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Review
. 2021 Jan 18;22(1):109-126.
doi: 10.1093/bib/bbz104.

Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology

Peter D Karp  1 , Peter E Midford  1 , Richard Billington  1 , Anamika Kothari  1 , Markus Krummenacker  1 , Mario Latendresse  2 , Wai Kit Ong  1 , Pallavi Subhraveti  1 , Ron Caspi  1 , Carol Fulcher  1 , Ingrid M Keseler  1 , Suzanne M Paley  1
Affiliations
Review

Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology

Peter D Karp et al. Brief Bioinform. .

Abstract

Motivation: Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets.

Results: In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages.

Availability: The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com.

Contact: pkarp@ai.sri.com.

Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Keywords: Computational genomics; metabolic models; metabolic pathways; systems biology.

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Figures

Figure 1
Figure 1
Major subsystems of Pathway Tools. Blue: PathoLogic. Yellow: Navigator. Purple: MetaFlux. Pink: Editors.
Figure 2
Figure 2
Top portion of trpA gene page showing tabbed sections and regulation summary diagram using new ‘web graphics’ rendering.
Figure 3
Figure 3
The top of the ‘Reactions’ tab of the trpA gene page.
Figure 4
Figure 4
Result of MORS routes to 4-methylphenyl sulfate.
Figure 5
Figure 5
Example spatial grid output from a MetaFLux community model showing two colonies of E coli K-12 at the corners, and concentrations of four metabolites from the FBA model.
Figure 6
Figure 6
Truncated top-level dashboard view of a gene expression time series data set [29] for E. coli, showing changes in gene expression after transition to aerobic conditions at [Formula: see text]. Other panels include Regulation, Cellular Processes, Cell Exterior and Response to Stimulus
Figure 7
Figure 7
Dashboard drill down to expression of genes producing proteins involved in response to DNA damage, same data set as Figure 6.
Figure 8
Figure 8
Results of Pathway Covering.
Figure 9
Figure 9
MultiOmics Explainer graph showing genes from [32] that, when knocked out, cause levels of cis-aconitate to increase. In particular, these genes indicate a connection between enterobactin-related genes and cis-aconitate metabolism that might not otherwise have been obvious.
See this image and copyright information in PMC

References

    1. Karp PD, Billington R, Caspi R, et al. The BioCyc collection of microbial genomes and metabolic pathways. Briefings in Bioinformatics 2017; 20:1085–1093. - PMC - PubMed
    1. Karp PD, Latendresse M, Paley SM, et al. Pathway Tools version 19.0: Integrated software for pathway/genome informatics and systems biology. arXiv, 2015, 1–79.
    1. Green ML, Karp PD. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 2004; 5(1): 76. - PMC - PubMed
    1. Duru IC, Laine P, Andreevskaya M, et al. Metagenomic and metatranscriptomic analysis of the microbial community in swiss-type maasdam cheese during ripening. Int J Food Microbiol 2018; 281:10–23. - PubMed
    1. Andreevskaya M, Jaaskelainen E, Johansson P, et al. Food spoilage-associated leuconostoc, lactococcus, and lactobacillus species display different survival strategies in response to competition. Appl Environ Microbiol 2018; 84(13): e00554-18. - PMC - PubMed

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