Fiore, Gianfranco
(2015)
Identification and control of gene networks in living cells.
[Tesi di dottorato]
| Tipologia del documento: |
Tesi di dottorato
|
| Lingua: |
English |
| Titolo: |
Identification and control of gene networks in living cells |
| Autori: |
| Autore | Email |
|---|
| Fiore, Gianfranco | ing.gianfrancofiore@gmail.com |
|
| Data: |
30 Marzo 2015 |
| Numero di pagine: |
144 |
| Istituzione: |
Università degli Studi di Napoli Federico II |
| Dipartimento: |
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 | [non definito] | | di Bernardo, Mario | [non definito] |
|
| Data: |
30 Marzo 2015 |
| Numero di pagine: |
144 |
| Parole chiave: |
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 |
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| Depositato il: |
07 Apr 2015 10:23 |
| Ultima modifica: |
25 Set 2015 09:37 |
| URI: |
http://www.fedoa.unina.it/id/eprint/10303 |
| DOI: |
10.6092/UNINA/FEDOA/10303 |
Abstract
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.
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