Exponential growth in scientific data set sizes and corresponding computation needs, forces scientists and engineers to structure and automate experiments in workflows running on distributed architectures. In eScience the flows typically evolve gradually from intensive experimentation and often involve multiple participants from separate organisations. Thus, there's a need for infrastructures supporting such highly dynamic and collaborative workflows. Despite much attention to scientific workflows in recent years, most existing systems tend to be single-user top-down approaches, which are inherently best suited for static and fixed flows with all steps known up front. In this work we introduce a simple general rule-based model for event-driven workflows based on data change triggers. A bottom-up workflow approach, that enables a high level of automation and allows dynamically changing flows – with or without manual user interaction. It is realised with an implementation on top of the Minimum intrusion Grid (MiG), which helps de-couple workflow design from underlying execution concerns, and provides built-in collaboration and sharing across organisation boundaries. However, the model itself applies to much wider range of scenarios, and other such possible implementation methods are briefly outlined.