A Software Architecture for Developmental Modeling in Plants: The Computable Plant Project

Victoria Gor, Bruce E. Shapiro, Henrik Jšnsson, Marcus Heisler, G. Venugopola Reddy, Elliot M. Meyerowitz and Eric Mjolsness

In: Bioinformatics of Genome Regulation and Structure, Second Biennial Edition, ed. N. A. Kolchanov, Kluwer Publications. (2005, in press).

Abstract

We present the software architecture of the Computable Plant Project, a multidisciplinary computationally based approach to the study of plant development. Arabidopsis thaliana is used as a model organism, and shoot apical meristem (SAM) development as a model process. SAMs are the plant tissues where regulated cell division and differentiation lead to plant parts such as flowers and leaves. We are using green fluorescent proteins to mark specific cell types and acquire time series of three-dimensional images via laser scanning confocal microscopy. To support this we have developed an interoperable architecture for experiment design that involves automated code generation, computational modeling, and image analysis. Automated image analysis, model fitting, and code generation allow us to explore alternative hypothesis in silico and guide in vivo experimental design. These predictions are tested using standard techniques such as inducible mutants and altered hormone gradients. The present paper focuses on the automated code generation architecture.