Background : A recurring challenge for in silico modeling of cell behavior is that experimentally validated models are so focused in scope that it is difficult to repurpose them. Hierarchical modeling is one way of combining specific models into networks. Effective use of hierarchical models requires both formal definition of the semantics of such composition, and efficient simulation tools for exploring the large space of complex behaviors. Objectives : BioRica (Soueidan et al, 2007) is a high-level hierarchical modeling framework integrating discrete and continuous multi-scale dynamics within the same semantics domain. It is an adaptation of the AltaRica formalism (Arnold et al., 2000). It explicitly addresses model reusability, repurposing and other engineering best practices that are necessary for sustainable, incremental development of comprehensive models incorporating individually validated components. The goal of the present work was to make the BioRica framework accessible for a wider audience. Methods : The BioRica approach expresses each existing model (in SBML) as a BioRica node, which are hierarchically composed to build a BioRica system. Individual nodes can be of two types. Discrete nodes are composed of states, and transitions described by constrained events, which can be non deterministic. This captures a range of existing discrete formalisms (Petri nets, finite automata, etc.). Stochastic behavior can be added by associating the likelihood that an event fires when activated. Markov chains or Markov decision processes can be concisely described. Timed behavior is added by defining the delay between an event's activation and the moment that its transition occurs. Continuous nodes are described by ODE systems, potentially a hybrid system whose internal state flows continuously while having discrete jumps. Results : The system has been implemented as a distributable software package. The BioRica model compiler and associated tools are available from the INRIA, (address to be provided). Discussion : By providing a reliable and functional software tool backed by a rigorous semantics, we hope to advance real adoption of hierarchical modeling by the systems biology community. By providing an understandable and mathematically rigorous semantics, this will make is easier for practicing scientists to build practical and functional models of the systems they are studying, and concentrate their efforts on the system rather than on the tool.