Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.

Autor:	Ng RH; Institute for Systems Biology, Seattle, WA, United States.; Department of Bioengineering, University of Washington, Seattle, WA, United States., Lee JW; Medical Scientist Training Program, University of Washington, Seattle, WA, United States.; Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States., Baloni P; Institute for Systems Biology, Seattle, WA, United States., Diener C; Institute for Systems Biology, Seattle, WA, United States., Heath JR; Institute for Systems Biology, Seattle, WA, United States.; Department of Bioengineering, University of Washington, Seattle, WA, United States., Su Y; Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.; Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2022 Jul 07; Vol. 12, pp. 914594. Date of Electronic Publication: 2022 Jul 07 (Print Publication: 2022).
DOI:	10.3389/fonc.2022.914594
Abstrakt:	The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets. Competing Interests: JH is a board member of PACT Pharma and Isoplexis and receives support from Gilead, Regeneron and Merck. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Ng, Lee, Baloni, Diener, Heath and Su.)
Databáze:	MEDLINE
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