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{"title"=>"A Multi-Paradigm Modeling Framework to Simulate Dynamic Reciprocity in a Bioreactor", "type"=>"journal", "authors"=>[{"first_name"=>"Himanshu", "last_name"=>"Kaul", "scopus_author_id"=>"55635298800"}, {"first_name"=>"Zhanfeng", "last_name"=>"Cui", "scopus_author_id"=>"7202504467"}, {"first_name"=>"Yiannis", "last_name"=>"Ventikos", "scopus_author_id"=>"6603704133"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "pui"=>"368622501", "doi"=>"10.1371/journal.pone.0059671", "scopus"=>"2-s2.0-84875629075", "sgr"=>"84875629075", "issn"=>"19326203", "pmid"=>"23555740"}, "id"=>"283eb720-3f36-30bf-9493-997ff0475d9a", "abstract"=>"Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications.", "link"=>"http://www.mendeley.com/research/multiparadigm-modeling-framework-simulate-dynamic-reciprocity-bioreactor", "reader_count"=>46, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>12, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>12, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>21, "Unspecified"=>2, "Environmental Science"=>6, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>5, "Medicine and Dentistry"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Chemical Engineering"=>1, "Chemistry"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>21}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>6}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Zambia"=>1, "Switzerland"=>1, "Portugal"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>4}

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• http://doi.org/10.1186/s12866-019-1468-9
• http://doi.org/10.1016/j.plrev.2013.05.004
• http://doi.org/10.1016/j.morpho.2019.10.042
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• http://doi.org/10.1016/j.ifacol.2015.09.140
• http://doi.org/10.1016/j.biosystems.2016.06.002
• http://doi.org/10.1016/j.jbiomech.2016.10.042
• http://doi.org/10.1093/bib/bbt077
• http://doi.org/10.1007/s12195-014-0363-6
• http://doi.org/10.1016/j.jtbi.2015.01.016
• http://doi.org/10.1111/jcpe.12326
• http://doi.org/10.1371/journal.pone.0154610
• http://doi.org/10.1016/j.coisb.2017.09.003
• http://doi.org/10.1063/1.5039473
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• http://doi.org/10.1016/j.cjche.2015.11.017
• http://doi.org/10.1177/2041731418787141

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1002246"], "description"=>"<p>Temporal evolution of cell population and nutrient concentration inside a bioreactor (geometry A) with a medium inlet velocity of 0.01 m/s. The final frame captures cell distribution at the end of 4 (physical) days – the time interval between snapshots (left to right) is 12.5 hours. The initial cell density was 100. The concentration contours can be observed to change continuously throughout the simulation. This is in contrast with physical systems with no cells inside where such behavior would not be possible after the flow becomes stationary beyond initial transients. This demonstrates the platform’s ability capture dynamic reciprocity.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>663456, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.g003", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Case_2_results_/663456", "title"=>"Case 2 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-30 07:11:54"}
• {"files"=>["https://ndownloader.figshare.com/files/1002244"], "description"=>"<p>Temporal evolution of cell population and nutrient concentration inside a 3D scaffold bioreactor (geometry A) with a medium inlet velocity of 0.001 m/s. The top-left port on the bioreactor serves as the inlet whereas the bottom-right port serves as the outlet. The final frame captures cell distribution at the end of 4 (physical) days – the time interval between snapshots (left to right) is 12.5 hours. The initial cell density was 100.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>663454, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.g002", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Case_1_results_/663454", "title"=>"Case 1 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-30 07:11:09"}
• {"files"=>["https://ndownloader.figshare.com/files/1002253"], "description"=>"<p>The figure shows temporal evolution of cell population and nutrient concentration in the same bioreactor set at different medium inlet velocities; 0.001 m/s (top) and 0.01 m/s (bottom). The bioreactor on the right ends up with considerably higher number of cells and a distinct growth pattern. This displays the dynamic nature of the system and the dependence of the spatiotemporal evolution of the system on processes such as chemotaxis and apoptosis. The frames were recorded at 5.5 days.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>663463, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.g005", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Different_boundary_conditions_lead_to_different_output_/663463", "title"=>"Different boundary conditions lead to different output.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-30 07:13:43"}
• {"files"=>["https://ndownloader.figshare.com/files/1002248"], "description"=>"<p>The figure shows migration response of MV3 melanoma cells based on FBS concentration gradient. The cells, displaying persistent random walk in the absence of FBS gradient, resort to chemotaxis on sensing FBS concentration. These results, when compared to similarly acquired ones but in the absence of the chemoattractant, confirm the capability of the simulation platform to capture such behaviors. The time interval between snapshots (left to right) is 1 hour. The final frame captures cell distribution at the end of 25 (physical) hours.</p>", "links"=>[], "tags"=>["validation"], "article_id"=>663458, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.g006", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_Validation_of_the_platform_/663458", "title"=>"Experimental Validation of the platform.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-30 07:12:23"}
• {"files"=>["https://ndownloader.figshare.com/files/1008639"], "description"=>"<p>The table lists various volume, boundary, and initial conditions applied to compute mass transport inside the bioreactors. The dynamic relationship between cell proliferation and mass transport of oxygen was investigated in two bioreactors of same volume but different geometries (shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059671#pone-0059671-g002\" target=\"_blank\">figures 2</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059671#pone-0059671-g004\" target=\"_blank\">4</a>). Oxygenated medium was introduced at two different velocities: 0.001 m/s and 0.01 m/s.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>669263, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bioreactor_Variables_/669263", "title"=>"Bioreactor Variables.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-29 02:34:23"}
• {"files"=>["https://ndownloader.figshare.com/files/1002275", "https://ndownloader.figshare.com/files/1002276", "https://ndownloader.figshare.com/files/1002277", "https://ndownloader.figshare.com/files/1002278", "https://ndownloader.figshare.com/files/1002280", "https://ndownloader.figshare.com/files/1002281"], "description"=>"<div><p>Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications.</p> </div>", "links"=>[], "tags"=>["multi-paradigm", "modeling", "simulate", "reciprocity"], "article_id"=>663485, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0059671.s001", "https://dx.doi.org/10.1371/journal.pone.0059671.s002", "https://dx.doi.org/10.1371/journal.pone.0059671.s003", "https://dx.doi.org/10.1371/journal.pone.0059671.s004", "https://dx.doi.org/10.1371/journal.pone.0059671.s005", "https://dx.doi.org/10.1371/journal.pone.0059671.s006"], "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Multi_Paradigm_Modeling_Framework_to_Simulate_Dynamic_Reciprocity_in_a_Bioreactor_/663485", "title"=>"A Multi-Paradigm Modeling Framework to Simulate Dynamic Reciprocity in a Bioreactor", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-03-30 07:22:58"}
• {"files"=>["https://ndownloader.figshare.com/files/1002242"], "description"=>"<p>The figure shows the communication between the transport-phenomena and agent-based modules that is at the heart of the modeling platform. Information relevant to bioreactor hydrodynamics and mass transport is communicated from the transport-phenomena module to the agent-based module where cells, modeled as agents, detect the local concentrations (and other continuum variables) and act based on the rules attributed to them. The cellular information is then relayed back to the transport-phenomena module to complete the circuit.</p>", "links"=>[], "tags"=>["modeling"], "article_id"=>663452, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flow_of_Information_in_the_modeling_framework_/663452", "title"=>"Flow of Information in the modeling framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-30 07:10:29"}
• {"files"=>["https://ndownloader.figshare.com/files/1008619"], "description"=>"<p>The table summarizes the rules used to simulate cellular dynamics.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>669241, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.t004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rules_/669241", "title"=>"Rules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-29 02:34:01"}
• {"files"=>["https://ndownloader.figshare.com/files/1002251"], "description"=>"<p>The figure shows temporal evolution of cell population and nutrient concentration inside a bioreactor (geometry B) with a medium inlet velocity of 0.001 m/s. The top right end of the bioreactor serves as the inlet whereas the entire left as well as bottom ends of the bioreactor serve as outlet. The initial cell density was 5. The time interval between snapshots (left to right) is 20 hours. The final frame captures cell distribution at the end of 6 (physical) days.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>663461, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.g004", "stats"=>{"downloads"=>5, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Case_3_results_/663461", "title"=>"Case 3 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-30 07:13:13"}
• {"files"=>["https://ndownloader.figshare.com/files/1008658"], "description"=>"<p>The table lists various test cases simulated for the purposes of this investigation and relevant parameters, such as medium flow rate, and relevant figures and supplementary material.</p>", "links"=>[], "tags"=>["genetics and genomics", "chemistry", "Computational biology", "biotechnology", "computer science", "developmental biology", "mathematics"], "article_id"=>669281, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Test_Cases_/669281", "title"=>"Test Cases.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-29 02:34:41"}
• {"files"=>["https://ndownloader.figshare.com/files/1008675"], "description"=>"<p>The table lists parameters pertinent to the cells as well as rules the cells followed during the computation. For example, under normoxic condition cells displayed persistent random walk changing direction once every two hours. Cells would only stop if it is in contact with another cell or about to undergo mitosis. Furthermore, chemotaxis ensues if a cell experiences local oxygen concentration of less than 0.0672 mol m⁻³. Failure to move to a normoxic region within 15 hours since the inception of chemotaxis leads to hypoxia-induced apoptosis.</p>", "links"=>[], "tags"=>["variables"], "article_id"=>669298, "categories"=>["Biological Sciences", "Developmental Biology", "Information And Computing Sciences", "Mathematics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Himanshu Kaul", "Zhanfeng Cui", "Yiannis Ventikos"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059671.t003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cellular_Variables_and_Rules_/669298", "title"=>"Cellular Variables and Rules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-29 02:34:58"}

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Relative Metric

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F1000Prime