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Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biol Ther 2024; 25:2344600. [PMID: 38678381 PMCID: PMC11057625 DOI: 10.1080/15384047.2024.2344600] [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: 10/30/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.
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Affiliation(s)
- Nicolò Cogno
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Condensed Matter Physics, Technische Universit¨at Darmstadt, Darmstadt, Germany
| | - Cristian Axenie
- Computer Science Department and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany
| | - Roman Bauer
- Nature Inspired Computing and Engineering Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
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2
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Aguadé-Gorgorió G, Anderson ARA, Solé R. Modeling tumors as complex ecosystems. iScience 2024; 27:110699. [PMID: 39280631 PMCID: PMC11402243 DOI: 10.1016/j.isci.2024.110699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/18/2024] Open
Abstract
Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.
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Affiliation(s)
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ricard Solé
- ICREA-Complex Systems Lab, UPF-PRBB, Dr. Aiguader 80, 08003 Barcelona, Spain
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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3
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Soares Dos Santos MP, Bernardo RMC, Vidal J, Moreira A, Torres DFM, Herdeiro CAR, Santos HA, Gonçalves G. Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management. Comput Biol Med 2024; 180:108961. [PMID: 39106673 DOI: 10.1016/j.compbiomed.2024.108961] [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: 03/15/2024] [Revised: 07/10/2024] [Accepted: 07/26/2024] [Indexed: 08/09/2024]
Abstract
PROBLEM Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice. OBJECTIVE This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm. DESIGN Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models). RESULTS Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics. CONCLUSIONS Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.
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Affiliation(s)
- Marco P Soares Dos Santos
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal.
| | - Rodrigo M C Bernardo
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal
| | - JoãoV Vidal
- Department of Physics and Aveiro Institute of Materials (CICECO), University of Aveiro, Aveiro, Portugal; Department of Physics and Institute for Nanostructures, Nanomodelling and Nanofabrication (I3N), University of Aveiro, Aveiro, Portugal
| | - Ana Moreira
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, 3810-193, Aveiro, Portugal
| | - Delfim F M Torres
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Carlos A R Herdeiro
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Hélder A Santos
- Department of Biomaterials and Biomedical Technology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Gil Gonçalves
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal
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4
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Wang Y, Bucher E, Rocha H, Jadhao V, Metzcar J, Heiland R, Frieboes HB, Macklin P. Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588498. [PMID: 38645004 PMCID: PMC11030335 DOI: 10.1101/2024.04.09.588498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Interactions between biological systems and engineered nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.
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5
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Singh D, Paquin D. Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6659-6693. [PMID: 39176414 DOI: 10.3934/mbe.2024292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.
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Affiliation(s)
- Dashmi Singh
- Stanford University Online High School, 415 Broadway Academy Hall, Floor 2, 8853,415 Broadway, Redwood City, CA 94063, USA
| | - Dana Paquin
- Stanford University Online High School, 415 Broadway Academy Hall, Floor 2, 8853,415 Broadway, Redwood City, CA 94063, USA
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6
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Lorenzo G, Ahmed SR, Hormuth DA, Vaughn B, Kalpathy-Cramer J, Solorio L, Yankeelov TE, Gomez H. Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data. Annu Rev Biomed Eng 2024; 26:529-560. [PMID: 38594947 DOI: 10.1146/annurev-bioeng-081623-025834] [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] [Indexed: 04/11/2024]
Abstract
Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
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Affiliation(s)
- Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Syed Rakin Ahmed
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David A Hormuth
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Brenna Vaughn
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Luis Solorio
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Thomas E Yankeelov
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA
- Department of Biomedical Engineering, Department of Oncology, and Department of Diagnostic Medicine, University of Texas, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Hector Gomez
- School of Mechanical Engineering and Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
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7
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Blom E, Engblom S. Morphological Stability for in silico Models of Avascular Tumors. Bull Math Biol 2024; 86:75. [PMID: 38758501 DOI: 10.1007/s11538-024-01297-x] [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: 09/14/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024]
Abstract
The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principle in silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework's flexibility, such additions can be readily included whenever the relevant data become available.
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Affiliation(s)
- Erik Blom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden
| | - Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden.
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8
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Aguadé-Gorgorió G, Anderson AR, Solé R. Modeling tumors as species-rich ecological communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590504. [PMID: 38712062 PMCID: PMC11071393 DOI: 10.1101/2024.04.22.590504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.
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Affiliation(s)
| | - Alexander R.A. Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA
| | - Ricard Solé
- ICREA-Complex Systems Lab, UPF-PRBB, Dr. Aiguader 80, 08003 Barcelona, Spain
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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9
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Zheng H, Harcum SW, Pei J, Xie W. Stochastic biological system-of-systems modelling for iPSC culture. Commun Biol 2024; 7:39. [PMID: 38191636 PMCID: PMC10774284 DOI: 10.1038/s42003-023-05653-w] [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: 07/29/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024] Open
Abstract
Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.
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Affiliation(s)
- Hua Zheng
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA
| | | | - Jinxiang Pei
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Wei Xie
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA.
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10
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Kamioka J, Sasaki K, Baba K, Tanaka T, Teranishi Y, Ogasawara T, Inoie M, Hata KI, Nishida K, Kino-Oka M. Agent-based approach for elucidating the release from collective arrest of cell motion in corneal epithelial cell sheet. J Biosci Bioeng 2023; 136:477-486. [PMID: 37923618 DOI: 10.1016/j.jbiosc.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 11/07/2023]
Abstract
Changes in cell fluidity have been observed in various cellular tissues and are strongly linked to biological phenomena such as self-organization. Recent studies suggested variety of mechanisms and factors, which are still being investigated. This study aimed to investigate changes in cell fluidity in multi-layered cell sheets, by exploring the collective arrest of cell motion and its release in cultures of corneal epithelial cells. We constructed mathematical models to simulate the behaviors of individual cells, including cell differentiation and time-dependent changes in cell-cell connections, which are defined by stochastic or kinetic rules. Changes in cell fluidity and cell sheet structures were expressed by simulating autonomous cell behaviors and interactions in tissues using an agent-based model. A single-cell level spatiotemporal analysis of cell state transition between migratable and non-migratable states revealed that the release from collective arrest of cell motion was initially triggered by a decreased ability to form cell-cell connections in the suprabasal layers, and was propagated by chain migration. Notably, the disruption of cell-cell connections and stratification occurred in the region of migratable state cells. Hence, a modeling approach that considers time-dependent changes in cell properties and behavior, and spatiotemporal analysis at the single-cell level can effectively delineate emergent phenomena arising from the complex interplay of cells.
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Affiliation(s)
- Junya Kamioka
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Kei Sasaki
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan; Global Center for Medical Engineering and Informatics, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Koichi Baba
- Department of Ophthalmology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Visual Regenerative Medicine, Division of Health Sciences, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Tomoyo Tanaka
- Japan Tissue Engineering Co., Ltd., 6-209-1 Miyakitadori, Gamagori, Aichi 443-0022, Japan
| | - Yosuke Teranishi
- Japan Tissue Engineering Co., Ltd., 6-209-1 Miyakitadori, Gamagori, Aichi 443-0022, Japan
| | - Takahiro Ogasawara
- Japan Tissue Engineering Co., Ltd., 6-209-1 Miyakitadori, Gamagori, Aichi 443-0022, Japan
| | - Masukazu Inoie
- Japan Tissue Engineering Co., Ltd., 6-209-1 Miyakitadori, Gamagori, Aichi 443-0022, Japan
| | - Ken-Ichiro Hata
- Japan Tissue Engineering Co., Ltd., 6-209-1 Miyakitadori, Gamagori, Aichi 443-0022, Japan
| | - Kohji Nishida
- Department of Ophthalmology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Masahiro Kino-Oka
- Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan; Research Base for Cell Manufacturability, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
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11
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Wirthl B, Janko C, Lyer S, Schrefler BA, Alexiou C, Wall WA. An in silico model of the capturing of magnetic nanoparticles in tumour spheroids in the presence of flow. Biomed Microdevices 2023; 26:1. [PMID: 38008813 PMCID: PMC10678808 DOI: 10.1007/s10544-023-00685-9] [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] [Accepted: 11/21/2023] [Indexed: 11/28/2023]
Abstract
One of the main challenges in improving the efficacy of conventional chemotherapeutic drugs is that they do not reach the cancer cells at sufficiently high doses while at the same time affecting healthy tissue and causing significant side effects and suffering in cancer patients. To overcome this deficiency, magnetic nanoparticles as transporter systems have emerged as a promising approach to achieve more specific tumour targeting. Drug-loaded magnetic nanoparticles can be directed to the target tissue by applying an external magnetic field. However, the magnetic forces exerted on the nanoparticles fall off rapidly with distance, making the tumour targeting challenging, even more so in the presence of flowing blood or interstitial fluid. We therefore present a computational model of the capturing of magnetic nanoparticles in a test setup: our model includes the flow around the tumour, the magnetic forces that guide the nanoparticles, and the transport within the tumour. We show how a model for the transport of magnetic nanoparticles in an external magnetic field can be integrated with a multiphase tumour model based on the theory of porous media. Our approach based on the underlying physical mechanisms can provide crucial insights into mechanisms that cannot be studied conclusively in experimental research alone. Such a computational model enables an efficient and systematic exploration of the nanoparticle design space, first in a controlled test setup and then in more complex in vivo scenarios. As an effective tool for minimising costly trial-and-error design methods, it expedites translation into clinical practice to improve therapeutic outcomes and limit adverse effects for cancer patients.
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Affiliation(s)
- Barbara Wirthl
- Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Garching bei München, Germany.
| | - Christina Janko
- Department of Otorhinolaryngology, Head and Neck Surgery, Section of Experimental Oncology and Nanomedicine (SEON), Else Kröner-Fresenius-Stiftung Professorship, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Stefan Lyer
- Department of Otorhinolaryngology, Head and Neck Surgery, Section of Experimental Oncology and Nanomedicine (SEON), Else Kröner-Fresenius-Stiftung Professorship, Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, Section of Experimental Oncology and Nanomedicine (SEON), Professorship for AI-Guided Nanomaterials within the framework of the Hightech Agenda (HTA) of the Free State of Bavaria, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Bernhard A Schrefler
- Department of Civil, Environmental and Architectural Engineering, University of Padua, Padua, Italy
- Institute for Advanced Study, Technical University of Munich, Garching bei München, Germany
| | - Christoph Alexiou
- Department of Otorhinolaryngology, Head and Neck Surgery, Section of Experimental Oncology and Nanomedicine (SEON), Else Kröner-Fresenius-Stiftung Professorship, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Wolfgang A Wall
- Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Garching bei München, Germany
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12
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Nanda P, Budak M, Michael CT, Krupinsky K, Kirschner DE. Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566861. [PMID: 38014103 PMCID: PMC10680629 DOI: 10.1101/2023.11.13.566861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
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13
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Laurent H, Hughes MDG, Walko M, Brockwell DJ, Mahmoudi N, Youngs TGA, Headen TF, Dougan L. Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering. Biomacromolecules 2023; 24:4869-4879. [PMID: 37874935 PMCID: PMC10646990 DOI: 10.1021/acs.biomac.3c00583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/03/2023] [Indexed: 10/26/2023]
Abstract
Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.
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Affiliation(s)
- Harrison Laurent
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
| | - Matt D. G. Hughes
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
| | - Martin Walko
- School
of Chemistry, University of Leeds, Leeds, United
Kingdom, LS2 9JT
| | - David J. Brockwell
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
| | - Najet Mahmoudi
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Tristan G. A. Youngs
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Thomas F. Headen
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Lorna Dougan
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
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14
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Eftimie R, Rolin G, Adebayo OE, Urcun S, Chouly F, Bordas SPA. Modelling Keloids Dynamics: A Brief Review and New Mathematical Perspectives. Bull Math Biol 2023; 85:117. [PMID: 37855947 DOI: 10.1007/s11538-023-01222-8] [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: 04/19/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023]
Abstract
Keloids are fibroproliferative disorders described by excessive growth of fibrotic tissue, which also invades adjacent areas (beyond the original wound borders). Since these disorders are specific to humans (no other animal species naturally develop keloid-like tissue), experimental in vivo/in vitro research has not led to significant advances in this field. One possible approach could be to combine in vitro human models with calibrated in silico mathematical approaches (i.e., models and simulations) to generate new testable biological hypotheses related to biological mechanisms and improved treatments. Because these combined approaches do not really exist for keloid disorders, in this brief review we start by summarising the biology of these disorders, then present various types of mathematical and computational approaches used for related disorders (i.e., wound healing and solid tumours), followed by a discussion of the very few mathematical and computational models published so far to study various inflammatory and mechanical aspects of keloids. We conclude this review by discussing some open problems and mathematical opportunities offered in the context of keloid disorders by such combined in vitro/in silico approaches, and the need for multi-disciplinary research to enable clinical progress.
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Affiliation(s)
- R Eftimie
- Laboratoire de Mathématiques de Besançon, Université de Franche-Comté, 25000, Besançon, France.
| | - G Rolin
- INSERM CIC-1431, CHU Besançon, F-25000, Besançon, France
- EFS, INSERM, UMR 1098 RIGHT, Université de Franche-Comté, F-25000, Besançon, France
| | - O E Adebayo
- Laboratoire de Mathématiques de Besançon, Université de Franche-Comté, 25000, Besançon, France
| | - S Urcun
- Institute for Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - F Chouly
- Institut de Mathématiques de Bourgogne, Université de Franche-Comté, 21078, Dijon, France
- Center for Mathematical Modelling and Department of Mathematical Engineering, University of Chile and IRL 2807 - CNRS, Santiago, Chile
- Departamento de Ingeniería Matemática, CI2MA, Universidad de Concepción, Casilla 160-C, Concepción, Chile
| | - S P A Bordas
- Institute for Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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15
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Ruscone M, Montagud A, Chavrier P, Destaing O, Bonnet I, Zinovyev A, Barillot E, Noël V, Calzone L. Multiscale model of the different modes of cancer cell invasion. Bioinformatics 2023; 39:btad374. [PMID: 37289551 PMCID: PMC10293590 DOI: 10.1093/bioinformatics/btad374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 06/10/2023] Open
Abstract
MOTIVATION Mathematical models of biological processes altered in cancer are built using the knowledge of complex networks of signaling pathways, detailing the molecular regulations inside different cell types, such as tumor cells, immune and other stromal cells. If these models mainly focus on intracellular information, they often omit a description of the spatial organization among cells and their interactions, and with the tumoral microenvironment. RESULTS We present here a model of tumor cell invasion simulated with PhysiBoSS, a multiscale framework, which combines agent-based modeling and continuous time Markov processes applied on Boolean network models. With this model, we aim to study the different modes of cell migration and to predict means to block it by considering not only spatial information obtained from the agent-based simulation but also intracellular regulation obtained from the Boolean model. Our multiscale model integrates the impact of gene mutations with the perturbation of the environmental conditions and allows the visualization of the results with 2D and 3D representations. The model successfully reproduces single and collective migration processes and is validated on published experiments on cell invasion. In silico experiments are suggested to search for possible targets that can block the more invasive tumoral phenotypes. AVAILABILITY AND IMPLEMENTATION https://github.com/sysbio-curie/Invasion_model_PhysiBoSS.
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Affiliation(s)
- Marco Ruscone
- Institut Curie, Université PSL, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- Mines ParisTech, Université PSL, F-75005 Paris, France
- Sorbonne Université, Collège Doctoral, F-75005 Paris, France
| | | | - Philippe Chavrier
- Institut Curie, PSL Research University, CNRS, UMR 144, Paris, France
| | - Olivier Destaing
- Institute for Advanced Biosciences, Centre de Recherche Université Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, France
| | - Isabelle Bonnet
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Andrei Zinovyev
- Institut Curie, Université PSL, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- Mines ParisTech, Université PSL, F-75005 Paris, France
| | - Emmanuel Barillot
- Institut Curie, Université PSL, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- Mines ParisTech, Université PSL, F-75005 Paris, France
| | - Vincent Noël
- Institut Curie, Université PSL, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- Mines ParisTech, Université PSL, F-75005 Paris, France
| | - Laurence Calzone
- Institut Curie, Université PSL, F-75005 Paris, France
- INSERM, U900, F-75005 Paris, France
- Mines ParisTech, Université PSL, F-75005 Paris, France
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16
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Barker AD, Alba MM, Mallick P, Agus DB, Lee JSH. An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next. Mol Cell Proteomics 2023:100569. [PMID: 37196763 PMCID: PMC10388583 DOI: 10.1016/j.mcpro.2023.100569] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/26/2023] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.
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Affiliation(s)
- Anna D Barker
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Complex Adaptive Systems Initiative and School of Life Sciences, Arizona State University, Tempe, Arizona
| | - Mario M Alba
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA
| | - Parag Mallick
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA; Department of Radiology, Stanford University, Stanford, CA
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Keck School of Medicine, University of Southern California, Los Angeles, CA; Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Jerry S H Lee
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Keck School of Medicine, University of Southern California, Los Angeles, CA; Viterbi School of Engineering, University of Southern California, Los Angeles, CA
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17
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Jørgensen ACS, Hill CS, Sturrock M, Tang W, Karamched SR, Gorup D, Lythgoe MF, Parrinello S, Marguerat S, Shahrezaei V. Data-driven spatio-temporal modelling of glioblastoma. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221444. [PMID: 36968241 PMCID: PMC10031411 DOI: 10.1098/rsos.221444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
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Affiliation(s)
| | - Ciaran Scott Hill
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin D02 YN77, Ireland
| | - Wenhao Tang
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| | - Saketh R. Karamched
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Dunja Gorup
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Mark F. Lythgoe
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Simona Parrinello
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Samuel Marguerat
- Genomics Translational Technology Platform, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
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18
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Pinho STR. Some features on methodology of dengue modelling linked to data: Comment on "Mathematical modelling for dengue fever epidemiology: a 10-year systematic review" by M. Aguiar et al. Phys Life Rev 2023; 44:276-278. [PMID: 36821892 PMCID: PMC9916129 DOI: 10.1016/j.plrev.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023]
Affiliation(s)
- Suani T R Pinho
- Instituto de Física, Universidade Federal da Bahia, 40170-115, Salvador, Brazil; Instituto Nacional de Ciência e Tecnologia - Sistemas Complexos, Brazil.
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19
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Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z. Mathematical modeling of cancer immunotherapy for personalized clinical translation. NATURE COMPUTATIONAL SCIENCE 2022; 2:785-796. [PMID: 38126024 PMCID: PMC10732566 DOI: 10.1038/s43588-022-00377-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2023]
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John Lowengrub
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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20
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Millar-Wilson A, Ward Ó, Duffy E, Hardiman G. Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies. iScience 2022; 25:105421. [DOI: 10.1016/j.isci.2022.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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21
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Howard FM, He G, Peterson JR, Pfeiffer JR, Earnest T, Pearson AT, Abe H, Cole JA, Nanda R. Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform. Breast Cancer Res Treat 2022; 196:57-66. [PMID: 36063220 PMCID: PMC9550684 DOI: 10.1007/s10549-022-06722-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/19/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need. METHODS We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes. RESULTS A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 - 95.5) and 89.4% (95% CI 81.3 - 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 - 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence. CONCLUSION A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.
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Affiliation(s)
- Frederick M Howard
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue | MC 2115, Chicago, IL, 60637, USA.
| | - Gong He
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue | MC 2115, Chicago, IL, 60637, USA
| | | | | | | | - Alexander T Pearson
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue | MC 2115, Chicago, IL, 60637, USA
| | - Hiroyuki Abe
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | | | - Rita Nanda
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue | MC 2115, Chicago, IL, 60637, USA
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22
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Heidary Z, Haghjooy Javanmard S, Izadi I, Zare N, Ghaisari J. Multiscale modeling of collective cell migration elucidates the mechanism underlying tumor-stromal interactions in different spatiotemporal scales. Sci Rep 2022; 12:16242. [PMID: 36171274 PMCID: PMC9519582 DOI: 10.1038/s41598-022-20634-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/15/2022] [Indexed: 11/09/2022] Open
Abstract
Metastasis is the pathogenic spread of cancer cells from a primary tumor to a secondary site which happens at the late stages of cancer. It is caused by a variety of biological, chemical, and physical processes, such as molecular interactions, intercellular communications, and tissue-level activities. Complex interactions of cancer cells with their microenvironment components such as cancer associated fibroblasts (CAFs) and extracellular matrix (ECM) cause them to adopt an invasive phenotype that promotes tumor growth and migration. This paper presents a multiscale model for integrating a wide range of time and space interactions at the molecular, cellular, and tissue levels in a three-dimensional domain. The modeling procedure starts with presenting nonlinear dynamics of cancer cells and CAFs using ordinary differential equations based on TGFβ, CXCL12, and LIF signaling pathways. Unknown kinetic parameters in these models are estimated using hybrid unscented Kalman filter and the models are validated using experimental data. Then, the principal role of CAFs on metastasis is revealed by spatial-temporal modeling of circulating signals throughout the TME. At this stage, the model has evolved into a coupled ODE-PDE system that is capable of determining cancer cells' status in one of the quiescent, proliferating or migratory conditions due to certain metastasis factors and ECM characteristics. At the tissue level, we consider a force-based framework to model the cancer cell proliferation and migration as the final step towards cancer cell metastasis. The ability of the multiscale model to depict cancer cells' behavior in different levels of modeling is confirmed by comparing its outputs with the results of RT PCR and wound scratch assay techniques. Performance evaluation of the model indicates that the proposed multiscale model can pave the way for improving the efficiency of therapeutic methods in metastasis prevention.
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Affiliation(s)
- Zarifeh Heidary
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Shaghayegh Haghjooy Javanmard
- Department of Physiology, Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | - Iman Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Nasrin Zare
- School of Medicine, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Jafar Ghaisari
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
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23
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Colson C, Byrne HM, Maini PK. Combining Mechanisms of Growth Arrest in Solid Tumours: A Mathematical Investigation. Bull Math Biol 2022; 84:80. [PMID: 35773547 PMCID: PMC9246818 DOI: 10.1007/s11538-022-01034-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 05/26/2022] [Indexed: 11/25/2022]
Abstract
The processes underpinning solid tumour growth involve the interactions between various healthy and tumour tissue components and the vasculature, and can be affected in different ways by cancer treatment. In particular, the growth-limiting mechanisms at play may influence tumour responses to treatment. In this paper, we propose a simple ordinary differential equation model of solid tumour growth to investigate how tumour-specific mechanisms of growth arrest may affect tumour response to different combination cancer therapies. We consider the interactions of tumour cells with the physical space in which they proliferate and a nutrient supplied by the tumour vasculature, with the aim of representing two distinct growth arrest mechanisms. More specifically, we wish to consider growth arrest due to (1) nutrient deficiency, which corresponds to balancing cell proliferation and death rates, and (2) competition for space, which corresponds to cessation of proliferation without cell death. We perform numerical simulations of the model and a steady-state analysis to determine the possible tumour growth scenarios described by the model. We find that there are three distinct growth regimes: the nutrient- and spatially limited regimes and a bi-stable regime, in which both growth arrest mechanisms are simultaneously active. Thus, the proposed model has the features required to investigate and distinguish tumour responses to different cancer treatments.
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Affiliation(s)
- Chloé Colson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK.
| | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK
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24
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Bergman D, Sweis RF, Pearson AT, Nazari F, Jackson TL. A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues. iScience 2022; 25:104387. [PMID: 35637730 PMCID: PMC9142654 DOI: 10.1016/j.isci.2022.104387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/30/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Agent-based models (ABMs) are a natural platform for capturing the multiple time and spatial scales in biological processes. However, these models are computationally expensive, especially when including molecular-level effects. The traditional approach to simulating this type of multiscale ABM is to solve a system of ordinary differential equations for the molecular events per cell. This significantly adds to the computational cost of simulations as the number of agents grows, which contributes to many ABMs being limited to around10 5 cells. We propose an approach that requires the same computational time independent of the number of agents. This speeds up the entire simulation by orders of magnitude, allowing for more thorough explorations of ABMs with even larger numbers of agents. We use two systems to show that the new method strongly agrees with the traditionally used approach. This computational strategy can be applied to a wide range of biological investigations.
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Affiliation(s)
- Daniel Bergman
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randy F. Sweis
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
| | - Alexander T. Pearson
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago, 5841 S Maryland Avenue, MC 2115, Chicago, IL 60605, USA
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25
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de Melo Quintela B, Hervas-Raluy S, Manuel Garcia Aznar J, Walker D, Wertheim KY, Viceconti M. A Theoretical Analysis of the Scale Separation in a Model to Predict Solid Tumour Growth. J Theor Biol 2022; 547:111173. [DOI: 10.1016/j.jtbi.2022.111173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 03/27/2022] [Accepted: 05/19/2022] [Indexed: 11/27/2022]
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26
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Liu F, Heiner M, Gilbert D. Hybrid modelling of biological systems: current progress and future prospects. Brief Bioinform 2022; 23:6555400. [PMID: 35352101 PMCID: PMC9116374 DOI: 10.1093/bib/bbac081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 11/15/2022] Open
Abstract
Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.
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Affiliation(s)
- Fei Liu
- School of Software Engineering, South China University of Technology, Guangzhou 510006, P.R. China
- Corresponding author: Fei Liu, School of Software Engineering, South China University of Technology, Guangzhou 510006, P.R. China. E-mail:
| | - Monika Heiner
- Department of Computer Science, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus 03046, Germany
| | - David Gilbert
- Department of Computer Science, Brunel University London, Middlesex UB8 3PH, UK
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Kulkarni P, Bhattacharya S, Achuthan S, Behal A, Jolly MK, Kotnala S, Mohanty A, Rangarajan G, Salgia R, Uversky V. Intrinsically Disordered Proteins: Critical Components of the Wetware. Chem Rev 2022; 122:6614-6633. [PMID: 35170314 PMCID: PMC9250291 DOI: 10.1021/acs.chemrev.1c00848] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Despite the wealth of knowledge gained about intrinsically disordered proteins (IDPs) since their discovery, there are several aspects that remain unexplored and, hence, poorly understood. A living cell is a complex adaptive system that can be described as a wetware─a metaphor used to describe the cell as a computer comprising both hardware and software and attuned to logic gates─capable of "making" decisions. In this focused Review, we discuss how IDPs, as critical components of the wetware, influence cell-fate decisions by wiring protein interaction networks to keep them minimally frustrated. Because IDPs lie between order and chaos, we explore the possibility that they can be modeled as attractors. Further, we discuss how the conformational dynamics of IDPs manifests itself as conformational noise, which can potentially amplify transcriptional noise to stochastically switch cellular phenotypes. Finally, we explore the potential role of IDPs in prebiotic evolution, in forming proteinaceous membrane-less organelles, in the origin of multicellularity, and in protein conformation-based transgenerational inheritance of acquired characteristics. Together, these ideas provide a new conceptual framework to discern how IDPs may perform critical biological functions despite their lack of structure.
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Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Supriyo Bhattacharya
- Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA, USA
| | - Srisairam Achuthan
- Division of Research Informatics, Center for Informatics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Amita Behal
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Sourabh Kotnala
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Govindan Rangarajan
- Department of Mathematics, Indian Institute of Science, Bangalore 560012, India
- Center for Neuroscience, Indian Institute of Science, Bangalore 560012, India
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Vladimir Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
- Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy pereulok, 9, Dolgoprudny, Moscow region 141700, Russia
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28
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Akasiadis C, Ponce‐de‐Leon M, Montagud A, Michelioudakis E, Atsidakou A, Alevizos E, Artikis A, Valencia A, Paliouras G. Parallel model exploration for tumor treatment simulations. Comput Intell 2022. [DOI: 10.1111/coin.12515] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Charilaos Akasiadis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | | | - Arnau Montagud
- Life Sciences Department Barcelona Supercomputing Center Barcelona Spain
| | - Evangelos Michelioudakis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
- Department of Informatics and Telecommunications University of Athens Athens Greece
| | - Alexia Atsidakou
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Elias Alevizos
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Alexander Artikis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Alfonso Valencia
- Life Sciences Department Barcelona Supercomputing Center Barcelona Spain
| | - Georgios Paliouras
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
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29
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Alwuthaynani M, Eftimie R, Trucu D. Inverse problem approaches for mutation laws in heterogeneous tumours with local and nonlocal dynamics. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3720-3747. [PMID: 35341271 DOI: 10.3934/mbe.2022171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cancer cell mutations occur when cells undergo multiple cell divisions, and these mutations can be spontaneous or environmentally-induced. The mechanisms that promote and sustain these mutations are still not fully understood. This study deals with the identification (or reconstruction) of the usually unknown cancer cell mutation law, which lead to the transformation of a primary tumour cell population into a secondary, more aggressive cell population. We focus on local and nonlocal mathematical models for cell dynamics and movement, and identify these mutation laws from macroscopic tumour snapshot data collected at some later stage in the tumour evolution. In a local cancer invasion model, we first reconstruct the mutation law when we assume that the mutations depend only on the surrounding cancer cells (i.e., the ECM plays no role in mutations). Second, we assume that the mutations depend on the ECM only, and we reconstruct the mutation law in this case. Third, we reconstruct the mutation when we assume that there is no prior knowledge about the mutations. Finally, for the nonlocal cancer invasion model, we reconstruct the mutation law that depends on the cancer cells and on the ECM. For these numerical reconstructions, our approximations are based on the finite difference method combined with the finite elements method. As the inverse problem is ill-posed, we use the Tikhonov regularisation technique in order to regularise the solution. Stability of the solution is examined by adding additive noise into the measurements.
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Affiliation(s)
- Maher Alwuthaynani
- Division of Mathematics, University of Dundee, Dundee DD1 4HN, Scotland, UK
| | - Raluca Eftimie
- Laboratoire Mathématiques de Besançcon, UMR-CNRS 6623, Université de Bourgogne Franche-Comté, 16 Route de Gray, Besançcon 25000, France
| | - Dumitru Trucu
- Division of Mathematics, University of Dundee, Dundee DD1 4HN, Scotland, UK
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30
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Kreger J, Komarova NL, Wodarz D. A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV. PLoS Comput Biol 2021; 17:e1009713. [PMID: 34936647 PMCID: PMC8730440 DOI: 10.1371/journal.pcbi.1009713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 01/05/2022] [Accepted: 12/02/2021] [Indexed: 11/30/2022] Open
Abstract
To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability. The evolution of human immunodeficiency virus within patients is an important part of the disease process. In particular, the presence of mutants that are resistant against anti-viral drugs can result in challenges to the long-term control of the infection. To study disease progression, computer simulations have been useful. However, in some cases these simulations can be difficult because of the complexity of the model. Here, we use a computational complexity reducing algorithm to simulate mutant dynamics in large populations, which can approximate the full model at a fraction of the time. The use of this algorithm allows us to study different transmission methods, viral processes that occur between virus strains within individual cells, and important quantities such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We find that the direct synaptic cell-to-cell transmission of the virus through virological synapses can have strong biological consequences because it can promote potentially defective viruses that carry genetic diversity which can be incorporated into functional virus genomes during infection. Through this process, synaptic transmission in human immunodeficiency virus might promote virus evolvability.
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Affiliation(s)
- Jesse Kreger
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
- * E-mail:
| | - Natalia L. Komarova
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
| | - Dominik Wodarz
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
- Department of Population Health and Disease Prevention Program in Public Health Susan and Henry Samueli College of Health Sciences, University of California, Irvine, California, United States of America
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31
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Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets. Bull Math Biol 2021; 83:115. [PMID: 34633559 DOI: 10.1007/s11538-021-00937-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/13/2021] [Indexed: 12/24/2022]
Abstract
Pancreatic ductal adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model consisting of pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs), as well as cytokines and growth factors which are responsible for intercellular communication between the PCCs and PSCs. We have built a stochastic Boolean network (BN) model, validated by literature and clinical data, in which we probed for intervention strategies that force this gene regulatory network (GRN) from a diseased state to a healthy state. To do so, we implemented methods from phenotype control theory to determine a procedure for regulating specific genes within the microenvironment. We identified target genes and molecules, such that the application of their control drives the GRN to the desired state by suppression (or expression) and disruption of specific signaling pathways that may eventually lead to the eradication of the cancer cells. After applying well-studied control methods such as stable motifs, feedback vertex sets, and computational algebra, we discovered that each produces a different set of control targets that are not necessarily minimal nor unique. Yet, we were able to gain more insight about the performance of each process and the overlap of targets discovered. Nearly every control set contains cytokines, KRas, and HER2/neu, which suggests they are key players in the system's dynamics. To that end, this model can be used to produce further insight into the complex biological system of pancreatic cancer with hopes of finding new potential targets.
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32
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Butner JD, Wang Z. Predicting immune checkpoint inhibitor response with mathematical modeling. Immunotherapy 2021; 13:1151-1155. [PMID: 34435504 DOI: 10.2217/imt-2021-0209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA.,Cancer Center, Houston Methodist Research Institute, Houston, TX 77030, USA.,Department of Physiology & Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
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33
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Paul D, Komarova NL. Multi-scale network targeting: A holistic systems-biology approach to cancer treatment. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:72-79. [PMID: 34428429 DOI: 10.1016/j.pbiomolbio.2021.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 11/15/2022]
Abstract
The vulnerabilities of cancer at the cellular and, recently, with the introduction of immunotherapy, at the tissue level, have been exploited with variable success. Evaluating the cancer system vulnerabilities at the organismic level through analysis of network topology and network dynamics can potentially predict novel anti-cancer drug targets directed at the macroscopic cancer networks. Theoretical work analyzing the properties and the vulnerabilities of the multi-scale network of cancer needs to go hand-in-hand with experimental research that uncovers the biological nature of the relevant networks and reveals new targetable vulnerabilities. It is our hope that attacking cancer on different spatial scales, in a concerted integrated approach, may present opportunities for novel ways to prevent treatment resistance.
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Affiliation(s)
- Doru Paul
- Medical Oncology, Weill Cornell Medicine, 1305 York Avenue 12th Floor, New York, NY, 10021, USA.
| | - Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA.
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34
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Akbarpour Ghazani M, Saghafian M, Jalali P, Soltani M. Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proc Inst Mech Eng H 2021; 235:1335-1355. [PMID: 34247529 PMCID: PMC8573697 DOI: 10.1177/09544119211028380] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Uncontrolled proliferation of cells in a tissue caused by genetic mutations inside a cell is referred to as a tumor. A tumor which grows rapidly encounters a barrier when it grows to a certain size in presence of preexisting vasculature. This is the time when it has to find a way to go on the growth. The tumor starts to secrete tumor angiogenic factors (TAFs) and stimulate preexisting vessels to grow new sprouts. These new sprouts will find their way to the tumor in the extracellular matrix (ECM) by the gradient of TAF. As these new capillaries anastomose and reach tumor, fresh oxygen is available for the tumor and it will reinitiate the growth. Number of initial sprouts, distance of initial tumor cells from the vessel(s) and initial density of the tumor at the time of sprout formation are questions which are to be investigated. In the present study, the aim is to find the response of tumor cells and vessels to the reciprocal effects of each other in different circumstances in the tissue. Together with a mathematical formulation, a radial basis function (RBF) neural network is established to predict the number of tumor cells at different circumstances including size and distance of initial tumors from the parent vessel. A final formulation is given for the final number of tumor cells as a function of initial tumor size and distance between a parent vessel and a tumor. Results of this simulation demonstrate that, increasing the distance between a tumor and a parent vessel decreases the number of final tumor cells. Specially, this decrement becomes faster beyond a certain distance. Moreover, initial tumors in bigger domains must become much bigger before inducing angiogenesis which makes it harder for them to survive.
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Affiliation(s)
- Mehran Akbarpour Ghazani
- Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran.,Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mohsen Saghafian
- Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Peyman Jalali
- Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.,Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada.,Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran
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35
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In-Silico Modeling of Tumor Spheroid Formation and Growth. MICROMACHINES 2021; 12:mi12070749. [PMID: 34202262 PMCID: PMC8303756 DOI: 10.3390/mi12070749] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 12/20/2022]
Abstract
Mathematical modeling has significant potential for understanding of biological models of cancer and to accelerate the progress in cross-disciplinary approaches of cancer treatment. In mathematical biology, solid tumor spheroids are often studied as preliminary in vitro models of avascular tumors. The size of spheroids and their cell number are easy to track, making them a simple in vitro model to investigate tumor behavior, quantitatively. The growth of solid tumors is comprised of three main stages: transient formation, monotonic growth and a plateau phase. The last two stages are extensively studied. However, the initial transient formation phase is typically missing from the literature. This stage is important in the early dynamics of growth, formation of clonal sub-populations, etc. In the current work, this transient formation is modeled by a reaction–diffusion partial differential equation (PDE) for cell concentration, coupled with an ordinary differential equation (ODE) for the spheroid radius. Analytical and numerical solutions of the coupled equations were obtained for the change in the radius of tumor spheroids over time. Human glioblastoma (hGB) cancer cells (U251 and U87) were spheroid cultured to validate the model prediction. Results of this study provide insight into the mechanism of development of solid tumors at their early stage of formation.
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36
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Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 2021; 13:3008. [PMID: 34208448 PMCID: PMC8234316 DOI: 10.3390/cancers13123008] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023] Open
Abstract
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Prashant K. Jha
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Angela M. Jarrett
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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37
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Bhattacharya P, Li Q, Lacroix D, Kadirkamanathan V, Viceconti M. A systematic approach to the scale separation problem in the development of multiscale models. PLoS One 2021; 16:e0251297. [PMID: 34003842 PMCID: PMC8130972 DOI: 10.1371/journal.pone.0251297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 04/25/2021] [Indexed: 11/19/2022] Open
Abstract
Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. Among the many challenges posed by such 'multiscale' problems, that of defining a 'scale' remains poorly addressed. This fundamental problem has led to much confusion in the field of biomedical engineering in particular. The present study proposes a definition of scale based on measurement limitations of existing instruments, available computational power, and on the ranges of time and space over which quantities of interest vary characteristically. The definition is used to construct a multiscale modelling methodology from start to finish, beginning with a description of the system (portion of reality of interest) and ending with an algorithmic orchestration of mathematical models at different scales within the system. The methodology is illustrated for a specific but well-researched problem. The concept of scale and the multiscale modelling approach introduced are shown to be easily adaptable to other closely related problems. Although out of the scope of this paper, we believe that the proposed methodology can be applied widely throughout engineering.
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Affiliation(s)
- Pinaki Bhattacharya
- Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom
- INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
- * E-mail:
| | - Qiao Li
- Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom
- INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Damien Lacroix
- Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom
- INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Visakan Kadirkamanathan
- INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Marco Viceconti
- Dipartimento di Ingegneria Industriale, Alma Mater Studiorum – University of Bologna, Bologna, Italy
- Laboratorio di Tecnologia Medica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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38
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Waters SL, Schumacher LJ, El Haj AJ. Regenerative medicine meets mathematical modelling: developing symbiotic relationships. NPJ Regen Med 2021; 6:24. [PMID: 33846347 PMCID: PMC8042047 DOI: 10.1038/s41536-021-00134-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 02/26/2021] [Indexed: 02/01/2023] Open
Abstract
Successful progression from bench to bedside for regenerative medicine products is challenging and requires a multidisciplinary approach. What has not yet been fully recognised is the potential for quantitative data analysis and mathematical modelling approaches to support this process. In this review, we highlight the wealth of opportunities for embedding mathematical and computational approaches within all stages of the regenerative medicine pipeline. We explore how exploiting quantitative mathematical and computational approaches, alongside state-of-the-art regenerative medicine research, can lead to therapies that potentially can be more rapidly translated into the clinic.
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Affiliation(s)
- S L Waters
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, Radcliffe Observatory Quarter, University of Oxford, Oxford, UK
| | - L J Schumacher
- Centre for Regenerative Medicine, The University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK
| | - A J El Haj
- Healthcare Technology Institute, Institute of Translational Medicine, School of Chemical Engineering, University of Birmingham, Birmingham, UK.
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39
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Corradetti B, Dogra P, Pisano S, Wang Z, Ferrari M, Chen SH, Sidman RL, Pasqualini R, Arap W, Cristini V. Amphibian regeneration and mammalian cancer: Similarities and contrasts from an evolutionary biology perspective: Comparing the regenerative potential of mammalian embryos and urodeles to develop effective strategies against human cancer. Bioessays 2021; 43:e2000339. [PMID: 33751590 DOI: 10.1002/bies.202000339] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/18/2021] [Accepted: 02/23/2021] [Indexed: 12/11/2022]
Abstract
Here we review and discuss the link between regeneration capacity and tumor suppression comparing mammals (embryos versus adults) with highly regenerative vertebrates. Similar to mammal embryo morphogenesis, in amphibians (essentially newts and salamanders) the reparative process relies on a precise molecular and cellular machinery capable of sensing abnormal signals and actively reprograming or eliminating them. As the embryo's evil twin, tumor also retains common functional attributes. The immune system plays a pivotal role in maintaining a physiological balance to provide surveillance against tumor initiation or to support its initiation and progression. We speculate that susceptibility to cancer development in adult mammals may be determined by the loss of an advanced regenerative capability during evolution and believe that gaining mechanistic insights into how regenerative capacity linked to tumor suppression is postnatally lost in mammals might illuminate an as yet unrecognized route to cancer treatment.
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Affiliation(s)
- Bruna Corradetti
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas, USA.,Texas A&M Health Science Center, College of Medicine, 8446 Riverside Pkwy, Bryan, TX, 77807, USA.,Swansea University Medical School, Swansea, Wales, UK
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Simone Pisano
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas, USA.,Swansea University Medical School, Swansea, Wales, UK
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Mauro Ferrari
- Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
| | - Shu-Hsia Chen
- Immunotherapy Research Center, Houston Methodist Research Institute, Houston, Texas, USA.,Cancer Center, Houston Methodist Research Institute, Houston, Texas, USA
| | - Richard L Sidman
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
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40
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Lu H, Um K, Tartakovsky DM. Hybrid models of chemotaxis with application to leukocyte migration. J Math Biol 2021; 82:23. [PMID: 33646399 DOI: 10.1007/s00285-021-01581-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/24/2020] [Accepted: 02/14/2021] [Indexed: 11/28/2022]
Abstract
Many chemical and biological systems involve reacting species with vastly different numbers of molecules/agents. Hybrid simulations model such phenomena by combining discrete (e.g., agent-based) and continuous (e.g., partial differential equation- or PDE-based) descriptors of the dynamics of reactants with small and large numbers of molecules/agents, respectively. We present a stochastic hybrid algorithm to model a stage of the immune response to inflammation, during which leukocytes reach a pathogen via chemotaxis. While large numbers of chemoattractant molecules justify the use of a PDE-based model to describe the spatiotemporal evolution of its concentration, relatively small numbers of leukocytes and bacteria involved in the process undermine the veracity of their continuum treatment by masking the effects of stochasticity and have to be treated discretely. Motility and interactions between leukocytes and bacteria are modeled via random walk and a stochastic simulation algorithm, respectively. Since the latter assumes the reacting species to be well mixed, the discrete component of our hybrid algorithm deploys stochastic operator splitting, in which the sequence of the diffusion and reaction operations is determined autonomously during each simulation step. We conduct a series of numerical experiments to ascertain the accuracy and computational efficiency of our hybrid simulations and, then, to demonstrate the importance of randomness for predicting leukocyte migration and fate during the immune response to inflammation.
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Affiliation(s)
- Hannah Lu
- Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, CA, 94305, USA
| | - Kimoon Um
- Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, CA, 94305, USA
| | - Daniel M Tartakovsky
- Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, CA, 94305, USA.
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41
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A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors. PLoS Comput Biol 2021; 17:e1008266. [PMID: 33566821 PMCID: PMC7901744 DOI: 10.1371/journal.pcbi.1008266] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 02/23/2021] [Accepted: 01/16/2021] [Indexed: 12/12/2022] Open
Abstract
Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.
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42
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Lecca P. Control Theory and Cancer Chemotherapy: How They Interact. Front Bioeng Biotechnol 2021; 8:621269. [PMID: 33520972 PMCID: PMC7841331 DOI: 10.3389/fbioe.2020.621269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/09/2020] [Indexed: 12/01/2022] Open
Abstract
Control theory arises in most modern real-life applications, not least in biological and medical applications. In particular, in biological and medical contexts, the role of control theory began to take shape in the early 1980s when the first works appeared on the application of control theory in models of pharmacokinetics and pharmacodynamics for antitumor therapies. Forty years after those first works, the theory of control continues to be considered a mathematical analysis tool of extreme importance and usefulness, but the challenges it must overcome in order to manage the complexity of biological processes are in fact not yet overcome. In this article, we introduce the reader to the basic ideas of control theory, its aims and its mathematical formalization, and we review its use in cell phase-specific models for cancer chemotherapy. We discuss strengths and limitations of the control theory approach to the analysis pharmacokinetics and pharmacodynamics models, and we will see that most of them are strongly related to data availability and mathematical form of the model. We propose some future research directions that could prove useful in overcoming the these limitations and we indicate the crucial steps preliminary to a useful and informative application of control theory to cancer chemotherapy modeling.
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Affiliation(s)
- Paola Lecca
- Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy
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43
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Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramírez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V. A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden. Nat Biomed Eng 2021; 5:297-308. [PMID: 33398132 PMCID: PMC8669771 DOI: 10.1038/s41551-020-00662-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/14/2020] [Indexed: 02/06/2023]
Abstract
A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time-course of tumour responses to immune-checkpoint inhibitors. The model takes into account intrinsic tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug–cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug–cancer sensitivities in individual patients.
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Affiliation(s)
- Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Karine A Al Feghali
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute, University of Basel, Basel, Switzerland
| | - George A Calin
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Javier Ruiz-Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Geoffrey V Martin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hussein A Tawbi
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James W Welsh
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. .,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.
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44
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Rosales GS, Darias NT. Introduction to Multiscale Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11472-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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45
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Kazerouni AS, Gadde M, Gardner A, Hormuth DA, Jarrett AM, Johnson KE, Lima EAF, Lorenzo G, Phillips C, Brock A, Yankeelov TE. Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology. iScience 2020; 23:101807. [PMID: 33299976 PMCID: PMC7704401 DOI: 10.1016/j.isci.2020.101807] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.
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Affiliation(s)
- Anum S. Kazerouni
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Manasa Gadde
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
| | - Andrea Gardner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kaitlyn E. Johnson
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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46
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Directionality of Macrophages Movement in Tumour Invasion: A Multiscale Moving-Boundary Approach. Bull Math Biol 2020; 82:148. [PMID: 33211193 PMCID: PMC7677171 DOI: 10.1007/s11538-020-00819-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 10/07/2020] [Indexed: 12/11/2022]
Abstract
Invasion of the surrounding tissue is one of the recognised hallmarks of cancer (Hanahan and Weinberg in Cell 100: 57–70, 2000. 10.1016/S0092-8674(00)81683-9), which is accomplished through a complex heterotypic multiscale dynamics involving tissue-scale random and directed movement of the population of both cancer cells and other accompanying cells (including here, the family of tumour-associated macrophages) as well as the emerging cell-scale activity of both the matrix-degrading enzymes and the rearrangement of the cell-scale constituents of the extracellular matrix (ECM) fibres. The involved processes include not only the presence of cell proliferation and cell adhesion (to other cells and to the extracellular matrix), but also the secretion of matrix-degrading enzymes. This is as a result of cancer cells as well as macrophages, which are one of the most abundant types of immune cells in the tumour micro-environment. In large tumours, these tumour-associated macrophages (TAMs) have a tumour-promoting phenotype, contributing to tumour proliferation and spread. In this paper, we extend a previous multiscale moving-boundary mathematical model for cancer invasion, by considering also the multiscale effects of TAMs, with special focus on the influence that their directional movement exerts on the overall tumour progression. Numerical investigation of this new model shows the importance of the interactions between pro-tumour TAMs and the fibrous ECM, highlighting the impact of the fibres on the spatial structure of solid tumour.
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47
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Dogra P, Butner JD, Ramirez JR, Cristini V, Wang Z. Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2447-2450. [PMID: 33018501 DOI: 10.1109/embc44109.2020.9175322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The application of nanomedicine for diagnosis and treatment of cancer has immense potential, but has witnessed only limited clinical success, in part due to insufficient understanding of the role of nanomaterial properties and physiological variables in governing nanoparticle (NP) pharmacology. Here, we present a multiscale mathematical model to examine the effects of physiological changes associated with patient age on the pharmacokinetics and tumor delivery efficiency of NPs. We show that physiological changes due to aging prolong the residence of NPs in the systemic circulation, thereby improving passive accumulation of NPs in tumors.Clinical Relevance - Understanding the effect of inter-individual variability on the pharmacological behavior of nanomaterials will improve their clinical translatability.
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48
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Frieboes HB, Raghavan S, Godin B. Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis. Front Bioeng Biotechnol 2020; 8:1011. [PMID: 32974325 PMCID: PMC7466654 DOI: 10.3389/fbioe.2020.01011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 08/03/2020] [Indexed: 12/13/2022] Open
Abstract
The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.
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Affiliation(s)
- Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, United States
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, United States
- Center for Predictive Medicine, University of Louisville, Louisville, KY, United States
| | - Shreya Raghavan
- Department of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX, United States
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, TX, United States
- Developmental Therapeutics Program, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, United States
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49
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Al‐Huniti N, Feng Y, Yu J(J, Lu Z, Nagase M, Zhou D, Sheng J. Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities. CPT Pharmacometrics Syst Pharmacol 2020; 9:419-427. [PMID: 32589767 PMCID: PMC7438808 DOI: 10.1002/psp4.12542] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/11/2020] [Indexed: 12/29/2022] Open
Abstract
Model-informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients' survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.
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Affiliation(s)
- Nidal Al‐Huniti
- Quantitative PharmacologyRegeneron PharmaceuticalsNew YorkNew YorkUSA
| | - Yan Feng
- Clinical Pharmacology and PharmacometricsBristol‐Myers SquibbLawrencevilleNew JerseyUSA
| | - Jingyu (Jerry) Yu
- Division of PharmacometricsUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Zheng Lu
- Clinical Pharmacology and PharmacometricsAstellasIllinoisUSA
| | - Mario Nagase
- Department of Clinical Pharmacology and Safety ScienceBioPharmaceuticals R&DAstraZenecaBostonMassachusettsUSA
| | - Diansong Zhou
- Department of Clinical Pharmacology and Safety ScienceBioPharmaceuticals R&DAstraZenecaBostonMassachusettsUSA
| | - Jennifer Sheng
- Clinical Pharmacology and PharmacometricsBristol‐Myers SquibbLawrencevilleNew JerseyUSA
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50
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Abstract
Tumor immunology is undergoing a renaissance due to the recent profound clinical successes of tumor immunotherapy. These advances have coincided with an exponential growth in the development of -omics technologies. Armed with these technologies and their associated computational and modeling toolsets, systems biologists have turned their attention to tumor immunology in an effort to understand the precise nature and consequences of interactions between tumors and the immune system. Such interactions are inherently multivariate, spanning multiple time and size scales, cell types, and organ systems, rendering systems biology approaches particularly amenable to their interrogation. While in its infancy, the field of 'Cancer Systems Immunology' has already influenced our understanding of tumor immunology and immunotherapy. As the field matures, studies will move beyond descriptive characterizations toward functional investigations of the emergent behavior that govern tumor-immune responses. Thus, Cancer Systems Immunology holds incredible promise to advance our ability to fight this disease.
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Affiliation(s)
| | - Edgar G Engleman
- Department of Pathology, Stanford University School of MedicineStanfordUnited States
- Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of MedicineStanfordUnited States
- Stanford Cancer Institute, Stanford UniversityStanfordUnited States
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