The Impact of Phenotypic Switching on Glioblastoma Growth and Invasion
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{"title"=>"The impact of phenotypic switching on glioblastoma growth and invasion", "type"=>"journal", "authors"=>[{"first_name"=>"Philip", "last_name"=>"Gerlee", "scopus_author_id"=>"25121071600"}, {"first_name"=>"Sven", "last_name"=>"Nelander", "scopus_author_id"=>"24329614700"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84864044363", "pui"=>"365284180", "pmid"=>"22719241", "doi"=>"10.1371/journal.pcbi.1002556", "sgr"=>"84864044363", "isbn"=>"9783319037585", "issn"=>"1553734X"}, "id"=>"628c3999-d866-346c-9c2e-4e71d19a033d", "abstract"=>"In this work, we develop a spatial mathematical model in order to analyse the growth behavior of the brain tumour glioblastoma. Tumours of this type have a diffuse boundary, with considerable local invasion of surrounding brain tissue, making surgery difficult. At the cellular level, the progression of a glioblastoma is known to depend on the balance between cell division (proliferation) and cell movement (migration). Based on recent evidence, our model assumes that each cell in a glioblastoma tumour resides in either of two mutually exclusive states: proliferating or migrating. From a probabilistic model of switching between these two phenotypes, we go on to derive equations that link cellular phenotypes to disease progression. The model has several possible applications. For instance, it could be used to predict the rate of disease progression in an individual patient, and to improve screening methods.", "link"=>"http://www.mendeley.com/research/impact-phenotypic-switching-glioblastoma-growth-invasion", "reader_count"=>45, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>3, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>3, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Materials Science"=>1, "Mathematics"=>5, "Agricultural and Biological Sciences"=>16, "Medicine and Dentistry"=>12, "Neuroscience"=>1, "Physics and Astronomy"=>4, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>12}, "Neuroscience"=>{"Neuroscience"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>5}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>1, "Brazil"=>1, "Germany"=>2}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/623805"], "description"=>"<p>The wave speed of the propagating tumour margin as a function of (a) , (b) and (c) . The phenotypic switching rates were fixed at . The dashed line in the inset of (c) has slope 1/2 and shows that .</p>", "links"=>[], "tags"=>["Computational biology", "oncology", "mathematics"], "article_id"=>294296, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g007", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_model_parameters_/294296", "title"=>"Impact of model parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:53:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/623457"], "description"=>"<p>Simulation results for and . (a) The result of a single realisation where P-cells are coloured blue and M-cells are coloured red. (b) The occupancy probability of finding a cell at location obtained by averaging over a large number of simulations. (c) A slice through the function in panel (b) at .</p>", "links"=>[], "tags"=>["individual-based"], "article_id"=>293955, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulating_the_individual_based_model_/293955", "title"=>"Simulating the individual-based model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:51:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/623738"], "description"=>"<p>The wave speed of the propagating tumour margin determined from both the individual-based model (dashed line) and phase space analysis of the continuum approximation (solid line). In (a) the switch rate to proliferation is fixed at , while in (b) we have fixed .</p>", "links"=>[], "tags"=>["ib-model", "analytical"], "article_id"=>294229, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_IB_model_and_analytical_result_/294229", "title"=>"Comparison between IB-model and analytical result.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:53:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/623382"], "description"=>"<p>A living glioma cell can be in either of two states, proliferating (P) or migrating (M), and transitions between the states with rates and respectively. A P-cell divides at rate while an M-cell moves with rate . Both cell types go into apoptosis and die with a constant rate .</p>", "links"=>[], "tags"=>["describing", "markov"], "article_id"=>293879, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_describing_the_continuous_time_Markov_process_each_cell_is_subject_to_/293879", "title"=>"Schematic describing the continuous time Markov process each cell is subject to.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:51:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/623605"], "description"=>"<p>Solutions of the 1-dimensional continuum model (equation (3) and (4)) for three different values of the switching rates at (black) and (red). The initial condition was set to , and . In (a) , (b) and in (c) . All solutions exhibit similar characteristics with an invading front of cancer cells stretching into the healthy tissue, similar to the solutions of the Fisher equation (1). The similarity between the solutions at the two different time points clearly shows that our system exhibits travelling wave solutions.</p>", "links"=>[], "tags"=>["solutions", "continuum"], "article_id"=>294104, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Numerical_solutions_of_the_continuum_model_/294104", "title"=>"Numerical solutions of the continuum model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:52:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/623536"], "description"=>"<p>(a) The tumour mass at for the 2-dimensional model as a function of the phenotypic switching rates (the rate at which cells become proliferative) and (the rate at which they become motile). (b) The tumour mass at for the 3-dimensional as a function of and . The results in 2 and 3 dimensions are similar, although a larger variability seems to exists in the 3-dimensional case.</p>", "links"=>[], "tags"=>["phenotypic", "switching", "rates", "tumour"], "article_id"=>294027, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_impact_of_phenotypic_switching_rates_on_tumour_mass_/294027", "title"=>"The impact of phenotypic switching rates on tumour mass.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:52:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/623685"], "description"=>"<p>The wave speed of the propagating tumour margin determined from both phase space analysis (solid line) and numerical simulation (dashed line). In (a) the switch rate to proliferation is fixed at , while in (b) we have fixed .</p>", "links"=>[], "tags"=>["continuum", "analytical"], "article_id"=>294172, "categories"=>["Cancer", "Mathematics", "Biological Sciences"], "users"=>["Philip Gerlee", "Sven Nelander"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002556.g005", "stats"=>{"downloads"=>2, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_continuum_model_and_analytical_result_/294172", "title"=>"Comparison between continuum model and analytical result.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 03:52:57"}

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Relative Metric

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