Data driven integrative modeling of dynamic GRNs controlling organ size in plants

Faculty : Usha Vijayraghavan (Dept. of Microbiology and Cell Biology); Mohit Kumar Jolly (Center for BioSystems Science & Engineering)

Complex interactions between genes regulate cellular processes and control cell fate.  In several model organisms, genetic studies of developmental programs show many instances where few key master regulators have large cascading effects that cumulatively determine cell and tissue fate. Recent, experimental technologies have enabled monitoring the effects of master regulatory genes on the organism’s genome and its output. Such data, acquired over temporal and spatial scales, are providing inputs for deriving biologically realistic and predictive models of gene regulatory networks that control organ size and shape.

In higher plants, the flowering represents a remarkable transformation of emerging new lateral organs as flowers instead of leaves or branches, in their prior developmental state. Our knowledge on factors and mechanisms that regulate this developmental process comes largely from experimental studies with Arabidopsis, a model laboratory plant. These have very recently lead to models for floral specification in Arabidopsis thaliana. Using rice inflorescence (flowering stem) as a developmental model we have been investigating how homologous conserved transcription regulators control organ fate. Yet questions on diversity of organ size and shape remain be to addressed. For example petals of Aradidopsis and lodicules in rice areanalogous organs but with very unique shape, internal structures and functions.  These distinct morphologies and functions of the mature organs are hypothesized to arise from early region-specific effects of transcription regulators on downstream genes that control growth in organ primordia i.e., control cell proliferation and cell expansion during organ development.

The graduate student, admitted to the mathematical biology program, would develop dynamic gene-regulatory networks by integrating large experimental datasets acquired from multiple platforms (genome-wide expression analysis, protein -DNA interaction dataset, protein interaction datasets and imaging for cell division) each of which provide measurable variables. Network modeling could predict how interactions between network components can lead to changes in the state of a system—opening avenues for biological experimental validation. Comparisons with data emerging from other plant models including Arabidopsis will be insightful to understand how function shapes the network wiring properties. In this case, imaging data for markers for cell division together with molecular data will help build models for organ shape.