Table 1.
Overview of select scientific software with relevance to MASSpy functionality.
Fig 1.
(A) MASSpy expands COBRApy to provide constraint-based methods for obtaining flux states. (B) Thermodynamic principles are utilized by MASSpy to sample concentration solution spaces and to evaluate how thermodynamic driving forces shift under different metabolic conditions. (C) MASSpy enables dynamic simulation of models to characterize transient dynamic behavior and contains ensemble modeling methods to represent biological uncertainty. (D) Network properties such as relevant timescales and system stability are characterized by MASSpy using various linear algebra and analytical methods. (E) MASSpy contains built-in functions that enable the visualization of dynamic simulation results. (F) Mechanisms of enzymatic regulation are explicitly modeled in MASSpy through enzyme modules, enabling computation of catalytic activities and functional states of enzymes.
Table 2.
Comparison of explicit helper methods for selected dynamic modeling tools.
Fig 2.
Enzyme modules are explicit representations of enzymatic regulatory mechanisms.
(A) The reaction catalyzed by pyruvate kinase is replaced with the stoichiometric description of the enzymatic mechanism. The steady state values obtained after simulating a 50% increase of ATP utilization are mapped onto a metabolic pathway map drawn using Escher [44]. The colors represent flux values and range from red to purple to gray, with red indicating higher flux values and gray indicating lower flux values. (B) Enzyme modules provide a network-level perspective of regulation mechanisms by plotting systemic quantities against fractional states of enzymes as described in Yurkovich et al. [24]. (C) The different signals of the enzyme module can be observed to provide enzyme-level resolution of the regulatory response.
Fig 3.
A MASSpy workflow for ensemble creation and modeling using MCMC sampling.
A typical ensemble modeling workflow using MASSpy to generate and assemble an ensemble of stable kinetic models for dynamic simulation and analysis. (A) The solution spaces for fluxes and concentrations are sampled using MCMC sampling to generate data for candidate model states. Rate constants are obtained through parameter fitting for elementary rate constants and computation of PERCs. (B) Sampling data is integrated into the candidate models, and models are subsequently filtered based on their stability to assemble the ensemble of stable dynamic models. Once assembled, the ensemble is used to study biological variability in the network of interest through (C) dynamic simulation and (D) analysis.
Fig 4.
Comparison of free energy and isozyme fractional abundances for carbon sources.
(A) The Gibbs free energy represents the thermodynamic driving force, shifting the metabolic state depending on the carbon source. (B) The glycolytic subnetwork extracted from E. coli iML1515 consists of 12 reactions represented by the 17 enzyme modules. (C) The fractional abundance for each enzyme form can be computed and compared for the different isozyme pairs, providing insight into how the catalytic activity is distributed across the isozymes in glucose and pyruvate growth conditions. The fractional abundances for all enzymes can be found in the supplement (S2 Fig).