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

Abstract

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.

A Mathematical Exploration of SDH-b Loss in Chromaffin Cells

“…Briefly, this method deduces intracellular flux patterns from mass isotopomer distributions measured via mass spectrometry. The process and established protocols were followed as described in Vera-Siguenza et al [27], Antoniewicz [28], Young [29], Rahim et al [30]. We conducted this analysis using the Isotopomer Network Compartmental Analysis (INCA) MATLAB routine, suitable for both steady-state and isotopically non-stationary metabolic flux analysis [29,30].…”

“…1). In this model (see Supplementary Material S.7), the osmotic gradient between the cytoplasm and the extracellular space influences the cytoplasmic volume, while the gradient between the cytoplasm and the mitochondrial matrix regulates mitochondrial volume [27,33,34,35,36,40,49,50]. Note, however, that this assumes that the plasma membrane cannot withstand hydrostatic pressure gradients.…”

“…Following the protocols outlined by Antoniewicz [28] and Vera-Siguenza et al [27], we began by monitoring the proliferation rates of WT and SDH-b K.O. cell lines over a 48-hour period (Fig.…”

“…10d demonstrates an increase in the activity of the Na + /H + antiporter, indicative of the cellular effort to regulate intracellular pH under acidic stress conditions commonly induced by increased lactate production. This response is critical, as the ability of the NHE to regulate acidity changes in the cytoplasm signifies the model's correct response to hypoxia [81,27].…”