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Quasi-cellular systems: stochastic simulation analysis at nanoscale range

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 li...

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Autores principales: Calviello, Lorenzo, Stano, Pasquale, Mavelli, Fabio, Luisi, Pier Luigi, Marangoni, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3633058/
https://www.ncbi.nlm.nih.gov/pubmed/23815522
http://dx.doi.org/10.1186/1471-2105-14-S7-S7
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author Calviello, Lorenzo
Stano, Pasquale
Mavelli, Fabio
Luisi, Pier Luigi
Marangoni, Roberto
author_facet Calviello, Lorenzo
Stano, Pasquale
Mavelli, Fabio
Luisi, Pier Luigi
Marangoni, Roberto
author_sort Calviello, Lorenzo
collection PubMed
description 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.
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spelling pubmed-36330582013-04-25 Quasi-cellular systems: stochastic simulation analysis at nanoscale range Calviello, Lorenzo Stano, Pasquale Mavelli, Fabio Luisi, Pier Luigi Marangoni, Roberto BMC Bioinformatics Research 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. BioMed Central 2013-04-22 /pmc/articles/PMC3633058/ /pubmed/23815522 http://dx.doi.org/10.1186/1471-2105-14-S7-S7 Text en Copyright © 2013 Calviello et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Calviello, Lorenzo
Stano, Pasquale
Mavelli, Fabio
Luisi, Pier Luigi
Marangoni, Roberto
Quasi-cellular systems: stochastic simulation analysis at nanoscale range
title Quasi-cellular systems: stochastic simulation analysis at nanoscale range
title_full Quasi-cellular systems: stochastic simulation analysis at nanoscale range
title_fullStr Quasi-cellular systems: stochastic simulation analysis at nanoscale range
title_full_unstemmed Quasi-cellular systems: stochastic simulation analysis at nanoscale range
title_short Quasi-cellular systems: stochastic simulation analysis at nanoscale range
title_sort quasi-cellular systems: stochastic simulation analysis at nanoscale range
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3633058/
https://www.ncbi.nlm.nih.gov/pubmed/23815522
http://dx.doi.org/10.1186/1471-2105-14-S7-S7
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