Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. © 2014 Hogg et al

Gender difference in HIV-1 RNA viral loads.

OBJECTIVES: To test and characterize the dependence of viral load on gender in different countries and racial groups as a function of CD4 T-cell count. METHODS: Plasma viral load data were analysed for > 30,000 HIV-infected patients attending clinics in the USA [HIV Insight (Cerner Corporation, Vienna, VA, USA) and Plum Data Mining LLC (East Meadow, NY, USA) databases] and the Netherlands (Athena database; HIV Monitoring Foundation, Amsterdam, Netherlands). Log-normal regression models were used to test for an effect of gender on viral load while adjusting for covariates and allowing the effect to depend on CD4 T-cell count. Sensitivity analyses were performed to test the robustness of conclusions to assumptions regarding viral loads below the lower limit of quantification (LLOQ). RESULTS: After adjusting for covariates, women had (nonsignificantly) lower viral loads than men (HIV Insight: -0.053 log(10) HIV-1 RNA copies/mL, P = 0.202; Athena: -0.005 log(10) copies/mL, P = 0.667; Plum: -0.072 log(10) copies/mL, P = 0.273). However, further investigation revealed that the gender effect depended on CD4 T-cell count. Women had consistently higher viral loads than men when CD4 T-cell counts were at most 50 cells/microL, and consistently lower viral loads than men when CD4 T-cell counts were greater than 350 cells/microL. These effects were remarkably consistent when estimated independently for the racial groups with sufficient data available in the HIV Insight and Plum databases. CONCLUSIONS: The consistent relationship between gender-related differences in viral load and CD4 T-cell count demonstrated here explains the diverse findings previously published