Section: New Results

Hierarchical modeling with BioRica

Participants : David James Sherman [correspondant] , Tiphaine Martin, Alice Garcia, Rodrigo Assar-Cuevas, Nicolás Loira.

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.

BioRica is a high-level hierarchical modeling framework for models combining continuous and discrete components. 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.

Rodrigo Assar formalized two strategies for integrating discrete control with continuous models, coefficient switches that control the parameters of the continuous model, and strong switches that choose different models. This was translated by Alice Garcia into a BioRica specification for hybrid systems that assures integrity of models, allowing composition, reconciliation, and reuse of models with SBML specifications. Rodrigo used this approach to describe two systems: wine fermentation kinetics, and cell fate decisions leading to bone and fat formation[11] . In the first, known models that describe the responses of yeast cells to different temperatures, resources and toxins, were reconciled using coefficient switches that gave the best adjustment of the model depending on the initial conditions and fermentation variable. In the second, a combination of accurate models to predict the bone and fat formation in response to activation of pathways such as the Wnt pathway, and changes of conditions affecting these functions such as increments in Homocysteine, were used to analyze the responses to treatments for osteoporosis and other bone mass disorders. Our hope is that this is a first step in obtaining in silico evaluations of medical treatments before testing them in vivo or in vitro.

Maria Llubères of the University of Puerto Rico visited Magnome and we established formal relationships between BioRica models and probabilistic boolean networks.