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- The genome also contained an expanded family of G-protein coupled receptors and many genes involved in secondary metabolism, both of which are important for fungal pathogenesis.
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2) P. triici populations on wheat and grass species show evidence of both sexual reproduction and clonality. Identical genotypes were found on both wheat and weed hosts, indicating gene flow between these populations.
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This document summarizes key findings from the genome sequencing of the oomycete plant pathogen Magnaporthe grisea, which causes rice blast disease. Some key points:
- The draft genome sequence was 38.8 megabases in length and contained a large number of genes encoding secreted proteins and carbohydrate-binding domains that help the pathogen infect plants.
- The genome also contained an expanded family of G-protein coupled receptors and many genes involved in secondary metabolism, both of which are important for fungal pathogenesis.
- Expression of several of these genes increased during early infection, suggesting they play a role in M. grisea's ability to infect rice plants and cause disease.
Networks and epidemiology - an introductionMarco Pautasso
network epidemiology, Phytophthora ramorum, network theory, plant pathology, epidemic spread, clustering, small-world, random, scale-free. Introduction: interconnected world, growing interest in network theory and disease spread in networks. Examples of recent work modelling disease (i) spread and (ii) control in networks of various kinds
The document summarizes research on the emergence of wheat blast disease in Brazil caused by the fungus Pyricularia triici. Key findings include:
1) P. triici that causes wheat blast is genetically distinct from P. oryzae that causes rice blast, based on analyses of genes and genotypes.
2) P. triici populations on wheat and grass species show evidence of both sexual reproduction and clonality. Identical genotypes were found on both wheat and weed hosts, indicating gene flow between these populations.
3) In contrast, P. triici populations show no gene flow with P. oryzae populations that infect rice. This supports the conclusion that wheat blast and rice blast are
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2. It provides examples of recent work modeling disease spread in networks and a case study on Phytophthora ramorum.
3. The author proposes further applications of network theory could include plant-vector interactions, conservation biology, and invasion ecology related to plant diseases.
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disease, globalized world, epidemiology, network theory, epidemic threshold, starting node, clustering, final size. Main results 1. lower epidemic threshold for scale-free networks 2. in-out correlation more important than clustering 3. out-degree as a predictor of epidemic final size 4. implications for the horticultural trade
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This document discusses networks and epidemiology, with a focus on applying network theory to model disease spread. It provides examples of modeling disease spread in different types of networks, including local, small-world, random and scale-free networks. It also presents a case study on modeling the spread of Phytophthora ramorum in directed networks of small size. The conclusion discusses potential future work applying network theory and spatially-explicit modeling to problems in conservation biology, invasion ecology, and other areas of biogeography.
An introduction to the species-people correlation, species, people and networks, ramorum leaf blight, sudden oak death, complex networks, network epidemiology, network theory, scale-free degree distribution, epidemic threshold and final size, clustering coefficient, stream macro-invertebrates, Phytophthora ramorum, Sudden Oak Death
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This document discusses building models of disease using data intensive science. It describes integrating omics data and computational models in a compute space. The challenges of the current drug discovery process are outlined, noting a need to better understand disease biology before testing compounds. Network models are proposed to capture disease complexity beyond single components. Examples are given of building gene co-expression networks from large datasets and using them to identify disease modules and key drivers. The potential for predictive models of genotype-specific drug responses is also mentioned.
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Epidemiological modelling of Phytophthora ramorum
1. Epidemiological modeling of
Phytophthora ramorum: network
properties of susceptible plant genera
movements in the UK nursery sector
Marco Pautasso,1 Tom Harwood,2 Mike Shaw,2
Xiangming Xu3 & Mike Jeger1
1 Imperial College London, UK
2 University of Reading, UK
3 East Malling Research, UK
SOD Symposium III,
8 Mar 2007
2. Disease spread in
a globalized world
number of passengers per day
From: Hufnagel, Brockmann & Geisel (2004) Forecast and control
of epidemics in a globalized world. PNAS 101: 15124-15129
3. Epidemiology is just one of the
many applications of network theory
Network pictures from:
Newman (2003) NATURAL
The structure and function
of complex networks. food webs
SIAM Review 45: 167-256
cell
metabolism
neural Food web of Little Rock
networks Lake, Wisconsin, US
ant nests sexual
partnerships
DISEASE
SPREAD
family
innovation networks
Internet flows co-authorship HIV
structure railway urban road nets spread
electrical networks networks network
power grids telephone calls
WWW
computing airport Internet E-mail
committees
grids networks software maps patterns
TECHNOLOGICAL SOCIAL
Modified from: Jeger MJ, Pautasso M, Holdenrieder O & Shaw MW (2007) Modelling disease
spread and control in networks: implications for plant sciences. New Phytologist in press
4. Different types of networks
local small-world
random scale-free
Modified from: Keeling & Eames (2005) Networks and epidemic models. Interface 2: 295-307
5. Epidemic development in different types of networks
scale-free
random
2-D lattice rewired
2-D lattice
1-D lattice rewired
1-D lattice
N of nodes of networks = 500;
p of infection = 0.1;
latent period = 2 time steps;
infectious period = 10 time steps
From: Shirley & Rushton (2005) The impacts of network topology on disease spread.
Ecological Complexity 2: 287-299
6. Temporal development; England & Wales, 2003-2005; n = 1104
100
Records positive to P. ramorum
75 nurseries/
n of records
garden
centres
50
25
0
3
4
5
03
3
04
4
05
5
3
4
5
-0
-0
-0
l-0
l-0
l-0
-0
-0
-0
n-
n-
n-
pr
pr
pr
ct
ct
ct
Ju
Ju
Ju
Ja
Ja
Ja
O
O
O
A
A
A
Data source: Department for Environment, Food and Rural Affairs, UK
7. Temporal development; England & Wales, 2003-2005; n = 1456
250
Records positive to P. ramorum
200
estates/
n of records
environment
150
100
50
0
3
4
5
03
04
05
4
5
3
3
4
5
-0
-0
-0
l -0
l -0
l-0
-0
-0
-0
n-
n-
n-
pr
pr
pr
ct
ct
ct
Ju
Ju
Ju
Ja
Ja
Ja
O
O
O
A
A
A
Data source: Department for Environment, Food and Rural Affairs, UK
8. Temporal development; England & Wales, 2003-2005; n = 704
Nursery records positive to P. ramorum
100%
UK origin
75% non-UK origin
n of records
50%
25%
0%
3
4
5
3
4
5
3
4
5
03
04
05
-0
-0
-0
l-0
l-0
l-0
-0
-0
-0
n-
n-
n-
pr
pr
pr
ct
ct
ct
Ju
Ju
Ju
Ja
Ja
Ja
O
O
O
A
A
A
Data source: Department for Environment, Food and Rural Affairs, UK
9. England and Wales: records positive
to Phytophthora ramorum
n = 2788
Jan 2003-Dec 2005
Courtesy of
Richard Baker, Data source: DEFRA, UK
CSL, UK
10. Web of susceptible genera connected by Phytophthora ramorum (based on
genus co-existence in 2788 positive findings in England & Wales, 2003-2005)
Viburnum
Camellia Umbellularia
Castanea Taxus
Syringa
Drimys
Fagus Rhodo-
dendron
Festuca
Hamamelis Quercus
Kalmia Pieris
Laurus Magnolia Parrotia
Leucothoe
Data source: DEFRA, UK
11. Frequency distribution of number of plant genera affected by
Phytophthora ramorum by n of records in the database of 2788
positive findings in England & Wales, 2003-2005)
1.2
log10 number of affected genera
y = -0.33x + 1.27
1.0 2
R = 0.93
0.8
0.6
0.4
0.2
0.0
0.0 1.0 2.0 3.0 4.0
log10 n of positive P. ramorum records in database
Data source: DEFRA, UK
12. Connectivity loss in the North American power grid
due to the removal of transmission substations
transmission nodes removed (%)
From: Albert, Albert & Nakarado (2004) Structural vulnerability of the
North American power grid. Physical Review E 69, 025103
13. Acknowledgements
Alan Inman, Department for Environment, Food
and Rural Affairs, UK
Claire Sansford, Judith Turner
& Richard Baker,
Central Science Laboratory, York, UK
Sandra Denman & Joan Webber,
Forest Research, Alice Holt, UK
Ottmar Holdenrieder, ETH, Zurich, CH
Jennifer Parke, Oregon State University
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