Cancer Biomarkers in the Era of Systems Biology

Firdous, Shazia; Srivastava, Sunil Kumar; Saha, Sudipto

doi:10.1007/978-981-19-1953-4_3

Cited by 2 publications

(2 citation statements)

References 144 publications

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“…Co-culture studies are possible today thanks to the technological advancement seen during the past couple of decades [17]. Nevertheless, the study of intercellular metabolic interactions across microenvironments remains incredibly challenging [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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

Vera-Siguenza,

Escribano-Gonzalez,

Serrano-Gonzalo

et al. 2023

PLoS Comput Biol

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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 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.

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Section: Introductionmentioning

confidence: 99%

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

Vera-Siguenza,

Escribano-Gonzalez,

Serrano-Gonzalo

et al. 2023

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

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

Vera-Sigüenza

Escribano-Gonzalez

Serrano-Gonzalo

et al. 2022

Preprint

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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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Cancer Biomarkers in the Era of Systems Biology

Cited by 2 publications

References 144 publications

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

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

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

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