A persistent invasive phenotype in post-hypoxic tumor cells is revealed by novel fate-mapping and computational modeling

Rocha, Heber L.; Godet, Inês; Kurtoglu, Furkan; Metzcar, John; Konstantinopoulos, Kali; Bhoyar, Soumitra; Gilkes, Daniele M.; Macklin, Paul

doi:10.1101/2020.12.30.424757

Cited by 3 publications

(4 citation statements)

References 51 publications

(63 reference statements)

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“…This combination of models has also been suggested in the context of data-driven estimation of pharmacokinetic parameters for drugs ( 58 ). Moreover, data-driven models enable parameter inference by studying parameter dependencies of simulation results through approximate Bayesian computation ( 59 , 60 ) or genetic algorithms ( 61 ).…”

Section: Facets Of Mechanistic Learningmentioning

confidence: 99%

A review of mechanistic learning in mathematical oncology

Metzcar,

Jutzeler,

Macklin

et al. 2024

Front. Immunol.

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Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework

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Section: Facets Of Mechanistic Learningmentioning

confidence: 99%

A review of mechanistic learning in mathematical oncology

Metzcar,

Jutzeler,

Macklin

et al. 2024

Front. Immunol.

Self Cite

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“…Depending on the availability of oxygen and nutrients, there are two critical regions in a tumor spheroid, namely necrotic and quiescent regions [21]. The huge lack of oxygen and nutrients in the necrotic region leads to cell death [22].…”

Section: Introductionmentioning

confidence: 99%

“…Detailed values and descriptions of all the parameters used in the governing equations used in this study. The exact values have been extracted from Refs [21,33,40]. …”

mentioning

confidence: 99%

An Acoustic Spheroid-on-Chip Platform for Long-Term Culturing of 3D Cell Aggregates

Shourabi¹,

Salajeghe²,

Barisam³

et al. 2021

Preprint

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Microfluidic lab-on-chip devices are widely being developed for chemical and biological studies. One of the most commonly used types of these chips is perfusion microwells for culturing multicellular spheroids. The main challenge in such systems is the formation of substantial necrotic and hypoxic zones within the cultured spheroids. Herein, we propose a novel acoustofluidic integrated platform to tackle this bottleneck problem. We show that such an approach enhances cell viability and shrinks necrotic and hypoxic zones in these spheroid-on-a-chip platforms without the need to increase the flow rate, leading to a significant reduction in costly reagents' consumption. Proof-of-concept, designing procedures, and finite element numerical simulation are discussed in details. Also, the effects of acoustic and hydrodynamic parameters on the cultured cells are investigated. The results show that by increasing acoustic boundary displacement amplitude (d0), the spheroid’s proliferating zone enlarges greatly. Moreover, it is shown that by implementing d0=0.5 nm, the required flow rate to maintain the necrotic zone below 13% will be decreased 12 times compared to non-acoustic chips.

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“…Depending on the availability of oxygen and nutrients, there are two critical regions in a tumor spheroid, namely necrotic and quiescent regions [ 23 , 24 ]. Massive lack of oxygen and nutrients in a necrotic region leads to cell death [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Proof-of-Concept Study Using Numerical Simulations of an Acoustic Spheroid-on-a-Chip Platform for Improving 3D Cell Culture

Shourabi

Salajeghe

Barisam

et al. 2021

Sensors

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Microfluidic lab-on-chip devices are widely being developed for chemical and biological studies. One of the most commonly used types of these chips is perfusion microwells for culturing multicellular spheroids. The main challenge in such systems is the formation of substantial necrotic and quiescent zones within the cultured spheroids. Herein, we propose a novel acoustofluidic integrated platform to tackle this bottleneck problem. It will be shown numerically that such an approach is a potential candidate to be implemented to enhance cell viability and shrinks necrotic and quiescent zones without the need to increase the flow rate, leading to a significant reduction in costly reagents’ consumption in conventional spheroid-on-a-chip platforms. Proof-of-concept, designing procedures and numerical simulation are discussed in detail. Additionally, the effects of acoustic and hydrodynamic parameters on the cultured cells are investigated. The results show that by increasing acoustic boundary displacement amplitude (d0), the spheroid’s proliferating zone enlarges greatly. Moreover, it is shown that by implementing d0= 0.5 nm, the required flow rate to maintain the necrotic zone below 13% will be decreased 12 times compared to non-acoustic chips.

show abstract

A persistent invasive phenotype in post-hypoxic tumor cells is revealed by novel fate-mapping and computational modeling

Cited by 3 publications

References 51 publications

A review of mechanistic learning in mathematical oncology

A review of mechanistic learning in mathematical oncology

An Acoustic Spheroid-on-Chip Platform for Long-Term Culturing of 3D Cell Aggregates

A Proof-of-Concept Study Using Numerical Simulations of an Acoustic Spheroid-on-a-Chip Platform for Improving 3D Cell Culture

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