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Abstract Detail


Genetics Section

Remington, David L. [1], Fogel, Rebecca [1], Gove, Robert [2], Rychtar, Jan [2].

Toward a systems biology of plant life history variation.

Systems biology is an emerging discipline that seeks to understand the molecular mechanisms by which the genome shapes the development of complex phenotypes. Systems biology approaches generally combine development of predictive models for the function of focal processes and pathways with analyses of experimental “-omic” data to test and refine the models. Systems biology methods are increasingly being used to infer pathways and transcriptional networks shaping within-species genetic variation. As genome sequence and transcriptional profile data become increasingly accessible in non-model organisms, systems biology approaches offer unprecedented opportunities to address longstanding questions about the biological basis for life history trade-offs in plants and other complex organisms. For example, what are the relative roles of genetic variation in developmental programs vs. physiological processes in shaping adaptive variation in resource acquisition and allocation patterns?
In this talk, I suggest some key components of a systems biology approach to understanding adaptive variation in plant life history processes. These components include: (a) developing predictive taxon-specific models of plant life history that include key developmental and physiological traits; (b) techniques for analyzing correlated life history traits as integrated trait networks rather than as mere assemblages of individual traits; and (c) techniques for using genetic data from segregating populations (e.g. QTL studies) to convert correlative trait networks into directed cause-effect networks that can be used to infer the underlying genetic mechanisms and test predictive models. The framework I propose is initially feasible even in organisms with relatively limited genomic data, but readily accommodates addition of transcriptomic, proteomic and metabolomic data as it becomes available. Examples will be used from our ongoing research on the genetics of resource allocation in Arabidopsis lyrata.


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1 - University of North Carolina at Greensboro, Department of Biology, P.O. Box 26170, Greensboro, NC, 27402, USA
2 - University of North Carolina at Greensboro, Department of Mathematics and Statistics, P.O. Box 26170, Greensboro, NC, 27402, USA

Keywords:
systems biology
life history
trait networks
Arabidopsis lyrata
Quantitative trait loci (QTL).

Presentation Type: Oral Paper:Papers for Sections
Session: 5
Location: 182/I K Barber
Date: Monday, July 28th, 2008
Time: 9:15 AM
Number: 5006
Abstract ID:289


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