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Coggan H, Weeden CE, Pearce P, Dalwadi MP, Magness A, Swanton C, Page KM. An agent-based modelling framework to study growth mechanisms in EGFR-L858R mutant cell alveolar type II cells. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240413. [PMID: 39021764 PMCID: PMC11252670 DOI: 10.1098/rsos.240413] [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: 03/12/2024] [Revised: 05/21/2024] [Accepted: 06/11/2024] [Indexed: 07/20/2024]
Abstract
Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in healthy lung stem cells, we grow murine alveolar type II organoids monoclonally in a three-dimensional Matrigel. Our experiments show that the EGFR-L858R mutation induces a change in organoid structure: mutated organoids display more 'budding', in comparison with non-mutant controls, which are nearly spherical. We perform on-lattice computational simulations, which suggest that this can be explained by the concentration of division among a small number of cells on the surface of the mutated organoids. We are currently unable to distinguish the cell-based mechanisms that lead to this spatial heterogeneity in growth, but suggest a number of future experiments which could be used to do so. We suggest that the likelihood of L858R-fuelled tumorigenesis is affected by whether the mutation arises in a spatial environment that allows the development of these surface protrusions. These data may have implications for cancer prevention strategies and for understanding NSCLC progression.
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Affiliation(s)
- Helena Coggan
- Department of Mathematics, University College London, London, UK
| | - Clare E. Weeden
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Philip Pearce
- Department of Mathematics, University College London, London, UK
- UCL Institute for the Physics of Living Systems, London, UK
| | - Mohit P. Dalwadi
- Department of Mathematics, University College London, London, UK
- UCL Institute for the Physics of Living Systems, London, UK
| | - Alastair Magness
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospital, London, UK
| | - Karen M. Page
- Department of Mathematics, University College London, London, UK
- UCL Institute for the Physics of Living Systems, London, UK
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Thalheim T, Aust G, Galle J. Organoid Cultures In Silico: Tools or Toys? BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010050. [PMID: 36671623 PMCID: PMC9854934 DOI: 10.3390/bioengineering10010050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023]
Abstract
The implementation of stem-cell-based organoid culture more than ten years ago started a development that created new avenues for diagnostic analyses and regenerative medicine. In parallel, computational modelling groups realized the potential of this culture system to support their theoretical approaches to study tissues in silico. These groups developed computational organoid models (COMs) that enabled testing consistency between cell biological data and developing theories of tissue self-organization. The models supported a mechanistic understanding of organoid growth and maturation and helped linking cell mechanics and tissue shape in general. What comes next? Can we use COMs as tools to complement the equipment of our biological and medical research? While these models already support experimental design, can they also quantitatively predict tissue behavior? Here, we review the current state of the art of COMs and discuss perspectives for their application.
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Affiliation(s)
- Torsten Thalheim
- Interdisciplinary Institute for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
- Correspondence:
| | - Gabriela Aust
- Department of Surgery, Research Laboratories, Leipzig University, Liebigstraße 20, 04103 Leipzig, Germany
| | - Joerg Galle
- Interdisciplinary Institute for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
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Sivakumar N, Warner HV, Peirce SM, Lazzara MJ. A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion. PLoS Comput Biol 2022; 18:e1010701. [PMID: 36441822 PMCID: PMC9747056 DOI: 10.1371/journal.pcbi.1010701] [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: 10/08/2021] [Revised: 12/13/2022] [Accepted: 11/01/2022] [Indexed: 11/29/2022] Open
Abstract
Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.
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Affiliation(s)
- Nikita Sivakumar
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Helen V. Warner
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Shayn M. Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Matthew J. Lazzara
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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Shou Y, Johnson SC, Quek YJ, Li X, Tay A. Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system. Mater Today Bio 2022; 14:100269. [PMID: 35514433 PMCID: PMC9062348 DOI: 10.1016/j.mtbio.2022.100269] [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: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.
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Key Words
- ABM, agent-based model
- APC, antigen-presenting cell
- BV, blood vessel
- Biomaterials
- CPM, Cellular Potts model
- Computational models
- DC, dendritic cell
- ECM, extracellular matrix
- FDC, follicular dendritic cell
- FRC, fibroblastic reticular cell
- Immunotherapy
- LEC, lymphatic endothelial cell
- LN, lymph node
- LV, lymphatic vessel
- Lymph node
- Lymphatic system
- ODE, ordinary differential equation
- PDE, partial differential equation
- PDMS, polydimethylsiloxane
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Affiliation(s)
- Yufeng Shou
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
| | - Sarah C. Johnson
- Department of Bioengineering, Stanford University, CA, 94305, USA
- Department of Bioengineering, Imperial College London, South Kensington, SW72AZ, UK
| | - Ying Jie Quek
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- Singapore Immunology Network, Agency for Science, Technology and Research, 138648, Singapore
| | - Xianlei Li
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
| | - Andy Tay
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, 117599, Singapore
- NUS Tissue Engineering Program, National University of Singapore, 117510, Singapore
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Covert MW, Gillies TE, Kudo T, Agmon E. A forecast for large-scale, predictive biology: Lessons from meteorology. Cell Syst 2021; 12:488-496. [PMID: 34139161 PMCID: PMC8217727 DOI: 10.1016/j.cels.2021.05.014] [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: 12/16/2020] [Revised: 04/01/2021] [Accepted: 05/18/2021] [Indexed: 11/19/2022]
Abstract
Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.
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Affiliation(s)
- Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Takamasa Kudo
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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Zheng F, Xiao Y, Liu H, Fan Y, Dao M. Patient-Specific Organoid and Organ-on-a-Chip: 3D Cell-Culture Meets 3D Printing and Numerical Simulation. Adv Biol (Weinh) 2021; 5:e2000024. [PMID: 33856745 PMCID: PMC8243895 DOI: 10.1002/adbi.202000024] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 02/13/2021] [Indexed: 12/11/2022]
Abstract
The last few decades have witnessed diversified in vitro models to recapitulate the architecture and function of living organs or tissues and contribute immensely to advances in life science. Two novel 3D cell culture models: 1) Organoid, promoted mainly by the developments of stem cell biology and 2) Organ-on-a-chip, enhanced primarily due to microfluidic technology, have emerged as two promising approaches to advance the understanding of basic biological principles and clinical treatments. This review describes the comparable distinct differences between these two models and provides more insights into their complementarity and integration to recognize their merits and limitations for applicable fields. The convergence of the two approaches to produce multi-organoid-on-a-chip or human organoid-on-a-chip is emerging as a new approach for building 3D models with higher physiological relevance. Furthermore, rapid advancements in 3D printing and numerical simulations, which facilitate the design, manufacture, and results-translation of 3D cell culture models, can also serve as novel tools to promote the development and propagation of organoid and organ-on-a-chip systems. Current technological challenges and limitations, as well as expert recommendations and future solutions to address the promising combinations by incorporating organoids, organ-on-a-chip, 3D printing, and numerical simulation, are also summarized.
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Affiliation(s)
- Fuyin Zheng
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yuminghao Xiao
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hui Liu
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Yubo Fan
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Ming Dao
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore
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Truskey GA, Fu J. The future of biomedical engineering: Bioengineering of organoids and tissue development. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2020. [DOI: 10.1016/j.cobme.2020.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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