Grass (Eastern Mongolia 2013)



• 4 Carbon (C4)

• Plants which contain 4 carbons (eg. Malate) as the first product of photosynthesis


Sesuvium portulacastrum – Fig Marigold (Cuba 2013)


Euphorbia pulcherrima – Poinsetta Spurge (Cuba 2013)



• Crassulacean Acid Metabolism (CAM)

• First seen in the Crassula family

• Plants which store acidic compounds (e.g. Malate) in vacuoles for breakdown during the day, photosynthesis occurs at night


Agave americana - Century plant (Greece 2013)


Crassula ovata – Jade plant (Macedonia 2013)


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

Fig. 1. ‘GTOPO30’ Digital Elevation Model 1Km resolution framework from United States Geological Survey.


Water – Energy Dynamic


• 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.

Fig. 2. Mean Annual Precipitation (1961-90).


Jan Apr

Jul Oct

Fig. 3. Mean Annual Temperature (1961-90).


Jan Apr

Jul Oct

Table 2 Variable Partitioning.


Linguistic expression

% Quantification

/ Notation

Range Mean temperature

(oC) 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

Fig. 4. Approximation of Plant Strategy Ordination.


Fig. 5. Digital elevation model mapping of candidate area E3, Cuba (1 Km resolution).


Fig. 6. Cuba quarterly mean precipitation (1961-90) at 18.5km resolution.


Fig. 7. Cuba quarterly mean temperature (1961-90) at 18.5km resolution.


Equation 1. Fuzzy Control Algorithm for Cuba, E3


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.

Fig. 8. 3-D Surface view of Fuzzy Control Algorithm for E3, variables precipitation and temperature.


Table 3. 20 Photosynthetic solutions and their quantification.


Genetic dispersion of plant characteristics


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.

Fig. 9. Plant photosynthetic evolutionary strength pareto


Definitions of metabolic utopia


• 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

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.


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Author Contact details:


James. N. FurzeFaculty of Environment and TechnologyUniversity of the West of EnglandFrenchay Campus, Coldharbour Lane,Bristol, BS16 1QY, UK Email: [email protected] ,

[email protected]

Education

J. furze, q. m. zhu, f. qiao and j. hill, (2013), utopian exploration of global patterns of plant metabolism, icmic 2013, cairo, egypt