Fiore, Gianfranco (2015) Identification and control of gene networks in living cells. [Tesi di dottorato]
Preview |
Text
Fiore_Gianfranco_27.pdf Download (6MB) | Preview |
| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Identification and control of gene networks in living cells |
| Creators: | Creators Email Fiore, Gianfranco ing.gianfrancofiore@gmail.com |
| Date: | 30 March 2015 |
| Number of Pages: | 144 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Medicina Molecolare e Biotecnologie Mediche |
| Scuola di dottorato: | Biotecnologie |
| Dottorato: | Biologia computazionale e bioinformatica |
| Ciclo di dottorato: | 27 |
| Coordinatore del Corso di dottorato: | nome email Cocozza, Sergio sergio.cocozza@unina.it |
| Tutor: | nome email di Bernardo, Diego UNSPECIFIED di Bernardo, Mario UNSPECIFIED |
| Date: | 30 March 2015 |
| Number of Pages: | 144 |
| Keywords: | quantitative biology, synthetic biology, system identification, control engineering, mathematical modelling |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/04 - Automatica |
| Aree tematiche (7° programma Quadro): | BIOTECNOLOGIE, PRODOTTI ALIMENTARI E AGRICOLTURA > Scienze della vita, biotecnologia e biochimica per prodotti e processi non-alimentari sostenibili |
| Date Deposited: | 07 Apr 2015 10:23 |
| Last Modified: | 25 Sep 2015 09:37 |
| URI: | http://www.fedoa.unina.it/id/eprint/10303 |
| DOI: | 10.6092/UNINA/FEDOA/10303 |
Collection description
System identification is a branch of control engineering aimed at developing computational approaches to derive, from measurement data, a quantitative dynamical model of a physical system able to predict its future behaviour. There is a long tradition in the successful application of system identification approaches to Medicine and Physiology, however, in Molecular Biology, only few attempts have been made to infer a quantitative model of gene regulation due to experimental limitations of current techniques. Indeed, whereas in Engineering it is now common to measure thousands of time-points at a desired sampling rate for a physical system to be modelled, this has been very difficult in Biology, where time-series data consist of very few samples. In order to overcome the current limitations, I devised an experimental platform based on a microfluidic device, a time-lapse microscopy apparatus and, a set of automated syringes all controlled by a computer, that allows to provide a time varying concentration of any molecule of interest (input) to a population of cells, and to measure the single-cell response in the form of the fluorescence level of a reporter protein, at a sufficiently high sampling rate, thus making it possible to evaluate the dynamics of the process of interest. I tested the experimental platform to implement and compare different linear and nonlinear system identification approaches to a transcriptional network in the yeast S. cerevisiae. The results I obtained confirm that the experimental system identification platform I developed can successfully be used to infer quantitative models of a eukaryotic promoter in a rapid and efficient manner. Moreover I have used the same experimental set up for the study and the it in-vivo implementation of novel feedback control strategies meant to precisely regulate the level of expression of a protein from the GAL1 endogenous promoter and from a complex synthetic transcriptional network in yeast cells. The proposed effective control approach, allows to generate custom time profiles of a desired protein, and it can be exploited to study trafficking or signalling pathways and the endogenous control mechanisms of a cell.
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |
