Cantone, Irene (2009) A yeast synthetic network for In-vivo Reverse-engineering and Modelling Assessment (IRMA). [Tesi di dottorato] (Unpublished)

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Item Type: Tesi di dottorato
Uncontrolled Keywords: System Biology; Synthetic Biology; Modelling; Reverse-engineering; Yeast Synthetic Network
Date Deposited: 13 Nov 2009 13:40
Last Modified: 30 Apr 2014 19:36
URI: http://www.fedoa.unina.it/id/eprint/3323

Abstract

Systems Biology approaches aim to reconstruct gene regulatory networks from experimental data. Conversely, Synthetic Biology aims at using mathematical models to design novel biological ‘circuits’ (synthetic networks) in order to seed new functions inside the cell. These disciplines require quantitative mathematical models and reverse-engineering techniques. A plethora of modelling strategies and reverse-engineering approaches has being proposed during the last years. Even if successful applications have being demonstrated, at present their usefulness and predictive ability cannot still be assessed and compared rigorously. There is the pressing and yet unsatisfied need for a ‘benchmark’: a perfectly known biological circuit that can be used to evaluate pro and cons of such techniques when applied at in vivo networks. In order to address this aim, we constructed in the simplest eukaryotic organism, the yeast Saccharomyces cerevisiae, a novel synthetic network for In-vivo Reverse-engineering and Modelling Assessment (IRMA). IRMA is composed of five well-studied genes that have been assembled to regulate each other in such a way to include a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. It was designed to be isolated from the cellular environment, and to respond to galactose by triggering transcription of its genes. To demonstrate that IRMA is a unique resource to validate the System and Synthetic biology approaches, we analysed the transcriptional response of IRMA genes following two different perturbation strategies: by performing a single perturbation and measuring mRNA changes at different time points, or by performing multiple perturbations and collecting mRNA measurements at steady state. We used these data as a ‘gold standard’ to assess either the predictive ability of mathematical modelling based on differential equations and, to compare four well-established reverse engineering algorithms, NIR, TSNI, BANJO and ARACNE. We thus showed the usefulness of IRMA as the first simplified model of eukaryotic gene networks built “ad hoc” to test the power of network modelling and reverse-engineering strategies.

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