PSAMM: A Portable System for the Analysis of Metabolic Models

The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies

Studying Escherichia coli in the mouse intestinal environment using 16S rRNA surveys (presentation slides)

<p>Slides used for presentation of Master's thesis.</p

Using PSAMM for the curation and analysis of genome-scale metabolic models

PSAMM is an open source software package that supports the iterative curation and analysis of genome-scale models (GEMs). It aims to integrate the annotation and consistency checking of metabolic models with the simulation of metabolic fluxes. The model representation in PSAMM is compatible with version tracking systems like Git, which allows for full documentation of model file changes and enables collaborative curations of large, complex models. This chapter provides a protocol for using PSAMM functions and a detailed description of the various aspects in setting up and using PSAMM for the simulation and analysis of metabolic models. The overall PSAMM workflow outlined in this chapter includes the import and export of model files, the documentation of model modifications using the Git version control system, the application of consistency checking functions for model curations, and the numerical simulation of metabolic models

Comparing the accuracy and efficiency of <i>FindPrimaryPairs</i> and <i>MapMaker</i> algorithms using annotations in the iJO1366, the KEGG RPAIR database, and the MetaCyc atom-mapping data.

<p>The MCC values were calculated according to descriptions in Materials and Methods, and the running time (seconds) was calculated based on the average time cost in seven independent runs of each algorithm on each reference set. TP–true positive; FP–false positive; FN–false negative; TN–true negative.</p

Overview of the internal workflow in PSAMM.

<p>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.</p

A diagram illustrating the modular representation of model components in the YAML format.

<p>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.</p

Bar chart showing the number of ambiguous reactions in the MetaCyc, KEGG, and iJO1366 reference datasets, where the two algorithms, <i>FindPrimaryPairs</i> and <i>MapMaker</i>, would potentially make arbitrary predictions of primary pairs.

<p>For <i>FindPrimaryPairs</i>, the reactions were counted for which the algorithm encountered ties on the top scoring reactant/product pairs in the last iteration of the primary pair assignment. For <i>MapMaker</i>, the reactions were counted for which the MILP solver would provide more than one optimal solutions that result in different primary pair predictions.</p

Distribution of blocked reactions in metabolic pathways.

<p>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.</p

Parameter values applied in the default implementation of <i>FindPrimaryPairs</i>.

<p>The weight assignments of carbon (<i>W</i>_<i>C</i>) and hydrogen (<i>W</i>_<i>H</i>) elements were determined based on the design of the algorithm. The weight assignment of other elements <i>W</i>_<i>other</i>, and the prior parameters <i>α</i>^{(<i>prior</i>)} and <i>β</i>^{(<i>prior</i>)} were determined based on a grid search of optimal parameters using the KEGG RPAIR annotations as reference data.</p

Stoichiometric inconsistencies in iKF1028 [70].

<p>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.</p