When Are Two Workflows the Same?

In the area of workflow management, one is confronted with a large number of competing languages and the relations between them (e.g. relative expressiveness) are usually not clear. Moreover, even within the same language it is generally possible to express the same workflow in different ways, a feature known as variability. This paper aims at providing some of the formal groundwork for studying relative expressiveness and variability by defining notions of equivalence capturing different views on how workflow systems operate. Firstly, a notion of observational equivalence in the absence of silent steps is defined and related to classical bisimulation. Secondly, a number of equivalence notions in the presence of silent steps are defined. A distinction is made between the case where silent steps are visible (but not controllable) by the environment and the case where silent steps are not visible, i.e., there is an alternation between system events and environment interactions. It is shown that these notions of equivalence are different and do not coincide with classical notions of bisimulation with silent steps (e.g. weak and branching)

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments