Poly(zwitterionic liquids) functionalized polypyrrole/graphene oxide nanosheets for electrochemically detecting dopamine at low concentration.

Poly(3-(1-vinylimidazolium-3-yl)propane-1-sulfonate) (PVIPS), a novel kind of poly(zwitterionic liquids) (PZILs) containing both imidazolium cation and sulfonate anion, was successfully modified on the surface of polypyrrole/graphene oxide nanosheets (PPy/GO) by covalent bonding. The obtained novel PZILs functionalized PPy/GO nanosheets (PVIPS/PPy/GO) modified glassy carbon electrode (GCE) presented the excellent electrochemical catalytic activity towards dopamine (DA) with high stability, sensitivity, selectivity and wide linear range (40-1220nM), especially having a lower detection limit (17.3nM). The excellent analytical performance is attributed to the strongly negative charges on the surface of modified GCE in aqueous solution, which is different from conventional poly(ionic liquids) modified GCE. DA cations could be quickly enriched on the electrode surface by electrostatic interaction in solution due to the existence of SO3(-) groups with negative charge at the end of pendant groups in zwitterionic PVIPS, resulting in a change of the electrons transmission mode in the oxidation of DA, that is, from a typical diffusion-controlled process at conventional poly(1-vinyl-3-ethylimidazole bromide) (PVEIB)/PPy/GO modified GCE to a typical surface-controlled process.

Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.

Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes.