Ensemble framework to infer large-scale causal gene regulatory networks from transcriptomic data

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It is now established that complex biological phenotypes are not governed by single genes but instead by networks of interacting genes and gene products. As a consequence, deciphering the structure of the gene regulatory network (GRN) is crucial to further our understanding of fundamental processes in human cells. However, the mapping of molecular interactions in the intracellular realm remains the bottleneck in the pipeline to produce biological knowledge from high-throughput biological data. ******Recent advances in bioinformatics and high-performance computing made it possible to infer undirected large-scale regulatory networks from collections of transcriptomic data. However very few network inference methods can infer the directionality (causality) of predicted gene interactions, despite this being key in the process of better interpreting GRNs. Another challenge when inferring large-scale GRNs consists in quantitatively assessing their validity. Popular, however weak, validation procedures include (i) showing that the algorithm under study performs well on simulated datasets for which the true underlying network is known by construction; (ii) using incomplete `gold standard' datasets, such as known transcription factors and their targets, which poorly recapitulate the interactions that can be inferred from transcriptomic data; and (iii) using low-throughput laboratory experiments to validate a few predicted interactions, which represent only a very small and potentially biased part of the inferred GRN.******Our research program addresses these issues from several angles. First we will build on our recent contributions in the field of network biology to develop new machine learning methods enabling inference of large-scale causal GRNs. These methods will be novel in their implementation of an ensemble approach for network and causality inference, and their integration of priors extracted from the biomedical literature. Second we will extend our recently published validation framework to quantitatively assess and compare our new methods with state-of-the-art network inference techniques. Third we will develop a computational platform to allow biologists to leverage our computational tools to adequately map their genes or pathways of interest into large GRNs.******To achieve these objectives, I will rely on my close collaborations with renowned computer scientists, bioinformaticians, and biologists, This will not only ensure that my methods are applied to real and new data, but also that the development of these methods are being continuously evaluated in order to impact the larger community of researchers involved in biological and medical studies.