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