Coarse-Grained Dynamics of Protein Synthesis in a Cell-Free System

Karzbrun, Eyal; Shin, Jinwoo; Bar‐Ziv, Roy; Noireaux, Vincent

doi:10.1103/physrevlett.106.048104

Cited by 128 publications

(151 citation statements)

References 29 publications

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“…At very low DNA concentrations, the mRNA production in single-phase droplets is too small to be detected reliably. Remarkably, both the RNA polymerase binding constant and the transcription rate constant in coacervates are of the same order of magnitude as the values typically found for E. coli in vivo (33), whereas two orders of magnitude lower constants are usually found in bulk experiments on cell-free expression kits with the same RNA polymerase in vitro (30,31). This supports our conclusion that the coacervates are crowded environments that mimic the conditions necessary for in vivo transcription.…”

supporting

confidence: 77%

“…S6 for mRNA production from 40, 60, 80, 120, and 160 pM DNA). All curves exhibit a delay time of ∼10-20 min and a slowdown in mRNA production after 80-120 min, probably due to depletion of transcription resources and inactivation of some of the components from the lysate (30,31). Clearly, the rate of mRNA production in the coacervate is increased about 50-fold compared with the nonshrunk droplets, and micromolar concentrations of mRNA are obtained in coacervate droplets from subnanomolar concentrations of DNA in the starting droplet.…”

mentioning

confidence: 87%

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Enhanced transcription rates in membrane-free protocells formed by coacervation of cell lysate

Соколова

Spruijt

Hansen

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

318

322

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Liquid-liquid phase transitions in complex mixtures of proteins and other molecules produce crowded compartments supporting in vitro transcription and translation. We developed a method based on picoliter water-in-oil droplets to induce coacervation in Escherichia coli cell lysate and follow gene expression under crowded and noncrowded conditions. Coacervation creates an artificial cell-like environment in which the rate of mRNA production is increased significantly. Fits to the measured transcription rates show a two orders of magnitude larger binding constant between DNA and T7 RNA polymerase, and five to six times larger rate constant for transcription in crowded environments, strikingly similar to in vivo rates. The effect of crowding on interactions and kinetics of the fundamental machinery of gene expression has a direct impact on our understanding of biochemical networks in vivo. Moreover, our results show the intrinsic potential of cellular components to facilitate macromolecular organization into membranefree compartments by phase separation.microdroplets | macromolecular crowding P rotocells are minimal compartmentalized systems exhibiting key characteristics of cellular function, including metabolism and replication (1, 2). Lipid vesicles are considered the prototypical protocell as they can form functional microscopic spherical assemblies suited for in vitro gene expression (3, 4). Compartmentalization via lipid bilayers is considered essential for the emergence of cells (4), but there are alternative models based on liquid-liquid phase transitions that lead to the emergence of compartments (5, 6). Compartmentalization is but one characteristic, as protocells ideally also mimic the highly crowded interior of living cells, which have total macromolecule concentrations in excess of 300 g/L (7). Examples in which compartmentalization and high local concentrations are obtained concurrently, include DNA brushes (8), aqueous two-phase systems (9), and liquid coacervates (10). Phase separation or coacervation occurs in a wide range of polymer and protein solutions, often triggered by changes in temperature or salt concentration, or by the addition of coacervating agents (11). The (complex) coacervate droplets that are formed in such systems present macromolecularly crowded, aqueous, physical compartments, 1-100 μm in diameter (12). Recent work has identified similar liquid phase transitions in vivo in the formation of intracellular non-membrane-bound compartments exhibiting liquid-like properties, slowed down diffusion, and strongly interacting macromolecular components (13,14). Well-studied examples are the intracellular localization of DNA or RNA and proteins in Cajal bodies, P granules, and nucleoli (15-17), which can contain over 100 components. Such complexity has not been achieved in two-phase systems in vitro (18,19). Although the physics of coacervates is well understood, progress in their development as protocell models has stalled, because of the lack of demonstrations of complex biochemical proce...

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supporting

confidence: 77%

mentioning

confidence: 87%

Enhanced transcription rates in membrane-free protocells formed by coacervation of cell lysate

Соколова

Spruijt

Hansen

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

318

322

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“…Based on the excellent control of the input conditions, IVT has been used as a template for computational modeling (18)(19)(20). Coarse-grained models consisting of a limited number of parameters and reactions that consider protein translation as a single reaction step have been developed and used to understand the dynamics of protein translation.…”

Section: Discussionmentioning

confidence: 99%

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling

Matsuura

Tanimura²,

Hosoda

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia colibased reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other largescale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions.computational modeling | protein translation | cell-free protein synthesis | quasi-stationary state | network analysis B iological systems contain and are driven by multiple molecular components and reactions that form a complex reaction network. To elucidate the fundamental rules or features underlying complex reaction networks, various abstract computational models have been constructed (1-4). For example, cellular automaton (2) and NK models (1) have been applied to extract the fundamental features in the patterning and evolution of biological systems, respectively. A simplified cellular model was used to reveal why expressed gene copy number follows the power-law distribution (Zipf's law) (3). Although these models capture only the basic features of biological systems, they have helped elucidate the complex properties of life mechanisms. Additionally, although models that enumerate all detailed cellular processes have been constructed (5-7), attempts to extract the features or rules from such detailed models have been performed rarely.A single enzymatic reaction can often be described by MichaelisMenten kinetics, but once reactions are connected to one other, it becomes difficult to understand and capture a complete description of the reaction dynamics because of its high dimensionality. Despite such complexity, many high-dimensional dynamic data obtained from a variety of numerical models exhibit common behavior (reviewed in ref. 8). The models...

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“…As of today, only a few papers have been published dealing with theoretical simulations, at different detail level, of PS behavior and time course [15][16][17][18], and only three deal with PS entrapped in liposomes [19][20][21]. This last condition is very interesting since the smaller the liposome, the lower the probability that a large amount of molecules of each constituent chemical species be entrapped in the volume of the liposome.…”

Section: Introductionmentioning

confidence: 99%

On Fine Stochastic Simulations of Liposome-Encapsulated PUREsystem™

Ospri

Marangoni

2016

Communications in Computer and Information Science

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Abstract. The PURESystem TM (for short: PS) is a defined set of about 80 different macromolecular species which can perform protein synthesis starting from a coding DNA. To understand the processes that take place inside a liposome with entrapped PS, several simulation approaches, of either a deterministic or stochastic nature, have been proposed in the literature. To correctly describe some peculiar phenomena that are observed only in very small liposomes (such as power-law distribution of solutes and supercrowding effect), a stochastic approach seems necessary, due to the very small average number of molecules contained in these liposomes. Here we recall the results reported in other works published by us and by other Authors, discussing the importance of a stochastic simulation approach and of a fine description of the system: both these aspects, in fact, were not properly acknowledged in such previous papers.

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Coarse-Grained Dynamics of Protein Synthesis in a Cell-Free System

Cited by 128 publications

References 29 publications

Enhanced transcription rates in membrane-free protocells formed by coacervation of cell lysate

Enhanced transcription rates in membrane-free protocells formed by coacervation of cell lysate

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling

On Fine Stochastic Simulations of Liposome-Encapsulated PUREsystem™

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