Systems biology aims to identify and quantify the molecular components of dynamic biological networks, determining interactions between the various players and integrating the resulting information into system models . This research necessitates the use of an ensemble of correlative measurement technologies. Ideally, data should be acquired from groups of elementary samples, such as single cells, using high throughput technologies, in order to disentangle biological noise due to the stochastic nature of interaction networks . An experimental environment of this type will generate large quantities of heterogeneous but related data. This presents many challenges, including the key problem of tracking and integrating measurements made on a series of related samples across diverse technological platforms.
A number of software tools are available to handle data originating from high throughput experimental set-ups. These are technique specific. Examples are, Omero for light microscopy , Leginon for electron microscopy (EM)  and PRISM for high-throughput proteomics . Difficulties arise when several instruments and/or complex (automated) preparation steps are required for the research, as is often the case in a micro-fluidic pipeline. One way to create a multi-instrument solution would be to amalgamate the domain-specific software systems. The disadvantage is that combinatorial problems caused by required interaction between and coordination of the individual software packages, will increase rapidly with the number and complexity of the technologies involved. Furthermore, the correlation of individual datasets in relation to space and time will become progressively more difficult.
Flexible data management systems such as openBIS (open Biological Information System)  offer a partial solution, providing scalable data storage and retrieval, metadata integration and searching, and data source tracking. Although the platform-independent, web-based graphical user interface (GUI) of openBIS allows user management, authorization and configurable database browsing, it does not allow in depth data handling and does not support direct instrument control. These shortfalls are overcome by the new software presented in this paper, openBEB (open Biological Experiment Browser).
The requirements to be met become apparent when the following typical example is considered (see also results and discussion): Eukaryotic cells growing in miniaturized Petri dishes and are subjected to pulse chase experiments. During the experiment, the cells are observed by time-lapse light microscopy (LM). At specific time points, individual cells are lysed and prepared for further analysis by EM. Subsequently, specific features of the images, e.g., fluorescence signals detected by LM, are tracked over time. This scenario has three requirements: (i) Data acquisition and instrument control must be tightly integrated. (ii) Various data types must be collected and handled, e.g., image data and time-resolved “wave” data. (iii) The individual steps of the experiment must be correlated in space and time, e.g., EM data of an individual cell must be assigned and correlated to series of time-lapse LM images. OpenBEB provides a flexible, data-type agnostic core framework that performs the tedious “house keeping” tasks demanded, such as data management and the creation and maintenance of a unified hierarchical coordinate (HC) system. The latter establishes the relationships between experimental results that have to be retained in multi-scale space and time, a fundamental requirement for any correlative measurement. Furthermore, openBEB provides a plug-in manager that supports plug-ins for data-type specific tasks and instrument control. An internal macro system allows the control and coordination of these individual technology-specific modules. Furthermore, plug-ins can be used to connect openBEB to databases such as openBIS, facilitating data storage and synchronization.
OpenBEB furnishes the end-user with an environment for instrument control, data acquisition, visual inspection, advanced visualization, annotation, information correlation and metadata management. Of advantage for the developer, the software architecture allows the rapid integration of new instrument-specific modules, facilitating the use of correlative methods for systems biology, e.g., complex micro-fluidic set-ups.