Victoria Gor

The systems biology markup language (SBML) : a medium for representation and exchange of biochemical network models

MOTIVATION Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY The specification of SBML Level 1 is freely available from http://www.sbml.org/

Modeling the organization of the WUSCHEL expression domain in the shoot apical meristem

MOTIVATION The above-ground tissues of higher plants are generated from a small region of cells situated at the plant apex called the shoot apical meristem. An important genetic control circuit modulating the size of the Arabidopsis thaliana meristem is a feed-back network between the CLAVATA3 and WUSCHEL genes. Although the expression patterns for these genes do not overlap, WUSCHEL activity is both necessary and sufficient (when expressed ectopically) for the induction of CLAVATA3 expression. However, upregulation of CLAVATA3 in conjunction with the receptor kinase CLAVATA1 results in the downregulation of WUSCHEL. Despite much work, experimental data for this network are incomplete and additional hypotheses are needed to explain the spatial locations and dynamics of these expression domains. Predictive mathematical models describing the system should provide a useful tool for investigating and discriminating among possible hypotheses, by determining which hypotheses best explain observed gene expression dynamics. RESULTS We are developing a method using in vivo live confocal microscopy to capture quantitative gene expression data and create templates for computational models. We present two models accounting for the organization of the WUSCHEL expression domain. Our preferred model uses a reaction-diffusion mechanism in which an activator induces WUSCHEL expression. This model is able to organize the WUSCHEL expression domain. In addition, the model predicts the dynamical reorganization seen in experiments where cells, including the WUSCHEL domain, are ablated, and it also predicts the spatial expansion of the WUSCHEL domain resulting from removal of the CLAVATA3 signal. AVAILABILITY An extended description of the model framework and image processing algorithms can be found at http://www.computableplant.org, together with additional results and simulation movies. SUPPLEMENTARY INFORMATION http://www.computableplant.org/ and alternatively for a direct link to the page, http://computableplant.ics.uci.edu/bti1036 can be accessed.

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.

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.

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.

Autonomy enables new science missions

The challenge of space flight in NASA’s future is to enable smaller, more frequent and intensive space exploration at much lower total cost without substantially decreasing mission reliability, capability, or the scientific return on investment. The most effective way to achieve this goal is to build intelligent capabilities into the spacecraft themselves. Our technological vision for meeting the challenge of returning quality science through limited communication bandwidth will actually put scientists in a more direct link with the spacecraft than they have enjoyed to date. Technologies such as pattern recognition and machine learning can place a part of the scientist’s awareness onboard the spacecraft to prioritize downlink or to autonomously trigger time-critical follow-up observations—particularly important in flyby missions—without ground interaction. Onboard knowledge discovery methods can be used to include candidate discoveries in each downlink for scientists’ scrutiny. Such capabilities will allo...