Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

Roy, Mahua; Finley, Stacey D.

doi:10.3389/fphys.2017.00217

Cited by 55 publications

(63 citation statements)

References 119 publications

(157 reference statements)

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“…The uptake rate of each nutrient is determined by the kinetic parameters of the ODE model as well as the amount available at the center of mass of the generalized cell. While the baseline ODE model framework is based on literature sources (21,32), additional features such as tumor response to acidosis and compressive stress (33) were added. The various components of the model, and the parameters associated with them, are as described below.…”

Section: Methodsmentioning

confidence: 99%

“…The model parameters were sampled within the ranges tabulated in Table 1 and trained so as to reproduce, both quantitatively and qualitatively, the growth profile of a multicellular spheroid. Our intracellular metabolic core model is trained to data from PaTu8988T cells (21); however, we were unable to find tumor spheroid growth data for this specific cell line. Thus, we chose a 10-fold change in tumor volume in approximately 15 days to be the standard for the model-simulated tumor growth.…”

Section: Cell Mitosis (Cell Division) Occurs When a Cell's Volume Incmentioning

confidence: 97%

“…Cell growth in the model is limited to proliferating cells (PCancer and PStem), with their growth dependent on the amount of ATP produced by the cell (21). The rate of production of ATP depends on the total amount of intracellular glucose and glutamine, as well as additional intracellular components generated from glycolysis, the tricarboxylic acid cycle (TCA) and the pentose phosphate pathway (PPP).…”

Section: Cell Growth Necrosis and Mitosismentioning

confidence: 99%

“…These model parameters were categorized as internal kinetic parameters, concentration threshold parameters and external parameters that depend on the tumor microenvironment. An initial range for each parameter was defined (Table 1) based on literature values (21,32), and the parameter space was sampled using Latin hypercube sampling (LHS). Multiple initial runs were conducted to identify the correct set of parameters using LHS within the parameter space defined by the range given in Table 1.…”

Section: Cell Mitosis (Cell Division) Occurs When a Cell's Volume Incmentioning

confidence: 99%

“…We further predict the effects of drugs inhibiting specific enzymatic reactions in inhibiting the growth of the entire tumor. The novelty of our model of avascular tumor growth described here lies in the incorporation of a detailed mechanistic metabolic network (21) within each cell unit. We couple the cells' behaviors to the diffusion of multiple nutrients, where the cellular growth and death rates are a function of ATP generation rate of a cell unit.…”

Section: Introductionmentioning

confidence: 99%

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Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Roy

Finley

2018

Preprint

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Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells' proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation and necrosis. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.

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Section: Methodsmentioning

confidence: 99%

Section: Cell Mitosis (Cell Division) Occurs When a Cell's Volume Incmentioning

confidence: 97%

Section: Cell Growth Necrosis and Mitosismentioning

confidence: 99%

Section: Cell Mitosis (Cell Division) Occurs When a Cell's Volume Incmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Roy

Finley

2018

Preprint

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A review of computational modeling, machine learning and image analysis in cancer metastasis dynamics

Hirway

Weinberg

2023

Comp Sys Onco

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Cancer is a life-threatening process that stems from genetic mutations in cells, which leads to the formation of tumors, and is a major cause of deaths in the United States, with secondary metastasis being a major driver of fatality. The development of an optimal metastatic environment is an essential process prior to tumor metastasis. This process, called pre-metastatic niche formation, involves the activation of resident fibroblast-like cells and macrophages. Tumormediated factors introduced to this environment transform resident cells that secrete additional growth factors and remodel the extracellular matrix, which is thought to promote tumor colonization and metastasis in the secondary environment. Furthermore, an important component of metastasis is the biological process of epithelial-mesenchymal transition, which can be exploited by cancer cells to change their phenotype, to migrate and proliferate as necessary. In this review, we discuss recent advances in the investigation of cancer growth and migration. Computational models that focus on biochemical signaling and multicellular dynamics are examined. Machine learning models and image analysis that classify cancer-related data are also explored. Through this review, we highlight advances in the study of important aspects of cancer and metastasis signaling and computational tools to study these dynamics.

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Gain and loss of function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells

2021

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This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Function mutations found in cancer cells. More specifically, from a theoretical perspective, our approach focuses on the determination of moiety conservation laws for the system and their relation with the corresponding stoichiometric surfaces. Then we show that Loss of Function mutations can be implemented in the model via modification of the initial conditions in the system, while Gain of Function mutations can be implemented by eliminating specific reactions. Finally, the model is utilized to examine in detail the G1-S phase of a colorectal cancer cell.

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Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

Cited by 55 publications

References 119 publications

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

A review of computational modeling, machine learning and image analysis in cancer metastasis dynamics

Gain and loss of function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells

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