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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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2
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Falcó C, Cohen DJ, Carrillo JA, Baker RE. Quantifying cell cycle regulation by tissue crowding. Biophys J 2024:S0006-3495(24)00317-5. [PMID: 38715360 DOI: 10.1016/j.bpj.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/24/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024] Open
Abstract
The spatiotemporal coordination and regulation of cell proliferation is fundamental in many aspects of development and tissue maintenance. Cells have the ability to adapt their division rates in response to mechanical constraints, yet we do not fully understand how cell proliferation regulation impacts cell migration phenomena. Here, we present a minimal continuum model of cell migration with cell cycle dynamics, which includes density-dependent effects and hence can account for cell proliferation regulation. By combining minimal mathematical modeling, Bayesian inference, and recent experimental data, we quantify the impact of tissue crowding across different cell cycle stages in epithelial tissue expansion experiments. Our model suggests that cells sense local density and adapt cell cycle progression in response, during G1 and the combined S/G2/M phases, providing an explicit relationship between each cell-cycle-stage duration and local tissue density, which is consistent with several experimental observations. Finally, we compare our mathematical model's predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns. Our work presents a systematic approach for investigating and analyzing cell cycle data, providing mechanistic insights into how individual cells regulate proliferation, based on population-based experimental measurements.
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Affiliation(s)
- Carles Falcó
- Mathematical Institute, University of Oxford, Oxford, United Kingdom.
| | - Daniel J Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey
| | - José A Carrillo
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Ruth E Baker
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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3
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Almeida L, Denis JA, Ferrand N, Lorenzi T, Prunet A, Sabbah M, Villa C. Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally informed mathematical modelling. J R Soc Interface 2024; 21:20230587. [PMID: 38196375 PMCID: PMC10777142 DOI: 10.1098/rsif.2023.0587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
Glucose is a primary energy source for cancer cells. Several lines of evidence support the idea that monocarboxylate transporters, such as MCT1, elicit metabolic reprogramming of cancer cells in glucose-poor environments, allowing them to re-use lactate, a by-product of glucose metabolism, as an alternative energy source with serious consequences for disease progression. We employ a synergistic experimental and mathematical modelling approach to explore the evolutionary processes at the root of cancer cell adaptation to glucose deprivation, with particular focus on the mechanisms underlying the increase in MCT1 expression observed in glucose-deprived aggressive cancer cells. Data from in vitro experiments on breast cancer cells are used to inform and calibrate a mathematical model that comprises a partial integro-differential equation for the dynamics of a population of cancer cells structured by the level of MCT1 expression. Analytical and numerical results of this model suggest that environment-induced changes in MCT1 expression mediated by lactate-associated signalling pathways enable a prompt adaptive response of glucose-deprived cancer cells, while fluctuations in MCT1 expression due to epigenetic changes create the substrate for environmental selection to act upon, speeding up the selective sweep underlying cancer cell adaptation to glucose deprivation, and may constitute a long-term bet-hedging mechanism.
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Affiliation(s)
- Luis Almeida
- Sorbonne Université, CNRS, Université de Paris, Inria, Laboratoire Jacques-Louis Lions UMR 7598, Paris 75005, France
| | - Jérôme Alexandre Denis
- Sorbonne Université, Cancer Biology and Therapeutics, INSERM, CNRS, Institut Universitaire de Cancérologie, Saint-Antoine Research Center (CRSA), Paris 75012, France
- Department of Endocrinology and Oncology Biochemistry, Pitié-Salpetrière Hospital, Paris 75013, France
| | - Nathalie Ferrand
- Sorbonne Université, Cancer Biology and Therapeutics, INSERM, CNRS, Institut Universitaire de Cancérologie, Saint-Antoine Research Center (CRSA), Paris 75012, France
| | - Tommaso Lorenzi
- Department of Mathematical Sciences ‘G. L. Lagrange’, Dipartimento di Eccellenza 2018-2022, Politecnico di Torino, Torino 10129, Italy
| | - Antonin Prunet
- Sorbonne Université, CNRS, Université de Paris, Inria, Laboratoire Jacques-Louis Lions UMR 7598, Paris 75005, France
- Sorbonne Université, Cancer Biology and Therapeutics, INSERM, CNRS, Institut Universitaire de Cancérologie, Saint-Antoine Research Center (CRSA), Paris 75012, France
| | - Michéle Sabbah
- Sorbonne Université, CNRS, Université de Paris, Inria, Laboratoire Jacques-Louis Lions UMR 7598, Paris 75005, France
| | - Chiara Villa
- Sorbonne Université, CNRS, Université de Paris, Inria, Laboratoire Jacques-Louis Lions UMR 7598, Paris 75005, France
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Murphy RJ, Gunasingh G, Haass NK, Simpson MJ. Growth and adaptation mechanisms of tumour spheroids with time-dependent oxygen availability. PLoS Comput Biol 2023; 19:e1010833. [PMID: 36634128 PMCID: PMC9876349 DOI: 10.1371/journal.pcbi.1010833] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/25/2023] [Accepted: 12/21/2022] [Indexed: 01/13/2023] Open
Abstract
Tumours are subject to external environmental variability. However, in vitro tumour spheroid experiments, used to understand cancer progression and develop cancer therapies, have been routinely performed for the past fifty years in constant external environments. Furthermore, spheroids are typically grown in ambient atmospheric oxygen (normoxia), whereas most in vivo tumours exist in hypoxic environments. Therefore, there are clear discrepancies between in vitro and in vivo conditions. We explore these discrepancies by combining tools from experimental biology, mathematical modelling, and statistical uncertainty quantification. Focusing on oxygen variability to develop our framework, we reveal key biological mechanisms governing tumour spheroid growth. Growing spheroids in time-dependent conditions, we identify and quantify novel biological adaptation mechanisms, including unexpected necrotic core removal, and transient reversal of the tumour spheroid growth phases.
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Affiliation(s)
- Ryan J. Murphy
- Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
| | - Gency Gunasingh
- Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Nikolas K. Haass
- Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Matthew J. Simpson
- Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
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Byrne H. In vitro
and
in silico
approaches to engineering three-dimensional biological tissues and organoids. Interface Focus 2022. [PMCID: PMC9372642 DOI: 10.1098/rsfs.2022.0046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
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