At the end of this message please find the summary of a proposal which
was submitted to iPlant (http://iplantcollaborative.org/)  to fund a
planning workshop relating to modeling crop responses to global change.
The submitted proposal reflects a very active discussion among eight or
so scientists with very different backgrounds, so it has evolved
considerably from what I may have discussed with some of you previously.


Three notable changes are;

-          We decided to focus mainly on crop plants, citing the call of
that National Academy of Sciences for the US National Plant Genome
Initiative to develop stronger ties with crop improvement.

-          The proposal focuses on global change as a problem area in
order to limit the number of possible traits (and ensure that the
project maximizes its relevance).

-          The concepts of "modeling" and "quantitative prediction" have
been broadened to give greater emphasis to using gene expression and
metabolomic data to develop models. This moves the project away somewhat
away from classical crop simulation models and quantitative prediction
of traits like grain yield, plant height or flowering time, but it
brings the work closer in line with apparent needs of the plant systems
biology community.

If approved, we would hold the workshop later this year (possibly
September at Biosphere 2 near Tucson). The workshop would allow us to
prepare the full proposal for iPlant to develop the necessary software
tools, databases (or database interfaces), etc.

About 60 people are listed as possible invitees for the workshop. The
iPlant guidelines suggest we can invite 25 to 35 people to the workshop.

If this proposal fails, we have the option of re-submitting in December.
I would welcome other suggestions on how to advance the proposed work,
including how to develop a fully international effort to improve use of
genomics in crop simulation modeling.

Please contact me directly if you would like a copy of the proposal.

My apologies for cross-postings.

 

Best regards,

Jeff White

 

Jeffrey W. White

USDA ARS, ALARC

21881 N Cardon Lane

Maricopa, AZ 85238, USA

Tel: +1-520-316-6368

[log in to unmask] <== CHANGED !!

 

Summary from the proposal

 

Anticipated global climate change will require directed adaptations of
crop species on an

unprecedented magnitude in order to sustain agricultural production. Our
Grand Challenge seeks to

dramatically improve quantitative prediction of phenotypes by
facilitating the characterization of

"molecular pathways" of ecologically and agriculturally important traits
affected by climate change. The

project will create computational tools that enable the integration of
phenotypes across diverse species. A

key focus will be the development of infrastructure tools for the global
integration of all available high

throughput data for effects of abiotic stress factors, associated with
climate change, that shape crop plant

performance in natural environments. Such integration can greatly
accelerate the generation and testing of

new hypotheses. The most promising hypotheses can be implemented in
process-based simulation tools

for further testing and application to climate change research issues
ranging from plant breeding strategies

to regional impact and mitigation studies.

Within the next few years, enormous amounts of functional genomics,
proteomics, metabolomics,

and comparative genomics data will be acquired. Multidisciplinary
approaches are needed that use

current biological knowledge to relate genetic change to phenotypic
outcome, as are young scientists that

are trained in more than one discipline. The existing datasets are often
incomplete, dated, difficult to

access, lacking in quality control, and error-prone. Nevertheless, they
contain valuable data that are

under-utilized. Using statistical and computational methods developed
within the iPlant context, these

data will be integrated to generate more accurate views of gene function
and to identify species- or

genotype-specific deviations, alterations, and adaptations to abiotic
stresses associated with climate

change. Such networks can directly inform phenotypic predictions of
developmental characteristics in

varied tissues and environmental contexts, which can guide plant
breeding. This level of modeling can

also support how processes are represented in the more integrative crop
simulators, which typically

provide quantitative predictions of phenotypes at the tissue, organ,
plant or community scales.

This workshop will generate new, and intensify existing, communications
concerning form and

content of available datasets across different crop communities, and
then relating those, where possible

and useful, to the model plants Arabidopsis and rice, as well as to
other major crop species. Through the

Grand Challenge proposal development process, we will identify existing
data and software resources,

computational and mathematical gaps, and relevant models toward the goal
of predicting crop phenotypes

from genotypes. Three major plant productivity traits affected by
climate change will be discussed in

detail: (i) temperature, atmospheric CO2, and ozone induced responses;
(ii) water deficit and flooding

induced responses; and (iii) phenology. The proposed Grand Challenge
Workshop (GCW) will include

participants from the fields of plant biology, genomics, bioinformatics,
crop breeding, crop physiology,

plant simulation modeling, computer science, mathematics, and education.
Expected workshop outcomes

include an initial consensus on key limitations in cyberinfrastructure
that limit current modeling

approaches, a better understanding of how different quantitative models
interconnect, prioritization

among traits, and identification of working groups needed to finalize
the Grand Challenge Proposal.