Data-driven reconstruction of directed networks: from genes to chaotic oscillators

Aneta Koseska

Short Abstract
Identifying regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks is of paramount importance in the contemporary systems biology research. These investigations are however hindered due to the short data sets that are available, rendering the standard association measures in most of the cases unable to address the inference problem in its full extent. We propose and analyze inner composition alignment - a novel, permutation-based asymmetric association measure to detect regulatory links from very short time series. The measure allows to identify (uni- and bidirectional) coupling and its directionality, distinguish direct from indirect links, and infer autoregulation. Applications to gene regulatory networks of E.coli and C.reinhardtii are presented. Moreover, the ability of the measure to analyze chaotic time series is addressed as well.