Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth

Abstract

We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes

“…Moreover, because any feature can be used as an output, these models could be used to infer relationships between variables that vary across scales, such as the effect of a signaling protein on cell morphology or size, which is not possible with most ODE models. 59 When coupled with optimizers that identify the best possible input sequences to produce a desired output, such models can enable model-predictive control of an equally broad set of biological variables. 53 The unique abilities of machine learning models can make other prediction tasks more feasible than they would be with traditional ODE approaches.…”

“…Deep learning methods are likely powerful and flexible enough to predict responses for many observable outputs to inducible inputs, giving them broad applications across processes such as metabolism and cell differentiation even in the absence of precise mechanistic information. Moreover, because any feature can be used as an output, these models could be used to infer relationships between variables that vary across scales, such as the effect of a signaling protein on cell morphology or size, which is not possible with most ODE models . When coupled with optimizers that identify the best possible input sequences to produce a desired output, such models can enable model-predictive control of an equally broad set of biological variables .…”

Nanoparticle-mediated cancer cell therapy: basic science to clinical applications

“…i Simulation of cancer growth with multiscale agent-based modelling [ 82 ]. j Vascular tumour growth (blue: proliferating tumour cells, yellow: quiescent tumour cells) [ 83 ] …”

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

“…Such models incorporate intracellular models of metabolism to inform cell- and tissue-level behaviors. 14,15 Lastly, constraint-based modeling stands out as a widely adopted form of metabolic modeling. This technique works by predicting the flux distributions within a network by applying a series of constraints, allowing for the exploration of metabolic behaviors under different physiological and pathological conditions.…”