• Computational Tools and In-Silico Models to Identify Transcriptional Determinants of Cell Phenotype Decision Making
  • Cortesi, Marilisa <1987>


  • ING-INF/06 Bioingegneria elettronica e informatica


  • The study of complex biological processes has significantly benefited from recent technological advancements and the increasing integration between experimental and computational approaches. In the following this combined approach will be applied to the study of gene expression and specifically to the identification of transcriptional determinants in a phenotypic regulation process involved in cell fate determination, the Epithelial to Mesenchymal transition (EMT). After the complete description of the technical aspects of the development of the model, its results and validation against experimental data are presented. In the last part of this thesis a series of software tools are presented. They can be used to analyse experimental data and either inform a computational representation of a process of interest (e.g. parameters identification) or improve the accuracy and reliability of a number of widely used in-vitro techniques. Two of them focus on gene expression quantification at single-cell level, from images acquired with an optical microscope. The ability of these instruments to identify the signal emitted by single-cells makes them able to identify the parameters of the computational model describing the process of interest. Another set of tools presented in the following, analyses one of the major consequences of EMT induction in a cell population, i.e. an augmented invasiveness. Two different experimental assays widely used to quantify this characteristic are considered and software tools that improve their reliability and accuracy are developed and characterized. Overall this thesis contributes to the increasingly applied approach that integrates in-vitro and in-silico techniques when studying biological processes. Indeed it includes a computational representation of a significant example of a phenotype regulation process, built entirely from freely available data and a collection of tools that could be used to analyse experimental data and reliably quantify their results.


  • 2017-05-12


  • Doctoral Thesis
  • PeerReviewed


  • application/pdf



Cortesi, Marilisa (2017) Computational Tools and In-Silico Models to Identify Transcriptional Determinants of Cell Phenotype Decision Making, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Bioingegneria , 29 Ciclo. DOI 10.6092/unibo/amsdottorato/8104.