Daniel T. Robb

Computational model for the formation of uniform silver spheres by aggregation of nanosize precursors.

We present results of computational modeling of the formation of uniform spherical silver particles prepared by rapid mixing of ascorbic acid and silver-amine complex solutions in the absence of a dispersing agent. Using an accelerated integration scheme to speed up the calculation of particle size distributions in the latter stages, we find that the recently reported experimental results-some of which are summarized here-can be modeled effectively by the two-stage formation mechanism used previously to model the preparation of uniform gold spheres. We treat both the equilibrium concentration of silver atoms and the surface tension of silver precursor nanocrystals as free parameters, and find that the experimental reaction time scale is fit by a narrow region of this two-parameter space. The kinetic parameter required to quantitatively match the final particle size is found to be very close to that used previously in modeling the formation of gold particles, suggesting that similar kinetics governs the aggregation process and providing evidence that the two-stage model of burst nucleation of nanocrystalline precursors followed by their aggregation to form the final colloids can be applied to systems both with and without dispersing agents. The model also reproduced semiquantitatively the effects of solvent viscosity and temperature on the particle preparation.

Random Phenotypic Variation of Yeast (Saccharomyces cerevisiae) Single-Gene Knockouts Fits a Double Pareto-Lognormal Distribution

Background Distributed robustness is thought to influence the buffering of random phenotypic variation through the scale-free topology of gene regulatory, metabolic, and protein-protein interaction networks. If this hypothesis is true, then the phenotypic response to the perturbation of particular nodes in such a network should be proportional to the number of links those nodes make with neighboring nodes. This suggests a probability distribution approximating an inverse power-law of random phenotypic variation. Zero phenotypic variation, however, is impossible, because random molecular and cellular processes are essential to normal development. Consequently, a more realistic distribution should have a y-intercept close to zero in the lower tail, a mode greater than zero, and a long (fat) upper tail. The double Pareto-lognormal (DPLN) distribution is an ideal candidate distribution. It consists of a mixture of a lognormal body and upper and lower power-law tails. Objective and Methods If our assumptions are true, the DPLN distribution should provide a better fit to random phenotypic variation in a large series of single-gene knockout lines than other skewed or symmetrical distributions. We fit a large published data set of single-gene knockout lines in Saccharomyces cerevisiae to seven different probability distributions: DPLN, right Pareto-lognormal (RPLN), left Pareto-lognormal (LPLN), normal, lognormal, exponential, and Pareto. The best model was judged by the Akaike Information Criterion (AIC). Results Phenotypic variation among gene knockouts in S. cerevisiae fits a double Pareto-lognormal (DPLN) distribution better than any of the alternative distributions, including the right Pareto-lognormal and lognormal distributions. Conclusions and Significance A DPLN distribution is consistent with the hypothesis that developmental stability is mediated, in part, by distributed robustness, the resilience of gene regulatory, metabolic, and protein-protein interaction networks. Alternatively, multiplicative cell growth, and the mixing of lognormal distributions having different variances, may generate a DPLN distribution.

Synthesis of Silver Colloids: Experiment and Computational Model

We summarize our recent results that model the formation of uniform spherical silver colloids prepared by mixing iso-ascorbic acid and silver-amine complex solutions in the absence of dispersants. We found that the experimental results can be modeled effectively by the two-stage formation mechanism used previously to model the preparation of colloidal gold spheres. The equilibrium concentration of silver atoms and the surface tension of silver precursor nanocrystals are both treated as free parameters, and the experimental reaction time scale is fit by a narrow region of this two-parameter space. The kinetic parameter required to match the final particle size is found to be very close to that used previously in modeling the formation of uniform gold particles, suggesting that similar kinetics governs the aggregation process. The model also reproduces semi quantitatively the effects of temperature and solvent viscosity on particle synthesis.