With incomplete lineage sorting (ILS), the genealogy of closely related species differs along their genomes. The amount of ILS depends on population parameters such as the ancestral effective population sizes and the recombination rate, but also on the number of generations between speciation events. We use a hidden Markov model parameterized according to coalescent theory to infer the genealog...

In this chapter, we consider the issue of Hidden Markov Model (HMM) training. First, HMMs are introduced and then we focus on the particular HMMs training problem. We emphasize the difficulty of this problem and present various criteria that can be considered. Many different adaptations of metaheuristics have already been used but, until now, a few extensive comparisons have been performed on t...

Recently, the extent of copy number variation (CNV) throughout the genome has been shown to be far greater than previously thought. Further, it has been demonstrated that specific copy number variable regions (CNVRs) are associated with particular diseases, suggesting that these genetic variations may have an important biological role. Hence, calling CNVRs and subsequently classifying samples a...

BACKGROUND Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dyna...

Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncerta...

In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other parameters such as mutation and recombination rates. As more loci, sequences, and hidden states are added to the model, however, the runtime of coalescent ...