ABSTRACT
Stochastic modeling has become an essential tool for studying biochemical reaction networks. There is a growing need for user-friendly and feature-complete software for model design and simulation. To address this need, we present GillesPy2, an open-source framework for building and simulating mathematical and biochemical models. GillesPy2, a major upgrade from the original GillesPy package, is now a stand-alone Python 3 package. GillesPy2 offers an intuitive interface for robust and reproducible model creation, facilitating rapid and iterative development. In addition to expediting the model creation process, GillesPy2 offers efficient algorithms to simulate stochastic, deterministic, and hybrid stochastic-deterministic models.
ABSTRACT
Mass transport in diffusive gradients in thin-film passive samplers is restricted to diffusion through a gel layer of agarose or agarose cross-linked polyacrylamide (APA). The gel layer diffusion coefficient, DGel, is typically determined using a standard analysis (SA) based on Fick's first law from two-compartment diffusion cell (D-Cell) tests. The SA assumes pseudo-steady-state flux, characterized by linear sink mass accumulation-time profiles with a typical threshold R2 ≥ 0.97. In 72 D-Cell tests with nitrate, 63 met this threshold, but the SA-determined DGel ranged from 10.1 to 15.8 × 10-6 cm2·s-1 (agarose) and 9.5 to 14.7 × 10-6 cm2·s-1 (APA). A regression model developed with the SA to account for the diffusive boundary layer had 95% confidence intervals (CIs) on DGel of 13 to 18 × 10-6 cm2·s-1 (agarose) and 12 to 19 × 10-6 cm2·s-1 (APA) at 500 rpm. A finite difference model (FDM) developed based on Fick's second law with non-steady-state (N-SS) flux decreased uncertainty in DGel tenfold. The FDM-captured decreasing source compartment concentrations and N-SS flux in the D-Cell tests and, at 500 rpm, the FDM-determined DGel ± 95% CIs were 14.5 ± 0.2 × 10-6 cm2·s-1 (agarose) and 14.0 ± 0.3 × 10-6 cm2·s-1 (APA), respectively.
