ONeSAMP: a program to estimate effective population size using approximate Bayesian computation

The estimation of effective population size from one sample of genotypes has been problematic because most estimators have been proven imprecise or biased. We developed a web-based program, ONeSAMP that uses approximate Bayesian computation to estimate effective population size from a sample of microsatellite genotypes. ONeSAMP requires an input file of sampled individuals' microsatellite genotypes along with information about several sampling and biological parameters. ONeSAMP provides an estimate of effective population size, along with 95% credible limits. We illustrate the use of ONeSAMP with an example data set from a re-introduced population of ibex Capra ibex.

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost-effectiveness by reducing the necessary amount of experiments and data points measured. (C) 2003 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Comparative evaluation of a new effective population size estimator based on approximate Bayesian computation

We describe and evaluate a new estimator of the effective population size (N-e), a critical parameter in evolutionary and conservation biology. This new "SummStat" N-e. estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N-e. Simulations of a Wright-Fisher population with known N-e show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, and N-e values. We also address the paucity of information about the relative performance of N-e estimators by comparing the SUMMStat estimator to two recently developed likelihood-based estimators and a traditional moment-based estimator. The SummStat estimator is the least biased of the four estimators compared. In 32 of 36 parameter combinations investigated rising initial allele frequencies drawn from a Dirichlet distribution, it has the lowest bias. The relative mean square error (RMSE) of the SummStat estimator was generally intermediate to the others. All of the estimators had RMSE > 1 when small samples (n = 20, five loci) were collected a generation apart. In contrast, when samples were separated by three or more generations and Ne less than or equal to 50, the SummStat and likelihood-based estimators all had greatly reduced RMSE. Under the conditions simulated, SummStat confidence intervals were more conservative than the likelihood-based estimators and more likely to include true N-e. The greatest strength of the SummStat estimator is its flexible structure. This flexibility allows it to incorporate any, potentially informative summary statistic from Population genetic data.

Mapping antimalarial pharmacophores as a useful tool for the rapid discovery of drugs effective in vivo: design, construction, characterization, and pharmacology of metaquine

Resistant strains of Plasmodium falciparum and the unavailability of useful antimalarial vaccines reinforce the need to develop new efficacious antimalarials. This study details a pharmacophore model that has been used to identify a potent, soluble, orally bioavailable antimalarial bisquinoline, metaquine (N,N'-bis(7-chloroquinolin-4-yl)benzene-1,3-diamine) (dihydrochloride), which is active against Plasmodium berghei in vivo (oral ID50 of 25 mu mol/kg) and multidrug-resistant Plasmodium falciparum K1 in vitro (0.17 mu M). Metaquine shows strong affinity for the putative antimalarial receptor, heme at pH 7.4 in aqueous DMSO. Both crystallographic analyses and quantum mechanical calculations (HF/6-31+G*) reveal important regions of protonation and bonding thought to persist at parasitic vacuolar pH concordant with our receptor model. Formation of drug-heme adduct in solution was confirmed using high-resolution positive ion electrospray mass spectrometry. Metaquine showed strong binding with the receptor in a 1: 1 ratio (log K = 5.7 +/- 0.1) that was predicted by molecular mechanics calculations. This study illustrates a rational multidisciplinary approach for the development of new 4-aminoquinoline antimalarials, with efficacy superior to chloroquine, based on the use of a pharmacophore model.