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Review

Bioinformatics Methods for Constructing Metabolic Networks

by
Denis V. Petrovsky
1,
Kristina A. Malsagova
1,*,
Vladimir R. Rudnev
1,2,
Liudmila I. Kulikova
1,2,3,
Vasiliy I. Pustovoyt
4,
Evgenii I. Balakin
4,
Ksenia A. Yurku
4 and
Anna L. Kaysheva
1
1
Institute of Biomedical Chemistry, 119121 Moscow, Russia
2
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
3
Institute of Mathematical Problems of Biology RAS—The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 142290 Pushchino, Russia
4
State Research Center—Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency Center, 123098 Moscow, Russia
*
Author to whom correspondence should be addressed.
Processes 2023, 11(12), 3430; https://doi.org/10.3390/pr11123430
Submission received: 2 November 2023 / Revised: 28 November 2023 / Accepted: 7 December 2023 / Published: 14 December 2023

Abstract

Metabolic pathway prediction and reconstruction play crucial roles in solving fundamental and applied biomedical problems. In the case of fundamental research, annotation of metabolic pathways allows one to study human health in normal, stressed, and diseased conditions. In applied research, it allows one to identify novel drugs and drug targets and to design mimetics (biomolecules with tailored properties), as well as contributes to the development of such disciplines as toxicology and nutrigenomics. It is important to understand the role of a metabolite as a substrate (the product or intermediate participant of an enzymatic reaction) in cellular signaling and phenotype implementation according to the pivotal paradigm of biology: “one gene–one protein–one function (one trait)”. Due to the development of omics technologies, a vast body of data on the metabolome composition of living organisms has been accumulated over the past two decades. Systematization of the information on the roles played by metabolites in implementation of cellular signaling, as well as metabolic pathway reconstruction and refinement, have necessitated the development of bioinformatic tools for performing large-scale omics data mining. This paper reviews web-accessible databases relevant to metabolic pathways and considers the applications of the three types of bioinformatics methods for constructing metabolic networks (graphs for substrate–enzyme–product transformation; stoichiometric analysis of substrate–product transformation; and product retrosynthesis). It describes, step by step, a generalized algorithm for constructing biological pathway maps which explains to the researcher the workflow implemented in available bioinformatics tools and can be used to create new tools in projects requiring pathway reconstruction.
Keywords: metabolic pathway; bioinformatic tools; omics data; pathway databases; pathway maps metabolic pathway; bioinformatic tools; omics data; pathway databases; pathway maps

Share and Cite

MDPI and ACS Style

Petrovsky, D.V.; Malsagova, K.A.; Rudnev, V.R.; Kulikova, L.I.; Pustovoyt, V.I.; Balakin, E.I.; Yurku, K.A.; Kaysheva, A.L. Bioinformatics Methods for Constructing Metabolic Networks. Processes 2023, 11, 3430. https://doi.org/10.3390/pr11123430

AMA Style

Petrovsky DV, Malsagova KA, Rudnev VR, Kulikova LI, Pustovoyt VI, Balakin EI, Yurku KA, Kaysheva AL. Bioinformatics Methods for Constructing Metabolic Networks. Processes. 2023; 11(12):3430. https://doi.org/10.3390/pr11123430

Chicago/Turabian Style

Petrovsky, Denis V., Kristina A. Malsagova, Vladimir R. Rudnev, Liudmila I. Kulikova, Vasiliy I. Pustovoyt, Evgenii I. Balakin, Ksenia A. Yurku, and Anna L. Kaysheva. 2023. "Bioinformatics Methods for Constructing Metabolic Networks" Processes 11, no. 12: 3430. https://doi.org/10.3390/pr11123430

APA Style

Petrovsky, D. V., Malsagova, K. A., Rudnev, V. R., Kulikova, L. I., Pustovoyt, V. I., Balakin, E. I., Yurku, K. A., & Kaysheva, A. L. (2023). Bioinformatics Methods for Constructing Metabolic Networks. Processes, 11(12), 3430. https://doi.org/10.3390/pr11123430

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