Coding-theoretic methods for reverse engineering of gene regulatory networks

We provide an overview of known modeling approaches for gene regulatory networks, and introduce a new framework for analyzing such networks as probabilistic polynomial dynamical systems. In the latter context, we describe how list decoding methods for Reed-Muller codes can be used to cope with small DNA microarray sample set problems and measurement noise. We also describe possible future research directions at the interface of systems biology and coding theory, pertaining to probabilistic dynamical systems with memory and probabilistic factor graphs with local list-decoding components.