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Inferring causal molecular networks: empirical assessment through a community-based effort.

ABSTRACT: It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

SUBMITTER: Hill SM

PROVIDER: S-EPMC4854847 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Inferring causal molecular networks: empirical assessment through a community-based effort.

Hill Steven M SM Heiser Laura M LM Cokelaer Thomas T Unger Michael M Nesser Nicole K NK Carlin Daniel E DE Zhang Yang Y Sokolov Artem A Paull Evan O EO Wong Chris K CK Graim Kiley K Bivol Adrian A Wang Haizhou H Zhu Fan F Afsari Bahman B Danilova Ludmila V LV Favorov Alexander V AV Lee Wai Shing WS Taylor Dane D Hu Chenyue W CW Long Byron L BL Noren David P DP Bisberg Alexander J AJ Mills Gordon B GB Gray Joe W JW Kellen Michael M Norman Thea T Friend Stephen S Qutub Amina A AA Fertig Elana J EJ Guan Yuanfang Y Song Mingzhou M Stuart Joshua M JM Spellman Paul T PT Koeppl Heinz H Stolovitzky Gustavo G Saez-Rodriguez Julio J Mukherjee Sach S

Nature methods 20160222 4

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The ne ...[more]

PMID: 26901648

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Project description:BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

Dataset Information

Inferring causal molecular networks: empirical assessment through a community-based effort.

Publications

Inferring causal molecular networks: empirical assessment through a community-based effort.

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