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Buttenschön A, Edelstein-Keshet L. Bridging from single to collective cell migration: A review of models and links to experiments. PLoS Comput Biol 2020; 16:e1008411. [PMID: 33301528 PMCID: PMC7728230 DOI: 10.1371/journal.pcbi.1008411] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Mathematical and computational models can assist in gaining an understanding of cell behavior at many levels of organization. Here, we review models in the literature that focus on eukaryotic cell motility at 3 size scales: intracellular signaling that regulates cell shape and movement, single cell motility, and collective cell behavior from a few cells to tissues. We survey recent literature to summarize distinct computational methods (phase-field, polygonal, Cellular Potts, and spherical cells). We discuss models that bridge between levels of organization, and describe levels of detail, both biochemical and geometric, included in the models. We also highlight links between models and experiments. We find that models that span the 3 levels are still in the minority.
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Affiliation(s)
- Andreas Buttenschön
- Department of Mathematics, University of British Columbia, Vancouver, Canada
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Varela PL, Ramos CV, Monteiro PT, Chaouiya C. EpiLog: A software for the logical modelling of epithelial dynamics. F1000Res 2018; 7:1145. [PMID: 30363398 PMCID: PMC6173114 DOI: 10.12688/f1000research.15613.2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/01/2019] [Indexed: 01/12/2023] Open
Abstract
Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.
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Affiliation(s)
- Pedro L Varela
- Instituto Superior Técnico, Universidade de Lisboa, Lisbon, P-1049-001, Portugal.,INESC-ID, Lisbon, P-1000-029, Portugal.,Instituto Gulbenkian de Ciência, Oeiras, P-2780-156, Portugal
| | - Camila V Ramos
- INESC-ID, Lisbon, P-1000-029, Portugal.,Instituto Gulbenkian de Ciência, Oeiras, P-2780-156, Portugal
| | - Pedro T Monteiro
- Instituto Superior Técnico, Universidade de Lisboa, Lisbon, P-1049-001, Portugal.,INESC-ID, Lisbon, P-1000-029, Portugal
| | - Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Oeiras, P-2780-156, Portugal.,CNRS, Centrale Marseille, l'Institut de Mathématiques de Marseille, Aix-Marseille University, Marseille, France
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Yankeelov TE, An G, Saut O, Luebeck EG, Popel AS, Ribba B, Vicini P, Zhou X, Weis JA, Ye K, Genin GM. Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success. Ann Biomed Eng 2016; 44:2626-41. [PMID: 27384942 DOI: 10.1007/s10439-016-1691-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 06/29/2016] [Indexed: 12/11/2022]
Abstract
Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.
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Affiliation(s)
- Thomas E Yankeelov
- Departments of Biomedical Engineering and Internal Medicine, Institute for Computational and Engineering Sciences, Cockrell School of Engineering, The University of Texas at Austin, 107 W. Dean Keeton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA.
| | - Gary An
- Department of Surgery and Computation Institute, The University of Chicago, Chicago, IL, USA
| | - Oliver Saut
- Institut de Mathématiques de Bordeaux, Université de Bordeaux and INRIA, Bordeaux, France
| | - E Georg Luebeck
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Aleksander S Popel
- Departments of Biomedical Engineering and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin Ribba
- Pharma Research and Early Development, Clinical Pharmacology, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Paolo Vicini
- Clinical Pharmacology and DMPK, MedImmune, Gaithersburg, MD, USA
| | - Xiaobo Zhou
- Center for Bioinformatics and Systems Biology, Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiming Ye
- Department of Biomedical Engineering, Watson School of Engineering and Applied Science, Binghamton University, State University of New York, Binghamton, NY, USA
| | - Guy M Genin
- Departments of Mechanical Engineering and Materials Science, and Neurological Surgery, Washington University in St. Louis, St. Louis, MO, USA
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Paroni A, Graudenzi A, Caravagna G, Damiani C, Mauri G, Antoniotti M. CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks. BMC Bioinformatics 2016; 17:64. [PMID: 26846964 PMCID: PMC4743236 DOI: 10.1186/s12859-016-0914-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 01/27/2016] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Dynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development. Yet, the topological and functional characterization of real GRNs is often still partial and an exhaustive picture of their functioning is missing. RESULTS We here introduce CABERNET, a Cytoscape app for the generation, simulation and analysis of Boolean models of GRNs, specifically focused on their augmentation when a only partial topological and functional characterization of the network is available. By generating large ensembles of networks in which user-defined entities and relations are added to the original core, CABERNET allows to formulate hypotheses on the missing portions of real networks, as well to investigate their generic properties, in the spirit of complexity science. CONCLUSIONS CABERNET offers a series of innovative simulation and modeling functions and tools, including (but not being limited to) the dynamical characterization of the gene activation patterns ruling cell types and differentiation fates, and sophisticated robustness assessments, as in the case of gene knockouts. The integration within the widely used Cytoscape framework for the visualization and analysis of biological networks, makes CABERNET a new essential instrument for both the bioinformatician and the computational biologist, as well as a computational support for the experimentalist. An example application concerning the analysis of an augmented T-helper cell GRN is provided.
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Affiliation(s)
- Andrea Paroni
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy
| | - Alex Graudenzi
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Via F.lli Cervi, 93, Segrate, I-20090, (MI), Italy.
| | - Giulio Caravagna
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK.
| | - Chiara Damiani
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy, Viale Sarca 336, Milan, I-20126, Italy.
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Via F.lli Cervi, 93, Segrate, I-20090, (MI), Italy. .,SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy, Viale Sarca 336, Milan, I-20126, Italy.
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. .,Milan Center for Neuroscience, University of Milan-Bicocca, Milan, Italy.
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Li XL, Oduola WO, Qian L, Dougherty ER. Integrating Multiscale Modeling with Drug Effects for Cancer Treatment. Cancer Inform 2016; 14:21-31. [PMID: 26792977 PMCID: PMC4712979 DOI: 10.4137/cin.s30797] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 11/08/2015] [Accepted: 11/15/2015] [Indexed: 12/12/2022] Open
Abstract
In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.
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Affiliation(s)
- Xiangfang L. Li
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Wasiu O. Oduola
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Lijun Qian
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Edward R. Dougherty
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
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