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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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Chiang CT, Lau R, Ghaffarizadeh A, Brovold M, Vyas D, Juárez EF, Atala A, Agus DB, Soker S, Macklin P, Ruderman D, Mumenthaler SM. High-throughput microscopy reveals the impact of multifactorial environmental perturbations on colorectal cancer cell growth. Gigascience 2021; 10:6237161. [PMID: 33871006 PMCID: PMC8054261 DOI: 10.1093/gigascience/giab026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 11/21/2020] [Accepted: 03/15/2021] [Indexed: 12/27/2022] Open
Abstract
Background Colorectal cancer (CRC) mortality is principally due to metastatic disease, with the most frequent organ of metastasis being the liver. Biochemical and mechanical factors residing in the tumor microenvironment are considered to play a pivotal role in metastatic growth and response to therapy. However, it is difficult to study the tumor microenvironment systematically owing to a lack of fully controlled model systems that can be investigated in rigorous detail. Results We present a quantitative imaging dataset of CRC cell growth dynamics influenced by in vivo–mimicking conditions. They consist of tumor cells grown in various biochemical and biomechanical microenvironmental contexts. These contexts include varying oxygen and drug concentrations, and growth on conventional stiff plastic, softer matrices, and bioengineered acellular liver extracellular matrix. Growth rate analyses under these conditions were performed via the cell phenotype digitizer (CellPD). Conclusions Our data indicate that the growth of highly aggressive HCT116 cells is affected by oxygen, substrate stiffness, and liver extracellular matrix. In addition, hypoxia has a protective effect against oxaliplatin-induced cytotoxicity on plastic and liver extracellular matrix. This expansive dataset of CRC cell growth measurements under in situ relevant environmental perturbations provides insights into critical tumor microenvironment features contributing to metastatic seeding and tumor growth. Such insights are essential to dynamical modeling and understanding the multicellular tumor-stroma dynamics that contribute to metastatic colonization. It also establishes a benchmark dataset for training and testing data-driven dynamical models of cancer cell lines and therapeutic response in a variety of microenvironmental conditions.
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Affiliation(s)
- Chun-Te Chiang
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
| | - Roy Lau
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
| | - Ahmadreza Ghaffarizadeh
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
| | - Matthew Brovold
- Wake Forest Institute for Regenerative Medicine, Winston-Salem, NC 27157, USA
| | - Dipen Vyas
- Wake Forest Institute for Regenerative Medicine, Winston-Salem, NC 27157, USA
| | - Edwin F Juárez
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
| | - Anthony Atala
- Wake Forest Institute for Regenerative Medicine, Winston-Salem, NC 27157, USA
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
| | - Shay Soker
- Wake Forest Institute for Regenerative Medicine, Winston-Salem, NC 27157, USA
| | - Paul Macklin
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA.,Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Daniel Ruderman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA 90064, USA
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Marku M, Verstraete N, Raynal F, Madrid-Mencía M, Domagala M, Fournié JJ, Ysebaert L, Poupot M, Pancaldi V. Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies. Cancers (Basel) 2020; 12:cancers12123664. [PMID: 33297362 PMCID: PMC7762229 DOI: 10.3390/cancers12123664] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/26/2020] [Accepted: 11/30/2020] [Indexed: 12/24/2022] Open
Abstract
Simple Summary The recent success of immunotherapy treatments against cancer relies on helping our own body’s defenses in the fight against tumours, namely reinvigorating the cancer killing action of T cells. Unfortunately, in a large proportion of patients these therapies are ineffective, in part due to the presence of other immune cells, macrophages, which are mis-educated by the cancer cells into promoting tumour growth. Here we start from an existing model of macrophage polarization and extend it to the specific conditions encountered inside a tumour by adding signals, receptors, transcription factors and cytokines that are known to be the key components in establishing the cancer cell-macrophage interaction. Then we use a mathematical Boolean model applied to a gene regulatory network of this biological process to simulate its temporal behaviour and explore scenarios that have not been experimentally tested so far. Additionally, the KO and overexpression simulations successfully reproduce the known experimental results while predicting the potential role of regulators (such as STAT1 and EGF) in preventing the formation of pro-tumoural macrophages, which can be tested experimentally. Abstract The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients.
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Affiliation(s)
- Malvina Marku
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
- Correspondence: (M.M.); (V.P.); Tel.: +33-5-82-74-17-74 (M.M.)
| | - Nina Verstraete
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
| | - Flavien Raynal
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
| | - Miguel Madrid-Mencía
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
| | - Marcin Domagala
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
| | - Jean-Jacques Fournié
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
| | - Loïc Ysebaert
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
- Service d’Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, 31330 Toulouse, France
| | - Mary Poupot
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
| | - Vera Pancaldi
- INSERM, Centre de Recherches en Cancérologie de Toulouse, 2 Avenue Hubert Curien, 31037 Toulouse, France; (N.V.); (F.R.); (M.M.-M.); (M.D.); (J.-J.F.); (L.Y.); (M.P.)
- Université III Toulouse Paul Sabatier, Route de Narbonne, 31330 Toulouse, France
- Barcelona Supercomputing Center, Carrer de Jordi Girona, 29, 31, 08034 Barcelona, Spain
- Correspondence: (M.M.); (V.P.); Tel.: +33-5-82-74-17-74 (M.M.)
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Mathias S, Coulier A, Bouchnita A, Hellander A. Impact of Force Function Formulations on the Numerical Simulation of Centre-Based Models. Bull Math Biol 2020; 82:132. [PMID: 33025278 PMCID: PMC7538447 DOI: 10.1007/s11538-020-00810-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 09/21/2020] [Indexed: 12/17/2022]
Abstract
Centre-based or cell-centre models are a framework for the computational study of multicellular systems with widespread use in cancer modelling and computational developmental biology. At the core of these models are the numerical method used to update cell positions and the force functions that encode the pairwise mechanical interactions of cells. For the latter, there are multiple choices that could potentially affect both the biological behaviour captured, and the robustness and efficiency of simulation. For example, available open-source software implementations of centre-based models rely on different force functions for their default behaviour and it is not straightforward for a modeller to know if these are interchangeable. Our study addresses this problem and contributes to the understanding of the potential and limitations of three popular force functions from a numerical perspective. We show empirically that choosing the force parameters such that the relaxation time for two cells after cell division is consistent between different force functions results in good agreement of the population radius of a two-dimensional monolayer relaxing mechanically after intense cell proliferation. Furthermore, we report that numerical stability is not sufficient to prevent unphysical cell trajectories following cell division, and consequently, that too large time steps can cause geometrical differences at the population level.
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Affiliation(s)
- Sonja Mathias
- Department of Information Technology, Uppsala University, Uppsala, Sweden.
| | - Adrien Coulier
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Anass Bouchnita
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Ecole Centrale Casablanca, Bouskoura, Morocco
| | - Andreas Hellander
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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Metzcar J, Wang Y, Heiland R, Macklin P. A Review of Cell-Based Computational Modeling in Cancer Biology. JCO Clin Cancer Inform 2020; 3:1-13. [PMID: 30715927 PMCID: PMC6584763 DOI: 10.1200/cci.18.00069] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.
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Faghihi D, Feng X, Lima EABF, Oden JT, Yankeelov TE. A Coupled Mass Transport and Deformation Theory of Multi-constituent Tumor Growth. JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS 2020; 139:103936. [PMID: 32394987 PMCID: PMC7213200 DOI: 10.1016/j.jmps.2020.103936] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We develop a general class of thermodynamically consistent, continuum models based on mixture theory with phase effects that describe the behavior of a mass of multiple interacting constituents. The constituents consist of solid species undergoing large elastic deformations and compressible viscous fluids. The fundamental building blocks framing the mixture theories consist of the mass balance law of diffusing species and microscopic (cellular scale) and macroscopic (tissue scale) force balances, as well as energy balance and the entropy production inequality derived from the first and second laws of thermodynamics. A general phase-field framework is developed by closing the system through postulating constitutive equations (i.e., specific forms of free energy and rate of dissipation potentials) to depict the growth of tumors in a microenvironment. A notable feature of this theory is that it contains a unified continuum mechanics framework for addressing the interactions of multiple species evolving in both space and time and involved in biological growth of soft tissues (e.g., tumor cells and nutrients). The formulation also accounts for the regulating roles of the mechanical deformation on the growth of tumors, through a physically and mathematically consistent coupled diffusion and deformation framework. A new algorithm for numerical approximation of the proposed model using mixed finite elements is presented. The results of numerical experiments indicate that the proposed theory captures critical features of avascular tumor growth in the various microenvironment of living tissue, in agreement with the experimental studies in the literature.
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Affiliation(s)
- Danial Faghihi
- Department of Mechanical and Aerospace Engineering, University at Buffalo
| | - Xinzeng Feng
- Oden Institute for Computational Engineering and Sciences
| | | | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin
- Department of Mathematics, The University of Texas at Austin
- Department of Computer Science, The University of Texas at Austin
- Livestrong Cancer Institutes, The University of Texas at Austin
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences
- Department of Biomedical Engineering, The University of Texas at Austin
- Department of Diagnostic Medicine, The University of Texas at Austin
- Department of Oncology, The University of Texas at Austin
- Livestrong Cancer Institutes, The University of Texas at Austin
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Macklin P. Key challenges facing data-driven multicellular systems biology. Gigascience 2019; 8:giz127. [PMID: 31648301 PMCID: PMC6812467 DOI: 10.1093/gigascience/giz127] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Revised: 09/27/2019] [Accepted: 09/30/2019] [Indexed: 12/17/2022] Open
Abstract
Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and financial) commitment.
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Affiliation(s)
- Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, 700 N Woodlawn Ave, Bloomington, IN 47408, USA
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Jacobs J, Rockne RC, Hawkins-Daarud AJ, Jackson PR, Johnston SK, Kinahan P, Swanson KR. Improved model prediction of glioma growth utilizing tissue-specific boundary effects. Math Biosci 2019; 312:59-66. [PMID: 31009624 DOI: 10.1016/j.mbs.2019.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 05/04/2018] [Accepted: 04/19/2019] [Indexed: 10/27/2022]
Abstract
Kinetic parameter estimates for mathematical models of glioblastoma multiforme (GBM), derived from clinical scans, have been used to predict the occurrence of hypoxia, necrosis, response to radiation therapy, and overall survival. Modeling GBM growth in a cerebral model encounters anatomical boundaries that interfere with model calibration from clinical measurements. METHODS The effect of boundaries is examined on both spherically symmetric and anatomical models of tumor growth. This effect is incorporated into a method that updates kinetic parameters. The efficacy of this method in reproducing clinical image-derived subject data is evaluated. RESULTS Spherically symmetric simulations of tumor growth with simple boundaries behave predictably when in a linear phase of growth. Anatomic simulations of eleven out of twenty subjects demonstrated improved fit to subject data with the new method. When only subjects exhibiting linear growth are considered, eight out of nine subject demonstrate improved fit to the data. CONCLUSION Anatomical boundaries to tumor growth measurably deflect progression and affect estimates of kinetic parameters. The presented method reliably updates kinetic parameters to fit anatomic computational models to clinically derived subject data when those data are in a linear regime.
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Affiliation(s)
- Joshua Jacobs
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA.
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope, Duarte, CA, USA
| | | | | | | | - Paul Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
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Stéphanou A, Fanchon E, Innominato PF, Ballesta A. Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why. Acta Biotheor 2018; 66:345-365. [PMID: 29744615 DOI: 10.1007/s10441-018-9330-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 05/05/2018] [Indexed: 12/22/2022]
Abstract
Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.
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Affiliation(s)
- Angélique Stéphanou
- Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38000, Grenoble, France.
| | - Eric Fanchon
- Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38000, Grenoble, France
| | - Pasquale F Innominato
- North Wales Cancer Centre, Betsi Cadwaladr University Health Board, Bangor, Denbighshire, UK
- INSERM and Université Paris 11 Unit 935, Villejuif, France
- University of Warwick, Coventry, UK
| | - Annabelle Ballesta
- INSERM and Université Paris 11 Unit 935, Villejuif, France
- University of Warwick, Coventry, UK
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Aristotelous AC, Crawford JM, Edwards GS, Kiehart DP, Venakides S. Mathematical models of dorsal closure. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 137:111-131. [PMID: 29852207 PMCID: PMC6109426 DOI: 10.1016/j.pbiomolbio.2018.05.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 05/20/2018] [Accepted: 05/22/2018] [Indexed: 12/13/2022]
Abstract
Dorsal closure is a model cell sheet movement that occurs midway through Drosophila embryogenesis. A dorsal hole, filled with amnioserosa, closes through the dorsalward elongation of lateral epidermal cell sheets. Closure requires contributions from 5 distinct tissues and well over 140 genes (see Mortensen et al., 2018, reviewed in Kiehart et al., 2017 and Hayes and Solon, 2017). In spite of this biological complexity, the movements (kinematics) of closure are geometrically simple at tissue, and in certain cases, at cellular scales. This simplicity has made closure the target of a number of mathematical models that seek to explain and quantify the processes that underlie closure's kinematics. The first (purely kinematic) modeling approach recapitulated well the time-evolving geometry of closure even though the underlying physical principles were not known. Almost all subsequent models delve into the forces of closure (i.e. the dynamics of closure). Models assign elastic, contractile and viscous forces which impact tissue and/or cell mechanics. They write rate equations which relate the forces to one another and to other variables, including those which represent geometric, kinematic, and or signaling characteristics. The time evolution of the variables is obtained by computing the solution of the model's system of equations, with optimized model parameters. The basis of the equations range from the phenomenological to biophysical first principles. We review various models and present their contribution to our understanding of the molecular mechanisms and biophysics of closure. Models of closure will contribute to our understanding of similar movements that characterize vertebrate morphogenesis.
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Affiliation(s)
- A C Aristotelous
- Department of Mathematics, West Chester University, West Chester, PA, USA.
| | - J M Crawford
- Department of Biology, Duke University, Durham, NC, USA
| | - G S Edwards
- Department of Physics, Duke University, Durham, NC, USA
| | - D P Kiehart
- Department of Biology, Duke University, Durham, NC, USA.
| | - S Venakides
- Department of Mathematics, Duke University, Durham, NC, USA
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11
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Ghaffarizadeh A, Heiland R, Friedman SH, Mumenthaler SM, Macklin P. PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput Biol 2018; 14:e1005991. [PMID: 29474446 PMCID: PMC5841829 DOI: 10.1371/journal.pcbi.1005991] [Citation(s) in RCA: 190] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 03/07/2018] [Accepted: 01/19/2018] [Indexed: 02/07/2023] Open
Abstract
Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells (the "players" upon the stage). PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility "out of the box." The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a "cellular cargo delivery" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.
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Affiliation(s)
- Ahmadreza Ghaffarizadeh
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Randy Heiland
- Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Samuel H. Friedman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
- Opto-Knowledge Systems, Inc., Torrance, California, United States of America
| | - Shannon M. Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Paul Macklin
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
- Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
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Benson JD, Higgins AZ, Desai K, Eroglu A. A toxicity cost function approach to optimal CPA equilibration in tissues. Cryobiology 2018; 80:144-155. [PMID: 28966012 PMCID: PMC8183460 DOI: 10.1016/j.cryobiol.2017.09.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 09/26/2017] [Accepted: 09/27/2017] [Indexed: 11/27/2022]
Abstract
There is growing need for cryopreserved tissue samples that can be used in transplantation and regenerative medicine. While a number of specific tissue types have been successfully cryopreserved, this success is not general, and there is not a uniform approach to cryopreservation of arbitrary tissues. Additionally, while there are a number of long-established approaches towards optimizing cryoprotocols in single cell suspensions, and even plated cell monolayers, computational approaches in tissue cryopreservation have classically been limited to explanatory models. Here we develop a numerical approach to adapt cell-based CPA equilibration damage models for use in a classical tissue mass transport model. To implement this with real-world parameters, we measured CPA diffusivity in three human-sourced tissue types, skin, fibroid and myometrium, yielding propylene glycol diffusivities of 0.6 × 10-6 cm2/s, 1.2 × 10-6 cm2/s and 1.3 × 10-6 cm2/s, respectively. Based on these results, we numerically predict and compare optimal multistep equilibration protocols that minimize the cell-based cumulative toxicity cost function and the damage due to excessive osmotic gradients at the tissue boundary. Our numerical results show that there are fundamental differences between protocols designed to minimize total CPA exposure time in tissues and protocols designed to minimize accumulated CPA toxicity, and that "one size fits all" stepwise approaches are predicted to be more toxic and take considerably longer than needed.
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Affiliation(s)
- James D Benson
- Department of Biology, University of Saskatchewan, Canada.
| | - Adam Z Higgins
- School of Chemical, Biological and Environmental Engineering, Oregon State University, USA
| | - Kunjan Desai
- Department of Neuroscience and Regenerative Medicine, Medical College of Georgia, Augusta University, USA
| | - Ali Eroglu
- Department of Neuroscience and Regenerative Medicine, Medical College of Georgia, Augusta University, USA
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Doloman A, Varghese H, Miller CD, Flann NS. Modeling de novo granulation of anaerobic sludge. BMC SYSTEMS BIOLOGY 2017; 11:69. [PMID: 28716030 PMCID: PMC5514506 DOI: 10.1186/s12918-017-0443-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 06/30/2017] [Indexed: 01/19/2023]
Abstract
BACKGROUND A unique combination of mechanical, physiochemical and biological forces influences granulation during processes of anaerobic digestion. Understanding this process requires a systems biology approach due to the need to consider not just single-cell metabolic processes, but also the multicellular organization and development of the granule. RESULTS In this computational experiment, we address the role that physiochemical and biological processes play in granulation and provide a literature-validated working model of anaerobic granule de novo formation. The agent-based model developed in a cDynoMiCs simulation environment successfully demonstrated a de novo granulation in a glucose fed system, with the average specific methanogenic activity of 1.11 ml C H 4/g biomass and formation of a 0.5 mm mature granule in 33 days. The simulated granules exhibit experimental observations of radial stratification: a central dead core surrounded by methanogens then encased in acidogens. Practical application of the granulation model was assessed on the anaerobic digestion of low-strength wastewater by measuring the changes in methane yield as experimental configuration parameters were systematically searched. CONCLUSIONS In the model, the emergence of multicellular organization of anaerobic granules from randomly mixed population of methanogens and acidogens was observed and validated. The model of anaerobic de novo granulation can be used to predict the morphology of the anaerobic granules in a alternative substrates of interest and to estimate methane potential of the resulting microbial consortia. The study demonstrates a successful integration of a systems biology approach to model multicellular systems with the engineering of an efficient anaerobic digestion system.
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Affiliation(s)
- Anna Doloman
- Department of Biological Engineering, Utah State University, Old Main Hill 4105, Logan, 84322-4105, UT, USA
| | - Honey Varghese
- Department of Computer Science, Utah State University, Old Main Hill 420, Logan, 84322-4205, UT, USA
| | - Charles D Miller
- Department of Biological Engineering, Utah State University, Old Main Hill 4105, Logan, 84322-4105, UT, USA
| | - Nicholas S Flann
- Department of Computer Science, Utah State University, Old Main Hill 420, Logan, 84322-4205, UT, USA.
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Sego TJ, Kasacheuski U, Hauersperger D, Tovar A, Moldovan NI. A heuristic computational model of basic cellular processes and oxygenation during spheroid-dependent biofabrication. Biofabrication 2017; 9:024104. [DOI: 10.1088/1758-5090/aa6ed4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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