  Running scientific workflows on a supercomputer can be a daunting task for a
scientific domain specialist. Workflow management solutions (WMS) are a
standard method for reducing the complexity of application deployment on high
performance computing (HPC) infrastructure. We introduce the design for a
middleware system that extends and combines the functionality from existing
solutions in order to create a high-level, staged user-centric
operation/deployment model. This design addresses the requirements of several
use cases in the life sciences, with a focus on neuroscience. In this
manuscript we focus on two use cases: 1) three coupled neuronal simulators (for
three different space/time scales) with in-transit visualization and 2) a
closed-loop workflow optimized by machine learning, coupling a robot with a
neural network simulation. We provide a detailed overview of the
application-integrated monitoring in relationship with the HPC job. We present
here a novel usage model for large scale interactive multi-application
workflows running on HPC systems which aims at reducing the complexity of
deployment and execution, thus enabling new science.
