Convergent evolution of mutational robustness.

<p>The dots represent the actual mutational robustness levels of a group of GRNs having identical initial <i>d</i> = 0.1. The mutational robustness levels at the initial and final generation are linked by solid lines. The population size is 100, and mutation rates are A) 0.001, B) 0.05, C) 0.1 and D) 0.2.</p

The Evolution of Heterogeneities Altered by Mutational Robustness, Gene Expression Noise and Bottlenecks in Gene Regulatory Networks

<div><p>Intra-population heterogeneity is commonly observed in natural and cellular populations and has profound influence on their evolution. For example, intra-tumor heterogeneity is prevalent in most tumor types and can result in the failure of tumor therapy. However, the evolutionary process of heterogeneity remains poorly characterized at both genotypic and phenotypic level. Here we study the evolution of intra-population heterogeneities of gene regulatory networks (GRN), in particular mutational robustness and gene expression noise as contributors to the development of heterogeneities. By <i>in silico</i> simulations, it was found that the impact of these factors on GRN can, under certain conditions, promote phenotypic heterogeneity. We also studied the effect of population bottlenecks on the evolution of GRN. When the GRN population passes through such bottlenecks, neither mutational robustness nor population fitness was observed to be substantially altered. Interestingly, however, we did detect a significant increase in the number of potential “generator” genes which can substantially induce population fitness, when stimulated by mutational hits.</p></div

Fitness gain and number of potential oncogene changes when subjected to type I population bottleneck.

<p>Each boxplots represents median and standard deviation of maximal fitness gain before and after the bottleneck for 50 simulation repeats. The dots represent the percentage of potential oncogenes before and after passing through the bottleneck (see main text for definition). Gene expression noise level is 0, population size is indicated in color, and mutation rates are A) 0.001, B) 0.05, C) 0.1and D) 0.2.</p

Coefficients in the linear regression models.

<p>The interaction between gene expression noise (<i>δ</i>) and mutational robustness (<i>d</i>) is considered (<i>δ</i>:<i>d</i>). The coefficients significantly not zero (F-test) are marked by * (P-value <0.01) and ** (P-value <0.0001).</p><p>Coefficients in the linear regression models.</p

The interaction among mutation, mutational robustness and gene expression noise.

<p>Each square represents a set of simulations with identical gene expression noise level and mutation rate. The color in each square index <i>_R</i> was defined as the proportion of simulation pairs indicating that a population with higher mutational robustness would be more phenotypically heterogeneous than a population with lower mutational robustness after stabilizing selection. The population size is 100.</p

Heterogeneity levels when a population is subjected to stabilizing selection.

<p>This figure shows the density of converged heterogeneity levels from 50 replications of simulation for a population with 100 individuals. The colors represent the mutation rates. A) genotypic heterogeneity and B) phenotypic heterogeneity.</p

Gram-Scale Synthesis and Biofunctionalization of Silica-Coated Silver Nanoparticles for Fast Colorimetric DNA Detection

A direct silica-coating method has been developed for the gram-scale synthesis of well-dispersed Ag@SiO2 nanoparticles. Subsequent surface functionalization via the well-established silica surface chemistry provided arching points for straightforward bioconjugation with amino-terminated oligonucleotides. Fast hybridization kinetics of the resulting robust oligo-modified Ag@SiO2 nanoprobes with complementary target oligonucleotides render themselves very useful for the fast colorimetric DNA detection based on the sequence-specific hybridization properties of DNA. Additionally, the reliable protocols developed in this study for preparing and functionalizing Ag@SiO2 nanoparticles can be readily extended to other silica-coated nanoparticles, which can also provide a specific platform for the covalent attachment of biomolecules such as amino-rich proteins, enzymes, or amino-terminated oligonucleotides for diverse bioapplications

Features selected for the best models in HepG2.

<p>Stacked bars represent the distributions of selected feature types between nucleosome position (NU), histone modification levels (HM), and DNA sequence information (SEQ). The bar length represents the selected fraction of each type of features (with the range from 0 to 1). Squares and diamonds represent the mean PCC and the mean BIC of the best models in the corresponding model categories, respectively, A) for CpG-related promoters, and B) for nonCpG-related promoters.</p

Predictive power of 2-models.

<p>The dots represent the predictive power of the models, blue and red indicating models were trained and tested in CpG-related and nonCpG-related promoters, respectively. The x-axis is the PCC generated by the 2-models involved in two histone modifications, one is from Class I, and the other is from Class II. The y-axis is the PCC generated by the 2-models involving two histone modifications from the same class, either Class I or Class II. Error bars give the standard derivations within the two cases.</p

The receiver operating characteristic (ROC) curve for the performance of models trained in NHEK cell, and the performance of <i>Rach et al</i>’s logistic regression model.

<p>A) for CpG-related promoters, and B) for nonCpG-related promoters.</p