Inferring the spatiotemporal DNA replication program from noisy data.

We generalize a stochastic model of DNA replication to the case where replication-origin-initiation rates vary locally along the genome and with time. Using this generalized model, we address the inverse problem of inferring initiation rates from experimental data concerning replication in cell populations. Previous work based on curve fitting depended on arbitrarily chosen functional forms for the initiation rate, with free parameters that were constrained by the data. We introduce a nonparametric method of inference that is based on Gaussian process regression. The method replaces specific assumptions about the functional form of the initiation rate with more general prior expectations about the smoothness of variation of this rate, along the genome and in time. Using this inference method, we recover, with high precision, simulated replication schemes from noisy data that are typical of current experiments.

Inferring where and when replication initiates from genome-wide replication timing data.

Based on an analogy between DNA replication and one dimensional nucleation-and-growth processes, various attempts to infer the local initiation rate I(x,t) of DNA replication origins from replication timing data have been developed in the framework of phase transition kinetics theories. These works have all used curve-fit strategies to estimate I(x,t) from genome-wide replication timing data. Here, we show how to invert analytically the Kolmogorov-Johnson-Mehl-Avrami model and extract I(x,t) directly. Tests on both simulated and experimental budding-yeast data confirm the location and firing-time distribution of replication origins.

Self-assembly of the ionic peptide EAK16: the effect of charge distributions on self-assembly.

Amphiphilic peptides suspended in aqueous solution display a rich set of aggregation behavior. Molecular-level studies of relatively simple amphiphilic molecules under controlled conditions are an essential step toward a better understanding of self-assembly phenomena of naturally occurring peptides/proteins. Here, we study the influence of molecular architecture and interactions on the self-assembly of model peptides (EAK16s), using both experimental and theoretical approaches. Three different types of EAK16 were studied: EAK16-I, -II, and -IV, which have the same amino acid composition but different amino acid sequences. Atomic force microscopy confirms that EAK16-I and -II form fibrillar assemblies, whereas EAK16-IV forms globular structures. The Fourier transform infrared spectrum of EAK16-IV indicates the possible formation of a beta-turn structure, which is not found in EAK16-I and -II. Our theoretical and numerical studies suggest the underlying mechanism behind these observations. We show that the hairpin structure is energetically stable for EAK16-IV, whereas the chain entropy of EAK16-I and -II favors relatively stretched conformations. Our combined experimental and theoretical approaches provide a clear picture of the interplay between single-chain properties, as determined by peptide sequences (or charge distributions), and the emerging structure at the nano (or more coarse-grained) level.

Two experimental tests of the halperin-lubensky-Ma effect at the nematic-smectic- A phase transition

We have conducted two quantitative tests of predictions based on the Halperin-Lubensky-Ma (HLM) theory of fluctuation-induced first-order phase transitions. First, we explore the effect of an external magnetic field on the nematic-smectic- A transition in a liquid crystal. Second, we examine the dependence of the first-order discontinuity as a function of mixture concentration in pure 8CB and three 8CB-10CB mixtures. We find the first quantitative evidence for deviations from the HLM theory.