Prediction and validation of avascular tumor growth pattern in different metabolic conditions using in silico and in vitro models.
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| Title: | Prediction and validation of avascular tumor growth pattern in different metabolic conditions using in silico and in vitro models. |
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| Authors: | Heidari, Mahshid1 (AUTHOR) mahshid.heidari@ut.ac.ir, Kabiri, Mahboubeh1 (AUTHOR) mkabiri@ut.ac.ir |
| Source: | Journal of Bioinformatics & Computational Biology. Oct2021, Vol. 19 Issue 5, p1-12. 12p. |
| Subjects: | Tumor growth, Propidium iodide, Metabolic models, Cell growth, Prediction models, Multiscale modeling, Breast cancer prognosis |
| Abstract: | Objectives: In recent years, scientists have taken many efforts for in vitro and in silico modeling of cancerous tumors. In fact, three-dimensional (3D) cultures of multicellular tumor spheroids (MCTSs) are good validators for computational results. The goal of this study is to simulate the 3D early growth of avascular tumors using MCTSs and to compare the in vitro models with the results and predictions of a specific computational modeling framework. Using these two types of models, the importance of metabolic condition on tumor growth behavior and necrosis could be predicted. Materials and methods: We took advantage of a previously developed computational model of tumor growth (constructed by integrating a generic metabolic network model of cancer cells with a multiscale agent-based framework). Among the computational predictions is the importance of glucose accessibility on tumor growth behavior. To study the effect of glucose concentration experimentally, MCTSs were grown in high and low glucose culture media. After that, tumor growth pattern was analyzed by MTT assay, cell counting and propidium iodide (PI) staining. Results: We obviously observed that the rate of necrosis increases and the rate of tumor growth and cell activity decreases as the glucose availability reduces, which is in line with the computational model prediction. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Bioinformatics & Computational Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 153534384 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction and validation of avascular tumor growth pattern in different metabolic conditions using in silico and in vitro models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Heidari%2C+Mahshid%22">Heidari, Mahshid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mahshid.heidari@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Kabiri%2C+Mahboubeh%22">Kabiri, Mahboubeh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mkabiri@ut.ac.ir</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Bioinformatics+%26+Computational+Biology%22">Journal of Bioinformatics & Computational Biology</searchLink>. Oct2021, Vol. 19 Issue 5, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Tumor+growth%22">Tumor growth</searchLink><br /><searchLink fieldCode="DE" term="%22Propidium+iodide%22">Propidium iodide</searchLink><br /><searchLink fieldCode="DE" term="%22Metabolic+models%22">Metabolic models</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+growth%22">Cell growth</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+cancer+prognosis%22">Breast cancer prognosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: In recent years, scientists have taken many efforts for in vitro and in silico modeling of cancerous tumors. In fact, three-dimensional (3D) cultures of multicellular tumor spheroids (MCTSs) are good validators for computational results. The goal of this study is to simulate the 3D early growth of avascular tumors using MCTSs and to compare the in vitro models with the results and predictions of a specific computational modeling framework. Using these two types of models, the importance of metabolic condition on tumor growth behavior and necrosis could be predicted. Materials and methods: We took advantage of a previously developed computational model of tumor growth (constructed by integrating a generic metabolic network model of cancer cells with a multiscale agent-based framework). Among the computational predictions is the importance of glucose accessibility on tumor growth behavior. To study the effect of glucose concentration experimentally, MCTSs were grown in high and low glucose culture media. After that, tumor growth pattern was analyzed by MTT assay, cell counting and propidium iodide (PI) staining. Results: We obviously observed that the rate of necrosis increases and the rate of tumor growth and cell activity decreases as the glucose availability reduces, which is in line with the computational model prediction. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Bioinformatics & Computational Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0219720021500244 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Tumor growth Type: general – SubjectFull: Propidium iodide Type: general – SubjectFull: Metabolic models Type: general – SubjectFull: Cell growth Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Multiscale modeling Type: general – SubjectFull: Breast cancer prognosis Type: general Titles: – TitleFull: Prediction and validation of avascular tumor growth pattern in different metabolic conditions using in silico and in vitro models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Heidari, Mahshid – PersonEntity: Name: NameFull: Kabiri, Mahboubeh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 02197200 Numbering: – Type: volume Value: 19 – Type: issue Value: 5 Titles: – TitleFull: Journal of Bioinformatics & Computational Biology Type: main |
| ResultId | 1 |
