Using large scale, multi-cellular pathway modeling to understand cellular differentiation

Genetic expression and control pathways can be successfully modeled as electrical circuits. Given the vast quantity of genomic data, very large and complex genetic circuits can be constructed. To tackle such problems, the massively-parallel, electronic circuit simulator, Xyce™, is being adapted to address biological problems. Unique to this bio-circuit simulator is the ability to simulate not just one or a set of genetic circuits in a cell, but many cells and their internal circuits interacting through a common environment. Additionally, the circuit simulator Xyce can couple to the optimization and uncertainty analysis framework Dakota allowing one to find viable parameter spaces for normal cell functionality and required parameter ranges for unknown or difficult to measure biological constants. Currently, electric circuit analogs for common biological and chemical machinery have been created. Using such analogs, one can construct expression, regulation and reaction networks. Individual species can be connected to other networks or cells via non-diffusive or diffusive channels (i.e. regions where species diffusion limits mass transport). Within any cell, a hierarchy of networks may exist operating at different time-scales to represent different aspects of cellular processes. Though under development, this simulator can model interesting biological and chemical systems. Here, we investigate Dassow's Drosophila sp. cellular differentiation network's stability as a function of initial conditions. For these computations, a collection of 100 cells connected through a diffusive environment are simulated within the circuit modeler Xyce while the optimization engine, Dakota, controls cellular pathway parameters. Our findings agree with Dassow's that the overall network is fairly robust. However, when one includes smooth initial conditions the ranges of acceptable parameter values shrink as well as the complexity of the final solution.