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Plant Systems Biology: From predictive network modeling to trait evolution

Center for Genomics & Systems Biology, New York University
on 2012/04/27 at 11:00


The ultimate goal of Systems Biology is to predict how network states change under untested conditions, or in response to modifications. Our first step to enable this ambitious goal in Plant Systems Biology, was the creation of an Arabidopsis multi-network where the “edges” connecting gene "nodes" are supported by metabolic, protein, RNA connections, which is embodied in a software platform for Plant Systems Biology called "VirtualPlant" ( ) [1]. By querying this Arabidopsis multi-network with transcriptome data, the resulting subnetworks can be used to derive testable hypotheses. Validated examples of hypotheses derived and validated from such subnetworks include a role for the central clock gene CCA1 as a hub of an organic-N regulated network [2], and a miR-TF motif involved in the N-regulation of lateral root outgrowth [3]. To further our predictive network modeling studies, we used a Machine-learning approach called "State Space Modeling" to analyze a High Resolution Dynamic Transcriptome (HRDT) dataset, and generated predictive network models for nitrogen regulatory networks that were validated in silico (using left-out data), and also experimentally [4]. Most recently, we are exploiting Arabidopsis and its natural variants, to identify gene networks associated with developmental adaptations of roots to nutrients in the environment [5]. To extend our genomic and systems biology studies beyond Arabidopsis, we have developed and exploited phylogenomic approaches to identify genes associated with the evolution of traits across a wide range of plant species. For this purpose, we have constructed a phylogenomic-scale tree called BIGPLANTv1.0, using 22,833 orthologous genes spanning 150 sequenced plant genomes [6]. This phylogenomic tree has been used to identify the genes and overrepresented GO-terms that support specific nodes in the evolution of land plants, thus enabling a functional phylogenomic approach for trait-to-gene discovery.


[1] Katari et al. (2010). Plant Physiol 152: 500-515.

[2] Gutiérrez (2008). PNAS 105: 4939.

[3] Gifford (2008). PNAS 105: 803.

[4] Krouk (2010). Genome Biol. 11: R123.

[5] Ruffel et al. (2011). PNAS 108: 18524-18529;

[6] Lee et al (2011). PLoS Genetics (12): e1002411.

This research is supported by NIH, NSF Plant Genome, NSF-DBI, NSF-2010, and DOE.