Fig 1.
Comparing the running time of different functions in PSAMM and COBRA.
The running time for both the PSAMM overall function (blue) and the PSAMM problem-solving steps (red) were calculated, and the running time for COBRA only included the problem-solving step (green). Each value represents a median of seven simulation runs using the same specifications, and the error bars indicate the 25th and the 75th percentiles.
Fig 2.
Overview of the internal workflow in PSAMM.
The five main components include: (1) user interface, (2) model input/output, (3) model representation, (4) linear programming utilities, and (5) model checking/simulation. Connections among these components form the internal workflow of PSAMM.
Table 1.
List of commands supported in the psamm-model program.
Additional parameters can be specified for the commands, for example to select a specific implementation among multiple algorithms, enable/disable thermodynamic constraints, or select linear programming solvers, etc. Details about these parameters are available through the -h or --help options for each command.
Table 2.
List of models that had inconsistencies in SBML syntax.
Fig 3.
An illustration of the PSAMM YAML format.
(a) This diagram shows an example of the YAML model format, which includes a central model definition (model.yaml) and multiple annotation files. Each box indicates a file with a possible filename indicated above the box, and the text within is a snapshot of the file content. (b) An example showing how changes can be tracked in a PSAMM YAML file (biomass.yaml) using the Git version control system in command line. The text highlighted in red indicates the stoichiometry of the compound arg-L in an old version of the biomass function, while the text highlighted in green indicates the updated value in a new version of the model. Additional examples of applying Git version control on the YAML format are provided in the supplemental materials (S2–S5 Texts).
Table 3.
Stoichiometric inconsistencies in iKF1028 [70].
Reaction: the reaction identifiers in the GEM; Equation: the reaction equations; H left/right: the total number of H atoms at the left/right side of the equations; H residue: the differences between the number of H atoms at the left versus the right side of the equations. Two reactions, RR08939 and IR01815, are shown at the bottom of the table, which correspond to the balanced version of the inconsistent reactions RR00610 and IR04287, respectively. Both pairs (marked with * and **, respectively) were present in iKF1028, rendering the overall model stoichiometrically inconsistent.
Fig 4.
Distribution of blocked reactions in metabolic pathways.
The GEMs were represented in each metabolic pathway as a solid circle. The color of the circles corresponds to the year in which a GEM was published (color legend was shown on the right, and the year of publication ranges from 2003 to 2014). The area of the circle is proportional to the total number of reactions in the pathway, and its vertical position indicates the fraction of reactions that are blocked. The median fractions were indicated by a red mark for each pathway, and models discussed in the main text were highlighted.
Fig 5.
A diagram illustrating the modular representation of model components in the YAML format.
The data structure is divided into the static components of model annotation (A) and the dynamic components of simulation settings (B). The reaction and compound annotation databases are associated with a number of required (highlighted in black, e.g. “- id” and “- equation” for reactions) and optional (gray) data entries, and user-defined, model-specific data entries are permitted in the annotation databases. The simulation settings can be represented with various combinations of the model, limits, and media files. Alternative conditions may be defined using a number of alternative modules that can be switched with one another.