Tracking Cell Signals in Fluorescent Images

In this paper we present the techniques for tracking cell signal in GFP (Green Fluorescent Protein) images of growing cell colonies. We use such tracking for both data extraction and dynamic modeling of intracellular processes. The techniques are based on optimization of energy functions, which simultaneously determines cell correspondences, while estimating the mapping functions. In addition to spatial mappings such as affine and Thin-Plate Spline mapping, the cell growth and cell division histories must be estimated as well. Different levels of joint optimization are discussed. The most unusual tracking feature addressed in this paper is the possibility of one-to-two correspondences caused by cell division. A novel extended softassign algorithm for solutions of one-to-many correspondences is detailed in this paper. The techniques are demonstrated on three sets of data: growing bacillus Subtillus and e-coli colonies and a developing plant shoot apical meristem. The techniques are currently used by biologists for data extraction and hypothesis formation

Morphogenesis in Plants: Modeling the Shoot Apical Meristem, and Possible Applications

A key determinant of overall morphogenesis in flowering plants such as Arabidopsis thaliana is the shoot apical meristem (growing tip of a shoot). Gene regulation networks can be used to model this system. We exhibit a very preliminary two-dimensional model including gene regulation and intercellular signaling, but omitting cell division and dynamical geometry. The model can be trained to have three stable regions of gene expression corresponding to the central zone, peripheral zone, and rib meristem. We also discuss a space-engineering motivation for studying and controlling the morphogenesis of plants using such computational models

A software architecture for developmental modeling in plants: The computable plant project.

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