Modelling of processes lets one understand the functions of interacting components, helps to identify parts of processes, and can predict outcomes of changes in the system. Unfortunately, what was a major area of financial modelling is now largely discredited, much to the cost of the rest of us; other areas such as insurance are becoming so constrained by rules and regulation as to be useless. Biological modelling, in contrast is advancing rapidly, whether with respect to subcellular events, whole organism development, or disease epidemiology. Professor Xueron Mao has organized a meeting at the University of Strathclyde in Glasgow, Scotland, on “Stochastic Modelling in Ecosystems.” More details on www.AoBBlog.com

1. Workshop on Stochastic Modelling in Ecosystems
Glasgow, June 2012
Between biodiversity and
crops: needs from stochastic
models at the ecosystem scale
Pat Heslop-Harrison
www.AoBBlog.com phh4@le.ac.uk
www.molcyt.com ID/PW „visitor‟
13/06/2012 1

2. 2

3. 3

4. Rainfall
Distribution
mm/yr
4

5. 5
Artist: R . Sphestre?, Le Tadorne, Piney, France. 2012

6. NASA
The Blue Marble
Apollo 17 7 Dec 1972

7. Stochastic Modelling in Ecosystems
Living components
– Plants and cyanobacteria (primary producers)
– Bacteria, fungi, animals
Interacting with abiotic components
– Light
– Water
– Wind, soil, nutrients, toxins, gasses ...
Recognizable homogeneity in one ecosystem
7

8. Ecosystems anchor slide
Largely
– Self-organizing
– Self-maintained
– Cycling
– Defined scope
– cf Household
– Aircraft
–
8

9. Stochastic Modelling in Ecosystems
Recognizing
– Inputs
– Outputs
– Networks / webs of organisms
– Cycles
– Scales
– Functions 9

10. Inputs
– Light
– Heat
– Water
– Gasses
– Nutrients
10

11. 50% of the world's protein needs
are derived from atmospheric
nitrogen fixed by the Haber-Bosch
process and its successors.
Global consumption of fertilizer
(chemically fixed nitrogen) 80
million tonnes
<<200 million tonnes fixed naturally

12. Outputs
– Light
– Heat
– Water
– Gasses
– Nutrients
12

13. Outputs
– Light
– Heat
– Water
– Gasses
– Nutrients
Discussion at the meeting: Prof Mathew
Williams pointed out that the „heat‟ input
is also an important modified output of an
ecosystem. Consider the different
temperatures and temperature cycles of
the desert and jungle ecosystems in the
second slide.
13

14. Outputs
Ecosystem
Services
Water, gasses,
nutrients
”nature‟s services, like flood control, water
filtration, waste assimilation”
14

15. Outputs
– Light
– Heat
– Ecosystem services
• Water, gasses, nutrients
– Chemical energy

16. Ecosystems: What do we take out?
The seven Fs
– Food
– Feed
– Fuel
– Fibre
– Flowers
– Pharmaceuticals
– Fun
16

18. Outputs
– Light
– Heat
– Ecosystem services
• Water, gasses, nutrients
– Chemical energy
– Long term storage
18

19. Stores of biologically
produced carbon in
Limestone
Peat
Oil and gas
19

20. Dynamic processes: turn-over
Outputs
– Limestone
20
– Made by marine organisms, formation and
stability affected by pH and temperature

21. Inputs - Biotic
– Diseases
– New organisms
• Aliens/invasives
– New genes and
genotypes of
existing
organisms
21

22. Outputs
– Light
– Heat
– Ecosystem services
– Chemical energy
– Long term storage
Required
and valued
22

23. Rio de Janeiro Conference in June 1992
Biological diversity as “the variability among
living organisms and the ecological complexes
of which they are part”
Conservation of ecosystems
Sustainability of human activity
Analysis of human effects and interactions
with the environment 23

24. … all suggest modelling … but
24

25. 25

26. Biotic Inputs
– New genes
– New species
• Diseases
• Alien species
Abiotic inputs
– Irrigation
– „Salt‟ (NaCl)
– Nitrogen
– Phosphorous
26

27. Water hyacinth – Eichornia: an invasive alien
plant from South America, fills water courses (a
surface habitat not used by any native species)
in Asia and Africa 27

28. Argenome mexicana: a goat-proof plant from
Mexcio introduced and successful in Africa 28

29. 29

30. Inputs are random variables
– with known or unknown distributions
Does the mean or the extreme
matter?
How does oscillation lead to
robustness?
Can routes from input to output be
simplified? 30

31. Rainfall
Distribution
mm/yr
31

32. Occasional ‘extreme inputs’:
Limiting composition of ecosystems
more than ‘mean input’ - Robustness 32

33. 33

34. 34

35. Anhalt, Barth, HH
Euphytica 2009 Theor App Gen

36. Regulation of oscillations
 Synchronization without external
regulators

37. Oscillations: noise and stability
 Stochastic fluctuations
– preserve stable oscillations
– ensure robustness of the oscillations to cell-to-cell
variations
 Robustness analysis requires stochastic
simulation
JongRae Kim et al. Stochastic noise and synchronisation during Dictyostelium aggregation make cAMP
oscillations robust. PLoS Computational Biology 2007

38. Coupling of oscillators seen at all scales from
subcellular to ecosystem
Jeong-Rae Kim, PHH, Kwang-Hyun Cho. J Cell Sci 2010

39.  Stable cAMP oscillations
in the cells with other
molecules/ions
Valeyev et al. Mol Biosyst 2009

40.  Entrainment of a cell by surrounding cells:
 Individual cells synchronized/oscillate in phase
 Regardless of frequency, some effect of [cAMP]
Valeyev et al. Mol Biosyst 2009

41. No Stronger
Coupling

43. 43

44. 44

45. Eyespot (fungus
Pseudocercosporella)
resistance from
Aegilops ventricosa
introduced to wheat
by chromosome
engineering
Many diseases where
all varieties are
highly susceptible
Alien variation can be
found and used7
Host and non-host

46. Crop standing
Lodging in cereals
Crop fallen

47. Ecosystems anchor slide
Largely
– Self-organizing
– Self-maintained
– Cycling
– Defined scope
Networks are
– Stable
– Oscillating
– Complex and
maybe modular
– Simplification
– Models for
modelling
47

48.  Dynamic interactions between calcium, IP3 and G
protein-dependent modules
 Valeyev et al. Mol Biosyst 2009 5: 612

49. Identification of design principles: points
of structural fragility in networks
Dynamic interactions
between the different
modules generate more
stable and robust cAMP
oscillations
Robustness comparison includin
module interactions
Analysis and extension of a biochemical network model using robust control
theory J.-S. Kim, Valeyev, Postlethwaite, PHH, Cho, Bates
Int. J. Robust Nonlinear Control 2010; 20:1017–1026. DOI: 10.1002/rnc.1528 49

50. Light in ecosystems
Heat Information
Energy
Quantity Quality Direction Periodicity
Photosynthesis
Control of development

51. Simplification
of genetic
networks while
maintaining
dynamic
properties
Reduction of Complex Signaling
Networks to a Representative Kernel
Jeong-Rae Kim, Junil Kim,Yung-Keun
Kwon, Hwang-Yeol Lee, PHH. Kwang-
Hyun Cho. Science Signaling 4 (175),
51
ra35. [DOI:10.1126/scisignal.2001390

52. Network reduction Circadian Clock regulation
after Leloup
& Goldbeter;
Andrew Millar
in Arabidopsis
X X
Y
Y
Z
Z
Kim, HH, Cho et al. 2011 Science Signaling

53. Integrin gene network
53

54. Function and multifunction
How many genes are there?
1990s: perhaps 100,000
2000: 25,000
How does this give the range of
functions and control?
Najl Valeyev

55. Lolium Biomass production
Susanne Barth, Ulrike Anhalt, Celine Tomaszewski

56. Anhalt, Barth, HH et al.
Segregation distortion in
Lolium: evidence for genetic
effects. Theoretical & Applied
Genetics 2008

57. Anhalt, Barth, HH Euphytica 2
Theor App Gen 2008

58. Anhalt UCM, Heslop-Harrison JS, Piepho HP, Byrne S, Barth S. 2009. Quantitative trait loci
mapping for biomass yield traits in a Lolium inbred line derived F2 population. Euphytica 170: 99-107.

59. Network
structures
differ between
systems: what
about
ecosystems?
Kim TH, Kim J, PHH, Cho KH. 2011. Evolutionary design principles and functional
characteristics based on kingdom-specific network motifs. Bioinformatics 27: 245-
251. http://dx.doi.org/10.1093/bioinformatics/btq633 59

60. 60

61. Threats to sustainability:
no different for 10,000 years
Habitat destruction
Climate change (abiotic stresses)
Diseases (biotic stresses)
Changes in what people want
MORE outputs needed
MORE stability in outputs from less
stable inputs / poorer environments

62. 62

64. How to exploit models
Increased sustainability
Increased value
Genetic improvement
Robustness („food security‟)
Benefits to all stakeholders:
Breeders, Farmers, Processors,
Retailers, Consumers, Citizens
64

65. 50 years of plant breeding progress
4
GM maize
Maize
Genetics
3.5
3 Rice
2.5
Agronomy Wheat
2
Human
1.5
Area
1
0.5
0
1961 1970 1980 1990 2000 2007

66. United Nations Millennium Development Goals-MDGs
• Goal 1 – Eradicate extreme
poverty and hunger
•
Goal 2 – Achieve universal primary education
• Goal 3 – Promote gender equity
and empower women
• Goal 4 – Reduce child mortality
• Goal 5 – Improve maternal health
• Goal 6- Combat
HIV/AIDS, malaria and other
diseases
• Goal 7 - Ensure environmental
sustainability
• Goal 8 - Develop a global
partnership for development

67. Conventional Breeding
Cross the best with the best and hope for
something better
Superdomestication
Decide what is wanted and then plan how to
get it
– Variety crosses
– Mutations
– Hybrids (sexual or cell-fusion)
– Genepool
– Transformation

68. Economic growth
Separate into increases in
inputs (resources, labour and
capital) and technical progress
90% of the growth in US output
per worker is attributable to
technical progress
Robert Solow – Economist

70. Market Demand “MORE”
Food production volume
– No possibility of market collapse
– Only slow market increase
– Reduced post-harvest loss
– Some crops gain/hit by global
trends

71. Inputs
Better genetically
– Harvest more
– Stress resistant (Disease = biotic and
environment – abiotic)
Higher
– Weed control improving for 8000 years
Lower
– Production loss less than cost decrease
– Better agronomy (cropping cycles etc.)

72. Needs from Stochastic Models of Ecosystems
Outputs Inputs
Ecosystem – Light
services – Heat
– Chemical – Water
energy
– Gasses
– Long term
– Nutrients
storage
72

73. Workshop on Stochastic Modelling in Ecosystems
Glasgow, June 2012
Between biodiversity and
crops: needs from stochastic
models at the ecosystem scale
Pat Heslop-Harrison
www.AoBBlog.com phh4@le.ac.uk
www.molcyt.com ID/PW „visitor‟
13/06/2012 73