Predicting altered pathways using extendable scaffolds.

Many diseases, especially solid tumors, involve the disruption or deregulation of cellular processes. Most current work using gene expression and other high-throughput data, simply list a set of differentially expressed genes. We propose a new method, PAPES (predicting altered pathways using extendable scaffolds), to computationally reverse-engineer models of biological systems. We use sets of genes that occur in a known biological pathway to construct component process models. We then compose these models to build larger scale networks that capture interactions among pathways. We show that we can learn process modifications in two coupled metabolic pathways in prostate cancer cells.

Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models.

Multi-wave self-report data on age at menopause in 2182 female twin pairs (1355 monozygotic and 827 dizygotic pairs), were analysed to estimate the genetic, common and unique environmental contribution to variation in age at menopause. Two complementary approaches for analysing correlated time-to-onset twin data are considered: the generalized estimating equations (GEE) method in which one can estimate zygosity-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modelled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the freeware package BUGS.