Learning Delays in Biological Regulatory Networks from Time Series Data

Abstract

Models of Biological Regulatory Networks are generally based on prior knowledge, either derived from literature and/or the manual analysis of biological observations. With the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. To have a better understanding of the dynamical phenomena at stake in the influences between biological components, it would be necessary to include delayed influences in the model.The main purpose of this work is to have a resulting network as consistent as possible with the observed datasets regarding the conflicts and the simultaneity between transitions. The originality of our work is threefold: (i) the identification the sign of the interactions, (ii) the direct integration of quantitative time delays in the learning approach and (iii) the identification of the qualitative discrete levels that lead to the systems dynamics.In this work the precision of our automatic approach is discussed by applying it on dynamical biological models coming from the DREAM4 Challenge datasets.