Inference of Delayed Biological Regulatory Networks from Time Series Data

Abstract

Abstract. The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. But with the development of highthroughput data, there is a growing need for methods that automatically generate admissible models. Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences amoung genes. In this paper, we propose a new methodology for models expressed through a timed extension of the Automata Networks [22] (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. The originality of our work consists in the integration of quantitative time delays directly in our learning approach. We show the benefits of such automatic approach on dynamical biological models, the DREAM4 datasets, a popular reverse-engineering challenge, in order to discuss the precision and the computational performances of our algorithm.

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

“…The first theoretical steps behind our method have been introduced in [26]. In the current paper, we have deepened our work to widen its applicability: after introducing a range of filters to curate the models resulting from our algorithms, we show its efficiency on new benchmarks coming from DREAM Challenges.…”

Learning Delays in Biological Regulatory Networks from Time Series Data

“…We have proposed a method called MoT-AN [14] to automatically model biological networks presented through the T-AN formalism. The models that we generate are consistent with given time series data.…”

“…The profit of our contribution lies in the fact that we overcome such limits, and we infer delays in a qualitative dynamical modeling of the network. MoT-AN method computes in [14] as many models as possible that satisfy on one hand the input (interaction graph and time series data) and on the other hand the dynamics semantics. But in these models, there exist some contradictions.…”

“…They show the advantage of such a framework for the analysis of mucus production in the bacterium Pseudomonas aeruginosa. The approach we presented in [14] is inherited from the idea that some models need to capture these timing features.…”