Quantitative interpretation of gene network controls and the effects of selection on these

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7 Νοε 2013 (πριν από 4 χρόνια και 8 μήνες)

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Project proposal idea for
CSS August

Quantitative interpretation of gene network controls and the
ects of selection on these

Dr. Scott Chapman, Plant Industry,
St. Lucia

G.L. Hammer
, The University of Queensland

A two year post
doc project

Two strategic
areas for
Australian science


Genome/phenome research to address the need for biologists who understand molecular

the behaviour of the whole cell, the whole tissue and the whole organism.


Complex system control,

as exhibited in plant phenological response to the environment

For CSIRO to maintain a lead in genome/phenome research, it must begin to develop skills in the
analysis and interpretation of complex gene networks and in the ways in which these can be
ulated to improve crop adaptation. The proposed research would focus on physiological traits
that CSIRO PI works on and link to the current UQ/CSIRO/QDPI project

with Pioneer Hi
Bred (the
world’s largest plant breeding company)
. CIRAD researchers are also
keen to collaborate in this
, and a grant has already been obtained to visit them in France this winter
. During development
the plan would link with Canberra researchers in plant development and genetics

(Programs P and
, including rice gene machine
team. The generic nature of the research could extend to
quantifying the control of other processes such as disease response. Some basic models exist for
these, but there are substantial development needs.

Molecular biologists, including in CSIRO PI are un
ravelling the genetic controls associated with
two of the most important genetic controls of crop yield:
and plant height. The
approximate genetic networks controlling these traits are frequently described in a qualitative sense,
using techn
ologies like mutation and gene silencing to knock out functional genes from a single
genotype. To be applied in the understanding of the process of plant breeding, these genetic
networks need to be (i) described and quantified to better infer their physiol
ogical effects; (ii)
investigated to determine the allelic effects that contribute to genetic variation. In conjunction with
CSE, CLW, QDPI, UQ and other private industry partners, we have and continue to develop two
types of models of genome/phenome attri


a biophysical model (APSIM) to propagate gene signals over scales (gene to canopy)


a gene simulation model (QU
Gene) to determine the effects of recombining genes that
control different biophysical attributes.

We use a cluster of >80 computers to ge
nerate the phenotypic state
space resulting from genetic
networks that differ in structure and strength of allelic effects. Our research would (i) help infer
genetic control from expression data; (ii) reduce the state
space according to improved
tion of the genetic controls of flowering and height; (ii) improve the identification of
better breeding methods that exploit understanding of established and selectable gene networks.

The project will employ a post
doc to
a biophysical simulatio
n module of
the gene
networks controlling
flowering and plant height interactions using data from the literature, small
CEF experiments & PI labs on
, rice & wheat. The model will be parameterised to
reflect known and postulated allelic variatio
n for various control points (gene networks) of these
processes & tested to examine which types of genetic networks appear to best reflect real
observations. Given the allelic variation, successful plant breeding strategies will be identified via
scale simulations where the biophysical and gene simulation models are linked.

The CSS outcomes from this work include:


data and computing methods to generate complex growth and development behaviour for
plant models


insight into the control of signalli
ng networks for key traits in crop plants


investigation of Bayesian networks in the prediction of plant phenotype from marker

Chapman, S.C., Cooper, M.
and Hammer, G.L.


Evaluating plant breeding strategies by
simulating gene action and dryland environment effects

Agronomy Journal
95: 99

Cooper, M., Chapman, S.C., Podlich, D.W., Hammer, G.L. (2002)

The GP problem: Quantifying
phenotype relationships. In Silico Biology 2, 0013 On
line invited paper:


The methods are largely to be developed from known research data

The research would require
travel and accommodation funds to work with researchers in Canberra (one to two visits per year)
utilise past and present experiments on flowering and plant height.

At current
the project
would require ca. $130
160 000
per year
2 years

The investment would greatly leverage current
relationships with a large breeding company and other in
stitutions to deliver insights into the
application of CSS in controlled plant evolution, i.e. plant breeding.