Abstract
BackgroundCell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, comprehensive genealogical information from these data. This information can be used to develop process models of cellular dynamics. However, so far there has been no structured, standardized way of annotating and storing the tracking results, which is critical for comparative analysis and data integration. The key requirement to be satisfied by an ontology is the representation of a cell’s change over time. Unfortunately, popular ontology languages, such as Web Ontology Language (OWL), have limitations for the representation of temporal information. The current paper addresses the fundamental problem of modeling changes of qualities over time in biomedical ontologies specified in OWL.ResultsThe presented analysis is a result of the lessons learned during the development of an ontology, intended for the annotation of cell tracking experiments. We present, discuss and evaluate various representation patterns for specifying cell changes in time. In particular, we discuss two patterns of temporally changing information: n-ary relation reification and 4d fluents. These representation schemes are formalized within the ontology language OWL and are aimed at the support for annotation of cell tracking experiments. We analyze the performance of each pattern with respect to standard criteria used in software engineering and data modeling, i.e. simplicity, scalability, extensibility and adequacy. We further discuss benefits, drawbacks, and the underlying design choices of each approach.ConclusionsWe demonstrate that patterns perform differently depending on the temporal distribution of modeled information. The optimal model can be constructed by combining two competitive approaches. Thus, we demonstrate that both reification and 4d fluents patterns can work hand in hand in a single ontology. Additionally, we have found that 4d fluents can be reconstructed by two patterns well known in the computer science community, i.e. state modeling and actor-role pattern.
Highlights
Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research
Patterns for modeling qualities In the current section we review two patterns frequently proposed for modeling temporal information, i.e. reification of n-ary relations and 4d fluents
In contrast to patterns 1 and 2, a Quality Assignment is not modeled as owl:ObjectProperty but instead as a reified owl:Class acting as a proxy between an Object and its Quality
Summary
Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, comprehensive genealogical information from these data. This information can be used to develop process models of cellular dynamics. Hierarchical and dynamic process [1]: it is a hallmark of all living systems that they change over time This is obvious during development, regeneration, or disease; but even under homeostatic conditions living matter is in a dynamic equilibrium; an example is the constant turnover in the hematopoietic system to maintain a certain number of blood cells. The goal is to reconstruct migration patterns, shape changes, changes in protein expression and, eventually, comprehensive genealogical information [5, 6] from the data. The analysis of the resulting videos has become a major bottleneck: manual analysis can be done on short sequences with few cells, but it is practically infeasible for
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