ArticleBMC bioinformatics2012
Characterization of the emergent properties of a synthetic quasi-cellular system.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 35 citations in OpenAlex.
- Modeling Cell-Free Protein Synthesis Systems-Approaches and Applications.Frontiers in bioengineering and biotechnology · 2020Review
- Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks.RSC advances · 2019Article
- Is Research on "Synthetic Cells" Moving to the Next Level?Life (Basel, Switzerland) · 2018Article
- A renewal model for the emergence of anomalous solute crowding in liposomes.BMC systems biology · 2015Article
- Spontaneous encapsulation and concentration of biological macromolecules in liposomes: an intriguing phenomenon and its relevance in origins of life.Journal of molecular evolution · 2014Review
- Synthetic biology outside the cell: linking computational tools to cell-free systems.Frontiers in bioengineering and biotechnology · 2014Review
- The encapsulation of cell-free transcription and translation machinery in vesicles for the construction of cellular mimics.Journal of visualized experiments : JoVE · 2013Article
- Quasi-cellular systems: stochastic simulation analysis at nanoscale range.BMC bioinformatics · 2013Article
- Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics.BMC bioinformatics · 2012Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe process of solutes entrapment during liposomes formation is interesting for the investigation of the relationship between the formation of compartments and the distribution of molecules inside them; a relevant issue in the studies of the origin of life. Theoretically, when no interactions are supposed among the chemical species to be entrapped, the entrapment is described by a standard Poisson process. But very recent experimental findings show that, for small liposomes (100 nm diameter), the distribution of entrapped molecules is best described by a power-law function. This is of a great importance, as the two random processes give rise to two completely different scenarios. Here we present an in silico stochastic simulation of the encapsulation of a cell-free molecular translation system (the PURE system), obtained following two different entrapment models: a pure Poisson process, and a power-law. The protein synthesis inside the liposomes has been studied in both cases, with the aim to highlight experimental observables that could be measured to assess which model gives a better representation of the real process.
resultsFirstly, a minimal model for in vitro protein synthesis, based on the PURE system molecular composition, has been formalized. Then, we have designed a reliable experimental simulation where stochastic factors affect the reaction course inside the compartment. To this end, 24 solutes, which represent the PURE system components, have been stochastically distributed among vesicles by following either a Poisson or a power-law distribution. The course of the protein synthesis within each vesicle has been consequently calculated, as a function of vesicle size. Our study can predict translation yield in a population of small liposomes down to the attoliter (10(-18) L) range. Our results show that the efficiency of protein synthesis peaks at approximately 3 · 10(-16) L (840 nm diam.) with a Poisson distribution of solutes, while a relative optimum is found at around 10(-17) L (275 nm diam.) for the power-law statistics.
conclusionsOur simulation clearly shows that the wet-lab measurement of an effective protein synthesis at smaller volumes than 10(-17) L would rule out, according to our models, a Poisson distribution of solutes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.