TY - JOUR
T1 - Constraint-Based Reconstruction and Analyses of Metabolic Models
T2 - Open-Source Python Tools and Applications to Cancer
AU - Ng, Rachel H.
AU - Lee, Jihoon W.
AU - Baloni, Priyanka
AU - Diener, Christian
AU - Heath, James R.
AU - Su, Yapeng
N1 - Publisher Copyright:
Copyright © 2022 Ng, Lee, Baloni, Diener, Heath and Su.
PY - 2022/7/7
Y1 - 2022/7/7
N2 - 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.
AB - 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.
KW - cancer
KW - constraint-based modeling
KW - genome-scale metabolic models
KW - metabolism
KW - omics
KW - python
KW - single-cell analysis
KW - systems biology
UR - http://www.scopus.com/inward/record.url?scp=85134598705&partnerID=8YFLogxK
U2 - 10.3389/fonc.2022.914594
DO - 10.3389/fonc.2022.914594
M3 - Review article
C2 - 35875150
AN - SCOPUS:85134598705
SN - 2234-943X
VL - 12
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 914594
ER -