Abstract
BackgroundNetwork inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.ResultsWe present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.ConclusionsDynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users.
Highlights
Network inference deals with the reconstruction of molecular networks from experimental data
We propose a new method for the identification of kinetic models of signaling networks from such data, dynamic probabilistic Boolean threshold networks (D-PBTN), which can treat multiple, combinatorial interventions as well as incomplete observations
We evaluate the performance of our method on simulated data, and study its behavior on networks of different sizes, with different amounts of available data and different levels of noise, and compare results against PBTN [35], deterministic effects propagation networks (DEPN) [34] and Bayesian Directed Acyclic Graph Learning tool (BDAGL), a dynamic Bayesian network based approach [40,41]
Summary
Network inference deals with the reconstruction of molecular networks from experimental data. There are a number of successful applications of network inference approaches to elucidate cellular signaling pathways, including meta-approaches integrating different methods [29]. For example, Sachs et al used Bayesian networks to reconstruct cellular protein signaling networks from protein phosphorylation measurements [5]. Ciaccio et al used Bayesian networks and two different mutual-information based approaches to infer signaling networks downstream of the EGF receptor [32]. Other approaches include nested effects models (NEM) [33], deterministic effects propagation networks (DEPN) [34] or probabilistic Boolean threshold networks (PBTN) [35], and have been applied, for example, to reconstruct signaling networks in the ERBB pathway, or in the innate immune response to infection
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