Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2012

Open Access Research

Quasi-cellular systems: stochastic simulation analysis at nanoscale range

Lorenzo Calviello1, Pasquale Stano2, Fabio Mavelli3, Pier Luigi Luisi2 and Roberto Marangoni14*

Author Affiliations

1 Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy

2 Dipartimento di Biologia, Università di Roma III, Via G. Marconi 446, 00146 Roma, Italy

3 Dipartimento di Chimica, Università di Bari, Via E. Orabona 4, 70121 Bari, Italy

4 Istituto di Biofisica del CNR, Via G. Moruzzi 1, 56124 Pisa, Italy

For all author emails, please log on.

BMC Bioinformatics 2013, 14(Suppl 7):S7  doi:10.1186/1471-2105-14-S7-S7

Published: 22 April 2013

Abstract

Background

The wet-lab synthesis of the simplest forms of life (minimal cells) is a challenging aspect in modern synthetic biology. Quasi-cellular systems able to produce proteins directly from DNA can be obtained by encapsulating the cell-free transcription/translation system PURESYSTEM™(PS) in liposomes. It is possible to detect the intra-vesicle protein production using DNA encoding for GFP and monitoring the fluorescence emission over time. The entrapment of solutes in small-volume liposomes is a fundamental open problem. Stochastic simulation is a valuable tool in the study of biochemical reaction at nanoscale range. QDC (Quick Direct-Method Controlled), a stochastic simulation software based on the well-known Gillespie's SSA algorithm, was used. A suitable model formally describing the PS reactions network was developed, to predict, from inner species concentrations (very difficult to measure in small-volumes), the resulting fluorescence signal (experimentally observable).

Results

Thanks to suitable features specific of QDC, we successfully formalized the dynamical coupling between the transcription and translation processes that occurs in the real PS, thus bypassing the concurrent-only environment of Gillespie's algorithm. Simulations were firstly performed for large liposomes (2.67µm of diameter) entrapping the PS to synthetize GFP. By varying the initial concentrations of the three main classes of molecules involved in the PS (DNA, enzymes, consumables), we were able to stochastically simulate the time-course of GFP-production. The sigmoid fit of the GFP-production curves allowed us to extract three quantitative parameters which are significantly dependent on the various initial states. Then we extended this study for small-volume liposomes (575 nm of diameter), where it is more complex to infer the intra-vesicle composition, due to the expected anomalous entrapment phenomena. We identified almost two extreme states that are forecasted to give rise to significantly different experimental observables.

Conclusions

The present work is the first one describing in the detail the stochastic behavior of the PS. Thanks to our results, an experimental approach is now possible, aimed at recording the GFP production kinetics in very small micro-emulsion droplets or liposomes, and inferring, by using the simulation as a reverse-engineering procedure, the internal solutes distribution, and shed light on the still unknown forces driving the entrapment phenomenon.