Minimizing the number of optimizations for efficient community dynamic flux balance analysis

Brunner, James D.; Chia, Nicholas

doi:10.1371/journal.pcbi.1007786

Cited by 18 publications

(12 citation statements)

References 53 publications

(129 reference statements)

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“…Therefore, to obtain the intracellular metabolism more accurately, the dynamic flux balance method (DFBA) was used in this study to simulate the biological metabolic process by combining the flux of extracellular metabolites obtained by the dynamic model as the constraint. DFBA is divided into two optimization methods, dynamic optimization method (DOA) and static optimization method (SOA) 28,29 . DOA uses the method of nonlinear programming to solve the optimization problem in the whole process, while SOA divides the whole fermentation process into several periods, calculates each period with the method of linear programming, and finally integrates the solution of the segments.…”

Section: Methodsmentioning

confidence: 99%

Dynamic flux balance analysis of 1,3‐propanediol production by clostridium butyricum fermentation

Pan,

Wang,

Wang

et al. 2023

Biotechnology Progress

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To study the relationship between the yield of 1,3‐propanediol (1,3‐PDO) and the flux change of the Clostridium butyricum metabolic pathway, an optimized calculation method based on dynamic flux balance analysis was used by combining genome‐scale flux balance analysis with a kinetic model. A more comprehensive and extensive metabolic pathway was obtained by optimization calculations. The primary extended branches include: the dihydroxyacetone node, which enters the pentose phosphate pathway; the α‐oxoglutarate node, which has synthetic metabolic pathways for glutamic acid and amino acids; and the serine and homocysteine nodes, which produce cystathionine before homocysteine enters the methionine cycle pathway. According to the expanded metabolic network, the flux distribution of key nodes in the metabolic pathway and the relationship between the flux distribution ratio of nodes and the yield of 1,3‐PDO were analyzed. At the dihydroxyacetone node, the flux of dihydroxyacetone converted to dihydroxyacetone phosphate was positively correlated with the yield of 1,3‐PDO. As an important intermediate product, the flux change in the metabolic pathway of α‐oxoglutarate reacting with amino acids to produce glutamic acid is positively correlated with the yield. When pyruvate was used as the central node to convert into lactic acid and α‐oxoglutarate, the proportion of branch flux was negatively correlated with the yield of 1,3‐PDO. These studies provide a theoretical basis for the optimization and further study of the metabolic pathway of C. butyricum.

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Section: Methodsmentioning

confidence: 99%

Dynamic flux balance analysis of 1,3‐propanediol production by clostridium butyricum fermentation

Pan,

Wang,

Wang

et al. 2023

Biotechnology Progress

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“…Community modeling of the human gut microbiome reveals community-level function and cross-feeding interactions, as demonstrated by Python package MICOM ( 75 ). Community models are further extended using dynamic FBA of microbial communities, which can be efficiently calculated using Python package called surfin_fba that reduces the number of optimization timesteps when modeling communities ( 76 ). Early attempts to model human cell populations were explored using MATLAB, beginning with popFBA that simulated clones of cancer cells with identical stoichiometry and capacity constraints while allowing extracellular fluxes ( 130 ).…”

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.

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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.

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“…In this study, we evaluated a total of twenty-four tools/approaches based on steady-state (9) [43,[45][46][47][56][57][58][59][60][61], dynamic (8) [62][63][64][65][66][67][68][69] and spatio-temporal (7) [34,[36][37][38][39][70][71][72] methods according to their usability to model microbial communities using GEMs. A description of the tools/approaches is found in the Supplementary File1.…”

Section: Overview Of Constrained-based Modeling Tools/approachesmentioning

confidence: 99%

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Scott

Benito-Vaquerizo

Zimmermann

et al. 2023

Preprint

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Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation byC. autoethanogenumandC. kluyverifor the static tools, b) glucose/xylose fermentation with engineeredE. coliandS. cerevisiaefor the dynamic tools, and c) a Petri dish ofE. coliandS. entericafor tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

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Minimizing the number of optimizations for efficient community dynamic flux balance analysis

Cited by 18 publications

References 53 publications

Dynamic flux balance analysis of 1,3‐propanediol production by clostridium butyricum fermentation

Dynamic flux balance analysis of 1,3‐propanediol production by clostridium butyricum fermentation

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

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

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