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Nagornov IS, Kato M. tugHall: a simulator of cancer-cell evolution based on the hallmarks of cancer and tumor-related genes. Bioinformatics 2020; 36:3597-3599. [PMID: 32170925 PMCID: PMC7267821 DOI: 10.1093/bioinformatics/btaa182] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/09/2019] [Accepted: 03/11/2020] [Indexed: 12/20/2022] Open
Abstract
Summary The flood of recent cancer genomic data requires a coherent model that can sort out the findings to systematically explain clonal evolution and the resultant intra-tumor heterogeneity (ITH). Here, we present a new mathematical model designed to computationally simulate the evolution of cancer cells. The model connects the well-known hallmarks of cancer with the specific mutational states of tumor-related genes. The cell behavior phenotypes are stochastically determined, and the hallmarks probabilistically interfere with the phenotypic probabilities. In turn, the hallmark variables depend on the mutational states of tumor-related genes. Thus, our software can deepen our understanding of cancer-cell evolution and generation of ITH. Availability and implementation The open-source code is available in the repository https://github.com/nagornovys/Cancer_cell_evolution. Contact mamkato@ncc.go.jp Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iurii S Nagornov
- Department of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo 104-0045, Japan
| | - Mamoru Kato
- Department of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo 104-0045, Japan
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2
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Tomeu AJ, Salguero AG. A Lock Free Approach To Parallelize The Cellular Potts Model: Application To Ductal Carcinoma In Situ. J Integr Bioinform 2020; 17:jib-2019-0070. [PMID: 32267247 PMCID: PMC7734501 DOI: 10.1515/jib-2019-0070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 12/19/2019] [Indexed: 01/08/2023] Open
Abstract
In the field of computational biology, in order to simulate multiscale biological systems, the Cellular Potts Model (CPM) has been used, which determines the actions that simulated cells can perform by determining a hamiltonian of energy that takes into account the influence that neighboring cells exert, under a wide range of parameters. There are some proposals in the literature that parallelize the CPM; in all cases, either lock-based techniques or other techniques that require large amounts of information to be disseminated among parallel tasks are used to preserve data coherence. In both cases, computational performance is limited. This work proposes an alternative approach for the parallelization of the model that uses transactional memory to maintain the coherence of the information. A Java implementation has been applied to the simulation of the ductal adenocarcinoma of breast in situ (DCIS). Times and speedups of the simulated execution of the model on the cluster of our university are analyzed. The results show a good speedup.
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Affiliation(s)
- Antonio J Tomeu
- University of Cadiz, Computer Science, Escuela Superior de Ingeniería, Campus of Puerto RealPuerto Real, Spain.,University of Cadiz, Faculty of Engineering, Department of Computer Science, Puerto Real, Spain
| | - Alberto G Salguero
- University of Cadiz, Faculty of Engineering, Department of Computer Science, Puerto Real, Spain
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3
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Lehotzky D, Zupanc GKH. Cellular Automata Modeling of Stem-Cell-Driven Development of Tissue in the Nervous System. Dev Neurobiol 2019; 79:497-517. [PMID: 31102334 DOI: 10.1002/dneu.22686] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 04/23/2019] [Accepted: 05/02/2019] [Indexed: 12/12/2022]
Abstract
Mathematical and computational modeling enables biologists to integrate data from observations and experiments into a theoretical framework. In this review, we describe how developmental processes associated with stem-cell-driven growth of tissue in both the embryonic and adult nervous system can be modeled using cellular automata (CA). A cellular automaton is defined by its discrete nature in time, space, and state. The discrete space is represented by a uniform grid or lattice containing agents that interact with other agents within their local neighborhood. This possibility of local interactions of agents makes the cellular automata approach particularly well suited for studying through modeling how complex patterns at the tissue level emerge from fundamental developmental processes (such as proliferation, migration, differentiation, and death) at the single-cell level. As part of this review, we provide a primer for how to define biologically inspired rules governing these processes so that they can be implemented into a CA model. We then demonstrate the power of the CA approach by presenting simulations (in the form of figures and movies) based on building models of three developmental systems: the formation of the enteric nervous system through invasion by neural crest cells; the growth of normal and tumorous neurospheres induced by proliferation of adult neural stem/progenitor cells; and the neural fate specification through lateral inhibition of embryonic stem cells in the neurogenic region of Drosophila.
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Affiliation(s)
- Dávid Lehotzky
- Laboratory of Neurobiology, Department of Biology, Northeastern University, Boston, Massachusetts
| | - Günther K H Zupanc
- Laboratory of Neurobiology, Department of Biology, Northeastern University, Boston, Massachusetts
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Wali A, Saeed M. m-CALP - Yet another way of generating handwritten data through evolution for pattern recognition. Biosystems 2018; 175:24-29. [PMID: 30528517 DOI: 10.1016/j.biosystems.2018.11.007] [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: 07/10/2018] [Revised: 11/10/2018] [Accepted: 11/29/2018] [Indexed: 11/19/2022]
Abstract
Recently a cellular automata learning and prediction (CALP) model was proposed that used evolution to generate handwritten data for pattern recognition. However, the proposed method by CALP was mechanical and not intuitive, as it did not consider the degree of image deformation that takes place during evolution. The problem with this approach is that it evolved handwritten shapes to a completely different form. We propose a new data generation through evolution method called m-CALP, that evolves handwritten shapes using an objective function that also considers the degree of image deformation as it evolves. We replicated the exact same experimental setup of CALP - with the same 5 handwritten data sets, and classifiers with the exact same settings, and then used it to record the performance of our evolved data. We show that data evolved using our method maintains the interesting properties of a pattern. CALP was shown to have performed better than the state-of-the-art synthetic over sampling methods such as SMOTE and BORDERLINE-SMOTE, and m-CALP performs better than CALP in most cases, in the same environment. Data generation using evolution is a promising field, as it allows researchers to develop trainers and classifier for even small-sized training data. Therefore, we also show that evolving small-sized training data to generate more data using m-CALP performs better than both CALP and larger sized training data.
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Affiliation(s)
- Aamir Wali
- Department of Computer Science, FAST-NUCES, Faisal Town, Lahore, Pakistan.
| | - Mehreen Saeed
- Department of Computer Science, FAST-NUCES, Faisal Town, Lahore, Pakistan.
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5
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Reher D, Klink B, Deutsch A, Voss-Böhme A. Cell adhesion heterogeneity reinforces tumour cell dissemination: novel insights from a mathematical model. Biol Direct 2017; 12:18. [PMID: 28800767 PMCID: PMC5553611 DOI: 10.1186/s13062-017-0188-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 07/17/2017] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Cancer cell invasion, dissemination, and metastasis have been linked to an epithelial-mesenchymal transition (EMT) of individual tumour cells. During EMT, adhesion molecules like E-cadherin are downregulated and the decrease of cell-cell adhesion allows tumour cells to dissociate from the primary tumour mass. This complex process depends on intracellular cues that are subject to genetic and epigenetic variability, as well as extrinsic cues from the local environment resulting in a spatial heterogeneity in the adhesive phenotype of individual tumour cells. Here, we use a novel mathematical model to study how adhesion heterogeneity, influenced by intrinsic and extrinsic factors, affects the dissemination of tumour cells from an epithelial cell population. The model is a multiscale cellular automaton that couples intracellular adhesion receptor regulation with cell-cell adhesion. RESULTS Simulations of our mathematical model indicate profound effects of adhesion heterogeneity on tumour cell dissemination. In particular, we show that a large variation of intracellular adhesion receptor concentrations in a cell population reinforces cell dissemination, regardless of extrinsic cues mediated through the local cell density. However, additional control of adhesion receptor concentration through the local cell density, which can be assumed in healthy cells, weakens the effect. Furthermore, we provide evidence that adhesion heterogeneity can explain the remarkable differences in adhesion receptor concentrations of epithelial and mesenchymal phenotypes observed during EMT and might drive early dissemination of tumour cells. CONCLUSIONS Our results suggest that adhesion heterogeneity may be a universal trigger to reinforce cell dissemination in epithelial cell populations. This effect can be at least partially compensated by a control of adhesion receptor regulation through neighbouring cells. Accordingly, our findings explain how both an increase in intra-tumour adhesion heterogeneity and the loss of control through the local environment can promote tumour cell dissemination. REVIEWERS This article was reviewed by Hanspeter Herzel, Thomas Dandekar and Marek Kimmel.
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Affiliation(s)
- David Reher
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig, 04103, Germany.
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany.
| | - Barbara Klink
- Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, Dresden, 01307, Germany
- German Cancer Consortium (DKTK), Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Center for Molecular Tumor Diagnostics (CMTD), National Center for Tumor Diseases (NCT), Dresden, Germany
| | - Andreas Deutsch
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany
| | - Anja Voss-Böhme
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany
- Hochschule für Technik und Wirtschaft Dresden, Fakultät Informatik/Mathematik, Friedrich-List-Platz 1, Dresden, 01069, Germany
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6
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Gadkar K, Kirouac DC, Mager DE, van der Graaf PH, Ramanujan S. A Six-Stage Workflow for Robust Application of Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:235-49. [PMID: 27299936 PMCID: PMC4879472 DOI: 10.1002/psp4.12071] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 02/18/2016] [Indexed: 12/30/2022]
Abstract
Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.
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Affiliation(s)
- K Gadkar
- Translational & Systems Pharmacology, PKPD, Genentech, South San Francisco, California, USA
| | - D C Kirouac
- Translational & Systems Pharmacology, PKPD, Genentech, South San Francisco, California, USA
| | - D E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - P H van der Graaf
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
| | - S Ramanujan
- Translational & Systems Pharmacology, PKPD, Genentech, South San Francisco, California, USA
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Marcu LG, Marcu D, Filip SM. In silico study of the impact of cancer stem cell dynamics and radiobiological hypoxia on tumour response to hyperfractionated radiotherapy. Cell Prolif 2016; 49:304-14. [PMID: 27079860 DOI: 10.1111/cpr.12251] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Accepted: 02/10/2016] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVES Advanced head and neck carcinomas (HNCs) are aggressive tumours, mainly due to hypoxia and a cancer stem cell (CSC) subpopulation. The aim of this study was to simulate tumour growth and behaviour during radiotherapy of three HNC groups (governed by different growth kinetics, hypoxia levels and CSC division pattern) to determine correlation between resistance factors and responses to hyperfractionated radiotherapy. METHODS An in silico HNC model was developed based on biologically realistic input parameters. During radiotherapy simulation, three parameters were studied: growth kinetics, hypoxia and probability of CSC symmetrical division. Both independent and combined effects on tumour response to hyperfractionated radiotherapy were assessed. RESULTS Oxic and very mildly hypoxic HNCs were revealed to be controlled by hyperfractionated radiotherapy, irrespective of growth kinetics and CSC division pattern. Moderately hypoxic tumours had different responses to radiotherapy: while slowly proliferating HNCs were still controllable, tumours with higher cell turnover were more resistant. In rapidly proliferating tumours, the number of fractions needed for tumour control increased exponentially with the probability of CSC symmetrical division, whereas in moderately growing HNC, this behaviour was linear. Severely hypoxic tumours could not be controlled by radiotherapy alone. Tumours with CSCs in a severely hypoxic niche required adjuvant therapies to be eradicated. CONCLUSIONS Growth kinetics strongly influence tumour responses to treatment. Slowly growing tumours showed linear dependence between dose and hypoxia/CSC, whereas rapidly growing tumours followed exponential behaviour.
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Affiliation(s)
- L G Marcu
- Faculty of Science, University of Oradea, Oradea, 410087, Romania.,School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - D Marcu
- Faculty of Science, University of Oradea, Oradea, 410087, Romania
| | - S M Filip
- Faculty of Science, University of Oradea, Oradea, 410087, Romania
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Santos J, Monteagudo Á. Analysis of behaviour transitions in tumour growth using a cellular automaton simulation. IET Syst Biol 2015; 9:75-87. [PMID: 26021328 DOI: 10.1049/iet-syb.2014.0015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The authors used computational biology as an approach for analysing the emergent dynamics of tumour growth at cellular level. They applied cellular automata for modelling the behaviour of cells when the main cancer cell hallmarks are present. Their model is oriented to mimic the development of multicellular spheroids of tumour cells. In their modelling, cells have a genome associated with the different cancer hallmarks, indicating if those are acquired as a consequence of mutations. The presence of the cancer hallmarks defines cell states and cell mitotic behaviours. These hallmarks are associated with a series of parameters, and depending on their values and the activation of the hallmarks in each of the cells, the system can evolve to different dynamics. With the simulation tool the authors performed an analysis of the first phases of cancer growth, using different and alternative strategies: firstly, studying the evolution of cancer cells and hallmarks in different representative situations regarding initial conditions and parameters, analysing the relative importance of the hallmarks for tumour progression; secondly, being the focus of this work, inspecting the behaviour transitions when the cancer cells are killed with a given probability during the cellular system progression.
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Affiliation(s)
- José Santos
- Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.
| | - Ángel Monteagudo
- Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
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Monteagudo Á, Santos J. Treatment Analysis in a Cancer Stem Cell Context Using a Tumor Growth Model Based on Cellular Automata. PLoS One 2015; 10:e0132306. [PMID: 26176702 PMCID: PMC4503350 DOI: 10.1371/journal.pone.0132306] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 06/11/2015] [Indexed: 12/31/2022] Open
Abstract
Cancer can be viewed as an emergent behavior in terms of complex system theory and artificial life, Cellular Automata (CA) being the tool most used for studying and characterizing the emergent behavior. Different approaches with CA models were used to model cancer growth. The use of the abstract model of acquired cancer hallmarks permits the direct modeling at cellular level, where a cellular automaton defines the mitotic and apoptotic behavior of cells, and allows for an analysis of different dynamics of the cellular system depending on the presence of the different hallmarks. A CA model based on the presence of hallmarks in the cells, which includes a simulation of the behavior of Cancer Stem Cells (CSC) and their implications for the resultant growth behavior of the multicellular system, was employed. This modeling of cancer growth, in the avascular phase, was employed to analyze the effect of cancer treatments in a cancer stem cell context. The model clearly explains why, after treatment against non-stem cancer cells, the regrowth capability of CSCs generates a faster regrowth of tumor behavior, and also shows that a continuous low-intensity treatment does not favor CSC proliferation and differentiation, thereby allowing an unproblematic control of future tumor regrowth. The analysis performed indicates that, contrary to the current attempts at CSC control, trying to make CSC proliferation more difficult is an important point to consider, especially in the immediate period after a standard treatment for controlling non-stem cancer cell proliferation.
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Affiliation(s)
- Ángel Monteagudo
- Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
| | - José Santos
- Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
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Day RS. What Tumor Dynamics Modeling Can Teach us About Exploiting the Stem-Cell View for Better Cancer Treatment. Cancer Inform 2015; 14:25-36. [PMID: 25780337 PMCID: PMC4345852 DOI: 10.4137/cin.s17294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Revised: 01/19/2015] [Accepted: 01/22/2015] [Indexed: 12/26/2022] Open
Abstract
The cancer stem cell hypothesis is that in human solid cancers, only a small proportion of the cells, the cancer stem cells (CSCs), are self-renewing; the vast majority of the cancer cells are unable to sustain tumor growth indefinitely on their own. In recent years, discoveries have led to the concentration, if not isolation, of putative CSCs. The evidence has mounted that CSCs do exist and are important. This knowledge may promote better understanding of treatment resistance, create opportunities to test agents against CSCs, and open up promise for a fresh approach to cancer treatment. The first clinical trials of new anti-CSC agents are completed, and many others follow. Excitement is mounting that this knowledge will lead to major improvements, even breakthroughs, in treating cancer. However, exploitation of this phenomenon may be more successful if informed by insights into the population dynamics of tumor development. We revive some ideas in tumor dynamics modeling to extract some guidance in designing anti-CSC treatment regimens and the clinical trials that test them.
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Affiliation(s)
- Roger S Day
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
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