Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making
Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-06-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00575/full |
| _version_ | 1811324737876393984 |
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| author | Marilisa Cortesi Alice Pasini Simone Furini Emanuele Giordano Emanuele Giordano Emanuele Giordano |
| author_facet | Marilisa Cortesi Alice Pasini Simone Furini Emanuele Giordano Emanuele Giordano Emanuele Giordano |
| author_sort | Marilisa Cortesi |
| collection | DOAJ |
| description | Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers. |
| first_indexed | 2024-04-13T14:20:55Z |
| format | Article |
| id | doaj.art-5e6e198555a945d2834a55f95100aadc |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T14:20:55Z |
| publishDate | 2019-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-5e6e198555a945d2834a55f95100aadc2022-12-22T02:43:29ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-06-011010.3389/fgene.2019.00575438524Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision MakingMarilisa Cortesi0Alice Pasini1Simone Furini2Emanuele Giordano3Emanuele Giordano4Emanuele Giordano5Laboratory of Cellular and Molecular Engineering “S. Cavalcanti”, Department of Electrical, Electronic and Information Engineering “G. Marconi” (DEI), Alma Mater Studiorum—University of Bologna, Bologna, ItalyLaboratory of Cellular and Molecular Engineering “S. Cavalcanti”, Department of Electrical, Electronic and Information Engineering “G. Marconi” (DEI), Alma Mater Studiorum—University of Bologna, Bologna, ItalyDepartment of Medical Biotechnologies, University of Siena, Siena, ItalyLaboratory of Cellular and Molecular Engineering “S. Cavalcanti”, Department of Electrical, Electronic and Information Engineering “G. Marconi” (DEI), Alma Mater Studiorum—University of Bologna, Bologna, ItalyBioEngLab, Health Science and Technology, Interdepartmental Center for Industrial Research (HST-CIRI), Alma Mater Studiorum—University of Bologna, Bologna, ItalyAdvanced Research Center on Electronic Systems (ARCES), Alma Mater Studiorum—University of Bologna, Bologna, ItalyComplex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.https://www.frontiersin.org/article/10.3389/fgene.2019.00575/fullcomputational modelingepithelial-mesenchymal transitioncell decisionboolean modelmarkov model |
| spellingShingle | Marilisa Cortesi Alice Pasini Simone Furini Emanuele Giordano Emanuele Giordano Emanuele Giordano Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making Frontiers in Genetics computational modeling epithelial-mesenchymal transition cell decision boolean model markov model |
| title | Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making |
| title_full | Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making |
| title_fullStr | Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making |
| title_full_unstemmed | Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making |
| title_short | Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making |
| title_sort | identification via numerical computation of transcriptional determinants of a cell phenotype decision making |
| topic | computational modeling epithelial-mesenchymal transition cell decision boolean model markov model |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00575/full |
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