Extremely low vapor‐pressure data as access to PC‐SAFT parameter estimation for ionic liquids and modeling of precursor solubility in ionic liquids

Precursor solubility is a crucial factor in industrial applications, dominating the outcome of reactions and purification steps. The outcome and success of thermodynamic modelling of this industrially important property with equations of states, such as Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), vastly depends on the quality of the pure-component parameters. The pure-component parameters for low-volatile compounds such as ionic liquids (ILs) have been commonly estimated using mixture properties, e. g. the osmotic pressure of aqueous solutions. This leads to parameters that depend on the solvent, and transferability to other mixtures often causes poor modeling results. Mixture-independent experimental properties would be a more suitable basis for the parameter estimation offering a way to universal parameter sets. Model parameters for ILs are available in the literature [10.1016/j.fluid.2012.05.029], but they were estimated using pure-IL density data. The present work focuses on a step towards a more universal estimation strategy that includes new experimental vapor-pressure data of the pure IL. ILs exhibit an almost negligible vapor pressure in magnitude of usually 10−5 Pa even at elevated temperatures. In this work, such vapor-pressure data of a series of 1-ethyl-3-methyl-imidazolium-based [C2mim]-ILs with various IL-anions (e. g. tetrafluoroborate [BF4]−, hexafluorophosphate [PF6]−, bis(trifluoromethylsulfonyl)imide [NTf2]−) were experimentally determined and subsequently used for PC-SAFT parameter estimation. The so-determined parameters were used to predict experimental molecular precursor solubility in ILs and infinitely diluted activity coefficients of various solvents in ILs. The parameters were further compared to modeling results using classical parametrization methods (use of liquid-density data only for the molecular PC-SAFT and the ion-based electrolyte PC-SAFT). As a result, the modeled precursor solubilities using the new approach are much more precise than using the classical parametrization methods, and required binary parameters were found to be much smaller (if needed). In sum, including the pure-component vapor-pressure data of ILs opens the door towards parameter estimation that is not biased by mixture data. This procedure might be suitable also for polymers and for all kind of ionic species but needs extension to ion-specific parametrization in the long term

Human Lin28 forms a high-affinity 1:1 complex with the 106~363 cluster miRNA miR-363

Lin28A is a post-transcriptional regulator of gene expression that interacts with and negatively regulates the biogenesis of let-7 family miRNAs. Recent data suggested that Lin28A also binds the putative tumour suppressor miR-363, a member of the 106~363 cluster of miRNAs. Affinity toward this miRNA and the stoichiometry of the protein-RNA complex are unknown. Characterisation of human Lin28's interaction with RNA has been complicated by difficulties in producing stable RNA-free protein. We have engineered a maltose binding protein fusion with Lin28, which binds let-7 miRNA with a Kd of 54.1 ± 4.2 nM, in agreement with previous data on a murine homologue. We show that human Lin28A binds miR-363 with 1:1 stoichiometry and with similar, if not higher, affinity (Kd = 16.6 ± 1.9 nM). Further analysis suggests that the interaction of the N-terminal cold shock domain of Lin28A with RNA is salt-dependent, supporting a model where the cold shock domain allows the protein to sample RNA substrates through transient electrostatic interactions

Effects of Transcriptional Pausing on Gene Expression Dynamics

Stochasticity in gene expression affects many cellular processes and is a source of phenotypic diversity between genetically identical individuals. Events in elongation, particularly RNA polymerase pausing, are a source of this noise. Since the rate and duration of pausing are sequence-dependent, this regulatory mechanism of transcriptional dynamics is evolvable. The dependency of pause propensity on regulatory molecules makes pausing a response mechanism to external stress. Using a delayed stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, we investigate how RNA polymerase pausing affects a gene's transcriptional dynamics and gene networks. We show that pauses' duration and rate of occurrence affect the bursting in RNA production, transcriptional and translational noise, and the transient to reach mean RNA and protein levels. In a genetic repressilator, increasing the pausing rate and the duration of pausing events increases the period length but does not affect the robustness of the periodicity. We conclude that RNA polymerase pausing might be an important evolvable feature of genetic networks