Fine-grained simulations of the microenvironment of vascularized tumours

Fredrich, Thierry; Rieger, Heiko; Chignola, Roberto; Milotti, E.

doi:10.1038/s41598-019-48252-8

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…The underlying molecular mechanisms are still not well understood, but the role of G-protein coupled receptors in proton sensing is increasingly investigated also in relation to pathological conditions, besides cancer, that result in an increased extracellular acidity, such as infarction and inflammation 42 . We conclude that our model can be used as an essential building block of more comprehensive in silico research on solid tumors 43 , but it may also help understanding how other cells can sense and dynamically adapt to pH changes.…”

Section: Resultsmentioning

confidence: 99%

“…[6][7][8] . This has already allowed us to characterize new biophysical properties of tumors and of their microenvironment [34][35][36][37] , but the program still contains an excindingly simplified description of how cells control their intracellular pH. The program has an incremental structure, and we add new parts as soon as they are independently validated.…”

Section: Discussionmentioning

confidence: 99%

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The control of acidity in tumor cells: a biophysical model

Piasentin

Milotti

Chignola

2020

Sci Rep

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Acidosis of the tumor microenvironment leads to cancer invasion, progression and resistance to therapies. We present a biophysical model that describes how tumor cells regulate intracellular and extracellular acidity while they grow in a microenvironment characterized by increasing acidity and hypoxia. the model takes into account the dynamic interplay between glucose and O 2 consumption with lactate and CO 2 production and connects these processes to H + and HCO − 3 fluxes inside and outside cells. We have validated the model with independent experimental data and used it to investigate how and to which extent tumor cells can survive in adverse micro-environments characterized by acidity and hypoxia. the simulations show a dominance of the H + exchanges in well-oxygenated regions, and of HCO − 3 exchanges in the inner hypoxic regions where tumor cells are known to acquire malignant phenotypes. the model also includes the activity of the enzyme carbonic Anhydrase 9 (CA9), a known marker of tumor aggressiveness, and the simulations demonstrate that CA9 acts as a nonlinear pH i equalizer at any O 2 level in cells that grow in acidic extracellular environments.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The control of acidity in tumor cells: a biophysical model

Piasentin

Milotti

Chignola

2020

Sci Rep

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“…[262]. Since tissue growth in a living organism requires nutrition supply and oxygen, a blood vessel network should be included: Hybrid models describe the tumor mass with a continuum model and the dynamically changing vascularization with a discrete pipe network [263,264]. Simulations show that the incorporation of a blood vessel network leads to a characteristic compartmentalization of the tumor into several regions differing in vessel density and diameter, and in necrosis.…”

Section: Tissuesmentioning

confidence: 99%

Computational models for active matter

et al. 2020

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A variety of computational models have been developed to describe active matter at different length and time scales. The diversity of the methods and the challenges in modeling active matterranging from molecular motors and cytoskeletal filaments over artificial and biological swimmers on microscopic to groups of animals on macroscopic scales-mainly originate from their out-ofequilibrium character, multiscale nature, nonlinearity, and multibody interactions. In the present review, various modeling approaches and numerical techniques are addressed, compared, and differentiated to illuminate the innovations and current challenges in understanding active matter. The complexity increases from minimal microscopic models of dry active matter toward microscopic models of active matter in fluids. Complementary, coarse-grained descriptions and continuum models are elucidated. Microscopic details are often relevant and strongly affect collective behaviors, which implies that the selection of a proper level of modeling is a delicate choice, with simple models emphasizing universal properties and detailed models capturing specific features. Finally, current approaches to further advance the existing models and techniques to cope with real-world applications, such as complex media and biological environments, are discussed.2

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“…Physical and structural characteristics (e.g. pressure, shear, radius) are often encoded into network elements like edges and nodes (Fredrich et al, 2018(Fredrich et al, , 2019. Networks are flexible data structures that can be modified and interrogated, enabling researchers to study interplay among agents and between agents and environments.…”

Section: The Tumor Microenvironment As a Model Systemmentioning

confidence: 99%

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Cain,

Evarts,

et al. 2024

Preprint

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Motivation: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies—e.g. live cell imaging, scRNAseq, agent-based models—requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Results: Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. Availability: All source codes for the agent-based model, emulation, and analyses are publicly available at github.com/bagherilab/ARCADE, github.com/bagherilab/emulation, and github.com/bagherilab/ emulation_analysis, respectively.

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Fine-grained simulations of the microenvironment of vascularized tumours

Cited by 9 publications

References 39 publications

The control of acidity in tumor cells: a biophysical model

The control of acidity in tumor cells: a biophysical model

Computational models for active matter

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

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