MOFA: Multi-Objective Flux Analysis for the COBRA Toolbox

Griesemer, Marc; Navid, Ali

doi:10.1101/2021.05.20.445041

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…For instance, in certain conditions, cells may aim to minimise glucose uptake and maximise ATP, a behaviour observed in experimental co-cultures [97]. This fact may explain the variety of Pareto-optimal solutions given by performing the MOFA routine [77]. We, however, presented in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Briefly, MOFA maps the n-1-dimensional surface of the Pareto front, where n is the number of objectives, by calculating a large set of Pareto solutions. That is, the routine maps an area of multiple criteria decision-making in the presence of trade-offs between two or more conflicting objectives [77, 78].…”

Section: Modelmentioning

confidence: 99%

“…As we assume, for simplicity's sake, that regardless of the biological functions of the two cell types, in our simulation, the biomass function carries sufficient knowledge about the biological system [74][75][76]. Our simulations implemented multi-objective optimisation using the MOFA routine for the COBRA package in MATLAB [77]. Briefly, MOFA maps the n-1-dimensional surface of the Pareto front, where n is the number of objectives, by calculating a large set of Pareto solutions.…”

Section: Multi-objective Functionmentioning

confidence: 99%

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Mathematical reconstruction of the metabolic network in anin-vitromultiple myeloma model

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It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies that exclusively utilise in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets that can translate into clinical therapies. Although this is increasingly recognised, and work addressing this is becoming routinary in a rapidly emerging field, much remains unknown.This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells. Together, our methodology and its results provide yet another step toward the relevance of studies of this type in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Multi-objective Functionmentioning

confidence: 99%

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“…Though a single cellular objective (i.e., maximizing biomass or minimizing ATP usage) is often reasonable when simulating prokaryotic metabolism, it may not be applicable for higher-order species. In particular, multiple objective simulations may better represent the metabolic behavior of mammalian cells [51][52][53] . Thus, we selected objective functions that collectively comprise the cellular goals of macrophages.…”

Section: Flux Predictions 341 Flux Balance Analysismentioning

confidence: 99%

Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

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2023

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Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

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“…As we assume, for simplicity's sake, that regardless of the biological functions of the two cell types, in our simulation, the biomass function carries sufficient knowledge about the biological system [75][76][77]. Our simulations implemented multi-objective optimisation using the MOFA routine for the COBRA package in MATLAB [78]. Briefly, MOFA maps the n-1-dimensional surface of the Pareto front, where n is the number of objectives, by calculating a large set of Pareto solutions.…”

Section: Co-culture Assembly Objective Function Medium and Phenotype ...mentioning

confidence: 99%

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PLoS Comput Biol

View full text Add to dashboard Cite

It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.

show abstract

MOFA: Multi-Objective Flux Analysis for the COBRA Toolbox

Cited by 4 publications

References 4 publications

Mathematical reconstruction of the metabolic network in anin-vitromultiple myeloma model

Mathematical reconstruction of the metabolic network in anin-vitromultiple myeloma model

Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model

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