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In the study of complex patterns in biology, mathematical and computational models are emerging as important tools. In addition to experimental approaches, these modeling tools have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to mycobacterial infection and tuberculous granuloma formation. We present an approach in which a computational model represents the interaction of the Mycobacterium infection with the innate immune system in zebrafish at a high level of abstraction. We use the Petri Net formalism to model the interaction between the key host elements involved in granuloma formation and infection dissemination. We define a qualitative model for the understanding and description of causal relations in this dynamic process. Complex processes involving cell-cell or cell-bacteria communication can be modeled at smaller scales and incorporated hierarchically into this main model; these are to be included in later elaborations. With the infection mechanism being defined on a higher level, lower-level processes influencing the host-pathogen interaction can be identified, modeled, and tested both quantitatively and qualitatively. This systems biology framework incorporates modeling to generate and test hypotheses, to perform virtual experiments, and to make experimentally verifiable predictions. Thereby it supports the unraveling of the mechanisms of tuberculosis infection.

We present a novel approach to modelling biological information using ontologies. The system interlinks three ontologies, comprising anatomical, developmental and taxonomical information, and includes instances of structures for different species. The framework is constructed for comparative analyses in the field of evolutionary development. We have applied the approach to the vertebrate heart and present four case studies of the functionality of the system, focusing on cross-species comparisons, developmental studies, physiological studies and 3D visualisation.

The Gene Expression Management System (GEMS) is a database system for patterns of gene expression. These patterns result from systematic whole-mount fluorescent in situ hybridization studies on zebrafish embryos. GEMS is an integrative platform that addresses one of the important challenges of developmental biology: how to integrate genetic data that underpin morphological changes during embryogenesis. Our motivation to build this system was by the need to be able to organize and compare multiple patterns of gene expression at tissue level. Integration with other developmental and biomolecular databases will further support our understanding of development. The GEMS operates in concert with a database containing a digital atlas of zebrafish embryo; this digital atlas of zebrafish development has been conceived prior to the expansion of the GEMS. The atlas contains 3D volume models of canonical stages of zebrafish development in which in each volume model element is annotated with an anatomical term. These terms are extracted from a formal anatomical ontology, i.e. the Developmental Anatomy Ontology of Zebrafish (DAOZ). In the GEMS, anatomical terms from this ontology together with terms from the Gene Ontology (GO) are also used to annotate patterns of gene expression and in this manner providing mechanisms for integration and retrieval. The annotations are the glue for integration of patterns of gene expression in GEMS as well as in other biomolecular databases. At the one hand, zebrafish anatomy terminology allows gene expression data within GEMS to be integrated with phenotypical data in the 3D atlas of zebrafish development. At the other hand, GO terms extend GEMS expression patterns integration to a wide range of bioinformatics resources.

This paper discusses current efforts to produce databases of gene expression for the major model embryos used in developmental biology. The efforts to build these resources were motivated by the need for immediate internet access to all types of research data, and the production of these databases is a major and new challenge for bioinformatics. Thus far bioinformatics has mainly been concerned with textually oriented resources and data, much of it concerned with gene and protein sequences. Because the genetic basis of developmental biology is integrated with developmental anatomy, these databases require the use of images to link molecular data with spatial information. In order to standardise database formats, digital atlases of some model systems are being produced that include integrated anatomical descriptions and these are being linked to appropriate genetic data. Integrating such image-based, searchable data into databases makes new demands on the field of bioinformatics and we consider here the imaging modalities that are used to obtain information and we discuss in particular the production of 3D images from serial sections. Next, we consider how to integrate textual and spatial descriptions of gene expression and the key tool needed to make this possible, i.e. anatomical nomenclature. A short review of internet resources on developmental biology is also given and future prospects for the development of these databases are discussed.