MEWpy: a computational strain optimization workbench in Python

Pereira, Vítor; Cruz, Fernando; Rocha, Miguel

doi:10.1093/bioinformatics/btab013

Cited by 21 publications

(13 citation statements)

References 16 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The full workflow for data integration has been illustrated in Figure 2. The media conditions for each cell line are defined as a preliminary step, and have been described in Supplementary table 1.These were integrated using the MEWpy ‘get_simulator’ function, setting the final media dictionary as the ‘envcond’ argument (Pereira et al, 2021). A limitation of defining media for human GEMs is the inclusion of fetal bovine serum (FBS), which is a complex, difficult to define solution.…”

Section: Resultsmentioning

confidence: 99%

Constraint-based modeling predicts metabolic signatures of low- and high-grade serous ovarian cancer

Meeson

Schwartz

2023

Preprint

View full text Add to dashboard Cite

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways link to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modeling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies, thus highlighting a niche in disease research. Here, a novel method for constraint-based modeling has been developed, employing the genome-scale model Human1 and flux balance analysis (FBA), enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer as a case study, subtype-specific metabolic differences have been predicted, which have been supported with CRISPR-Cas9 data and an extensive literature review. Metabolic drivers of aggressive phenotypes, as well as pathways responsible for increased proliferation and chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate fatty acid biosynthesis and the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

show abstract

Section: Resultsmentioning

confidence: 99%

Constraint-based modeling predicts metabolic signatures of low- and high-grade serous ovarian cancer

Meeson

Schwartz

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It provides efficient, parallelized implementations of standard in silico strain design methods for predicting gene knockout strategies (OptGene [evolutionary algorithm] ( 40 ), OptKnock [linear programming] ( 42 ) and for predicting gene expression modulation targets (Differential FVA, Flux Scanning based on Enforced Objective Flux [FSEOF] ( 43 ). Instead of modulating genes, there are algorithms that optimize at the regulatory level by changing transcription factors, such as OptRAM ( 44 ) and OptORF ( 46 ) in MEWpy (Metabolic Engineering Workbench in python) ( 45 ). These simulation tools for strain design and in silico knockouts/perturbations can be easily adapted to study metabolism in the context of physiology and disease, especially cancer.…”

Section: Cobra Methods In Pythonmentioning

confidence: 99%

“…For example, we take the minimum expression of required subunits, but take the sum of isozyme expression. This calculation can be performed in Python packages like CORDA (Cost Optimization Reaction Dependency Assessment) ( 49 ) and MEWpy ( 45 ). Marín de Mas et al further improved GPR evaluation in their Python implementation of stoichiometric GPR (S-GPR) that considers the stoichiometry of protein subunits ( 104 ).…”

Section: Cobra Methods In Pythonmentioning

confidence: 99%

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

Lee

Baloni

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

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.

show abstract

“…As the first method of its kind, OptKnock ( Burgard et al , 2003 ) harnessed mixed-integer linear programming (MILP) for the rational design of microbial production hosts based on the concept of growth-coupled production. Today, a plethora of computational methods exists for various applications of metabolic design, for example, to enforce growth-coupled or even growth-independent production of a target chemical or to find synthetic lethals ( Cardoso et al , 2018 ; Jensen et al , 2019 ; Pereira et al , 2021 ; Schneider et al , 2020 , 2021 ; Tepper and Shlomi, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the increasing number of constraint-based modeling tools developed in Python [e.g. ScrumPy ( Poolman, 2006 ), COBRApy ( Ebrahim et al , 2013 ), cameo ( Cardoso et al , 2018 ), OptCouple ( Jensen et al , 2019 ), ReFramed ( https://zenodo.org/record/4700490 ), CNApy ( Thiele et al , 2021 ) and MEWpy ( Pereira et al , 2021 )], available packages with strain design algorithms are still distributed over different platforms including the Python packages mentioned above, the MATLAB-based COBRA toolbox ( Heirendt et al , 2019 ) and CellNetAnalyzer ( von Kamp et al , 2017 ), as well as the Java-based OptFlux ( Rocha et al , 2010 ). Moreover, each of these packages focuses only on one or very few of the published design methods.…”

Section: Introductionmentioning

confidence: 99%

StrainDesign: a comprehensive Python package for computational design of metabolic networks

et al. 2022

View full text Add to dashboard Cite

Summary Various constraint-based optimization approaches have been developed for the computational analysis and design of metabolic networks. Herein, we present StrainDesign, a comprehensive Python package that builds upon the COBRApy toolbox and integrates the most popular metabolic design algorithms, including nested strain optimization methods such as OptKnock, RobustKnock and OptCouple as well as the more general minimal cut sets approach. The optimization approaches are embedded in individual modules, which can also be combined for setting up more elaborate strain design problems. Advanced features, such as the efficient integration of GPR rules and the possibility to consider gene and reaction additions or regulatory interventions, have been generalized and are available for all modules. The package uses state-of-the-art preprocessing methods, supports multiple solvers and provides a number of enhanced tools for analyzing computed intervention strategies including 2D and 3D plots of user-selected metabolic fluxes or yields. Furthermore, a user-friendly interface for the StrainDesign package has been implemented in the GUI-based metabolic modeling software CNApy. StrainDesign provides thus a unique and rich framework for computational strain design in Python, uniting many algorithmic developments in the field and allowing modular extension in the future. Availability and implementation The StrainDesign package can be retrieved from PyPi, Anaconda and GitHub (https://github.com/klamt-lab/straindesign) and is also part of the latest CNApy package.

show abstract

MEWpy: a computational strain optimization workbench in Python

Cited by 21 publications

References 16 publications

Constraint-based modeling predicts metabolic signatures of low- and high-grade serous ovarian cancer

Constraint-based modeling predicts metabolic signatures of low- and high-grade serous ovarian cancer

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

StrainDesign: a comprehensive Python package for computational design of metabolic networks

Contact Info

Product

Resources

About