Menolascina, Filippo (2011) Synthetic Gene Networks Identification and Control by means of Microfluidic Devices. [Tesi di dottorato] (Unpublished)


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Item Type: Tesi di dottorato
Lingua: English
Title: Synthetic Gene Networks Identification and Control by means of Microfluidic Devices
Date: 30 November 2011
Number of Pages: 217
Institution: Università degli Studi di Napoli Federico II
Department: Biologia e patologia cellullare e molecolare "L. Califano"
Scuola di dottorato: Biotecnologie
Dottorato: Biologia computazionale e bioinformatica
Ciclo di dottorato: 24
Coordinatore del Corso di dottorato:
Di Bernardo,
Di Bernardo,
Date: 30 November 2011
Number of Pages: 217
Uncontrolled Keywords: Control Theory in Sythetic Biology
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/04 - Automatica
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/06 - Bioingegneria elettronica e informatica
Date Deposited: 06 Dec 2011 14:57
Last Modified: 30 Apr 2014 19:48
DOI: 10.6092/UNINA/FEDOA/8872


In this Thesis, I demonstrated how modelling and control of synthetic gene networks can be achieved by using principles from control and systems theory. The first step consisted in analysing the expected challenges and in looking for suitable solutions to them. The first choice made in this context concerned the selection of a suitable testbed to prove real-time in-vivo controllability of gene networks in a cell population. I needed a benchmark that had the following properties: (a) a reasonable level of complexity (b) an available differential equation model (c) decoupled from the rest of endogenous gene circuitry. As described in Chapter 2, IRMA satisfied these properties. IRMA is synthetic gene network built in S. cerevisiae, thus a eukaryotic model system, made up of five yeast genes completely rearranged in their regulatory network topology to avoid any cross talk with wild type genome. A non-linear, hybrid, time-delayed mathematical model for IRMA has been re-derived to test the effectiveness of the designed control strategies in-silico. Several alternative strategies have been designed, tested and presented in Chapter 3 to address the problem at hand. A major goal I struggled to achieve during control system design was to minimise the level of complexity of the proposed strategies: the risk in ignoring this lays in obtaining solutions showing astonishing results in-silico but failing to display the robustness required in real-world experiments. Therefore, the proposed schemes spanned from the simple relay based control to the Smith Predictor based configuration that includes a proportional-integral controller and a Pulse-Width-Modulation strategy. Designs, peculiarities and performances of these control schemes have been exposed in Chapter 3. In order to translate these designs in in-vivo control strategies, I proposed and implemented a new technological platform presented in Chapter 4. As previously outlined, a paradigm shift was needed in order to demonstrate the working hypothesis. As a matter of fact, flask-based experiments could not be used to accomplish real-time in-vivo control experiment because of the several limitations (theoretical and practical) listed in Chapter 4. Therefore I decided to take advantage of the latest achievements in the field of microfluidics. Device design, optimisation and fabrication has been presented together with the results of a collaboration with Prof. Jeff Hasty at University of California at San Diego. During my visit at Prof. Hasty’s laboratory in late 2009, I have been able to acquire technical expertise that allowed me to re-engineer their platform in Dr. di Bernardo Laboratory at TIGEM as described in Chapter 4. Once the platform was set up, I carried out experimental procedures to obtain in-vivo real-time control whose results have been reported in Chapter 5. At the same time I could apply principles from control theory to the identification of a well-known gene network motif, namely a Positive Feedback Loop, in a mammalian cell line. The results of this study have been presented in Chapter 6 with considerations on dynamical properties of this simple circuit and some hints at potential advantages it can confer to cells. All these results, taken together confirm that control theory principles can be successfully applied to both the identification and control of gene regulatory networks and open new roads for research in the fields of systems and synthetic biology.


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