List-decoding methods for inferring polynomials in finite dynamical gene network models.

Motivation: The problem of reverse engineering the dynamics of gene expression profiles is of focal importance in systems biology. Due to noise and the inherent lack of sufficiently large datasets generated via high-throughput measurements, known reconstruction frameworks based on dynamical systems models fail to provide adequate settings for network analysis. This motivates the study of new approaches that produce stochastic lists of explanations for the observed network dynamics that can be efficiently inferred from small sample sets and in the presence of errors.

Local conservation scores without a priori assumptions on neutral substitution rates.

Background: Comparative genomics aims to detect signals of evolutionary conservation as an indicator of functional constraint. Surprisingly, results of the ENCODE project revealed that about half of the experimentally verified functional elements found in non-coding DNA were classified as unconstrained by computational predictions. Following this observation, it has been hypothesized that this may be partly explained by biased estimates on neutral evolutionary rates used by existing sequence conservation metrics.