==> IF YOU RESPOND, PLEASE RESPOND TO ME AND NOT THE ENTIRE LISTSERVER.

 

Colleagues,

 

We are contacting you to ask whether you would like to collaborate in
the iPlant Project: "Cyberinfrastructural Support for Genetic and
Ecophysiological Studies of Plant Phenological Control in Complex and
Changing Environments." This project seeks to develop a
"cyberinfrastructure" (CI) that vastly improves the integration of data
analytic tools across scales ranging from the molecular level to
field-level phenology of crops and plants in natural ecosystems.
Emphasis is given to creating models at levels of process detail ranging
from molecular genetics to field ecophysiology. The CI is intended to be
flexible and adaptable enough to cover different species with minimal
adaptation and to be readily extended to other traits. To further the
goal of broad applicability tools, this CI proposal is being developed
in parallel with another one on abiotic stresses.

 

Inserted below please find the project summary. If you would like
further information, please contact me directly (NOT via the
listserver), and I'll send you the 13 page executive summary of the full
proposal.

 

The iPlant Collaborative (iplantcollaborative.org) is not designed to
support extensive experimentation or model development. The assumption
is that limitations arise form the lack of fluid access to integrated
data and support for group work. Thus, collaboration should develop
through sharing of data resources, tools, or test applications or
outreach situations.

            We propose to develop working groups related to various
aspects of the project.  One type of group would be species-related.
These groups would have the task of identifying sources of molecular,
phenotypic and environmental data, existing models, and tools.   For
example, in Arabidopsis, there is a vast amount of molecular data
available relating to the control of floral initiation.  In contrast,
although gene discovery in wheat is constrained by the large genome size
and lack of efficient means for transformation, the extensive data on
phenotypes and pedigrees present opportunities for using association
mapping to identify important loci and inform revision of
ecophysiological models.  The species that have been suggested to date
are: Arabidopsis, rice, maize, wheat (possibly with barley), grain
legumes (examined as a group), sorghum, Mimulus (monkey flower), and
poplar  Suggestions for other collaborators or species groups are still
more than welcome but please insure there is a core group of researchers
and data resources.

            Another work area will involve test driving versions of the
CI (including its data access/integration, modeling support, analysis
tools, user-friendliness, etc.) during the development process.
Needless to say, people can be in more than one group.  This will
provide the opportunity to influence system directions and gain early
use of the technology. Education and outreach is another area where
participation is welcome.  Equally important is participation by those
with expertise in the development of specific tools that may be of
interest to a broader community, especially if their utility or
effectiveness can be enhanced by juxtaposition with large stores of
data. 

At this point, we are simply soliciting statements of interest in
collaboration. Please indicate if you are interested in collaborating
and if so, what the nature of your participation would be. If your
contribution depends on the outcome of pending grants or other
considerations, please feel free to explain that your participation may
be conditional. To meet our submission deadline, a reply is needed by
January 30, 2009.

 

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]

 

Cyberinfrastructural Support for Genetic and Ecophysiological Studies of
Plant Phenological Control in Complex and Changing Environments


            


The phenology of a plant constitutes the temporal framework within which
morphology elaborates, the resultant organs either grow or senesce,
metabolic processes occur, and, ultimately, the genotype's contribution
to the next generation (its fitness) is made.   The proper timing of
life cycle events is critical to the avoidance of both biotic and
abiotic stressors and to achieving synchrony with beneficial species
such as pollinators or seed dispersal agents.  However, phenology is
strongly influenced by environments that are changing in uncertain ways
under the impact of human activity.  Empirical ecophysiological models
of plant phenology under field conditions date back over 270 years, but
molecular insights into the reasons for their accuracy exist only for
flowering time in Arabidopsis thaliana.  Understanding the control of
plant phenology and (ultimately) being able to predict it across a broad
range of species is a problem which, due to its complexity and the lash
of accelerating environmental change, rises to the level of a Grand
Challenge.

 

Specifically, answers are urgently needed to the following scientific
questions:

 

1.       What are the genetic and physiological factors that control
plant phenology in non-constant environments?

2.       How do the multiple signaling pathways/subsystems into which
these factors are organized integrate environmental information to
produce observed phenotypes?   

3.       What are the ranges of genetic diversity regarding phenological
events? 

4.       What selective factors influence plant phenology in the natural
world, how do they adaptively modify phenological control
mechanisms/networks, and how can we learn from this to enhance programs
of artificial selection for these traits (and others)?

5.       How can different model types (ecophysiological, molecular,
statistical, etc.) be combined for tasks ranging from phenological
prediction to gene discovery and network inference?

 

To answer these questions mechanisms are needed that aggregate
information from lower organizational scales (e.g. molecular and
cellular) and transfer this information to higher biological levels
(e.g., leaf or canopy). And then, by exploiting clues provided by
synteny, orthology, network similarities, similar forms or
ecophysiological behavior, and/or other evidence, we need to be able to
make inferences across species. 

 

The cyberinfrastructure (CI) detailed in this proposal will do much to
help achieve the required integration of the wealth of disparate
information, tools, models, and other relevant assets that are currently
dispersed in disorganized, incompatible, and/or inaccessible venues.   

 

Even more importantly, few plant biologists possess the skills to
accomplish the necessary syntheses alone.  The CI proposed here will
directly support graduate students, post-doctoral associates, and
faculty working together in interdisciplinary teams.  Beyond this, the
CI will be an educational tool in venues from K-12 to post-graduate
work, provide outreach to multiple clientele groups, and serve as a
basis for a variety of citizen science activities.