Abstract
A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network.Due to the static character of the challenge data, we tackled the problem via a sparse Gaussian Markov Random Field, which relates network topology with the covariance inverse generated by the gene measurements. As for the computations, we used the Graphical Lasso algorithm which provided a large range of candidate network topologies. The main task was to select the optimal network topology and for that, different model selection criteria were explored. The selected networks were compared with the golden standards and the results ranked using the scoring metrics applied in the challenge, giving a better insight in our submission and the way to improve it.Our approach provides an easy statistical and computational framework to infer gene regulatory networks that is suitable for large networks, even if the number of the observations (perturbations) is greater than the number of variables (genes).
Highlights
Traditional methods where one gene or one chemical reaction was studied at a time, have taken step to more sophisticated ones, which try to elucidate the complex machinery connecting all the biochemical reactions happening in a cell
Given the steady-state nature of the multifactorial sub-challenge data, we focused on Gaussian Markov Random Field theory [6] that leads to the estimation of undirected graphical models [7]
The goal of the multifactorial sub-challenge was to reverse engineer five gene regulatory networks from training data consisting of steady-states levels of variation of the original networks, obtained after applying multifactorial perturbations to the system
Summary
Traditional methods where one gene or one chemical reaction was studied at a time, have taken step to more sophisticated ones, which try to elucidate the complex machinery connecting all the biochemical reactions happening in a cell. The DREAM project [1,2], acronym for Dialogue on Reverse Engineering Assessment and Methods, is an initiative that tries to motivate the systems biology community to investigate and develop methodologies that translate biochemical processes into gene regulatory networks, by challenging the participants to infer network structure from some given in silico gene expression data sets. This in silico data were generated by the GeneNetWeaver tool version 2.0 [3] based on the ideas in [4]. The multifactorial subchallenge, posted in the DREAM4 initiative web page [5] aimed to reverse engineer five gene regulatory networks of size 100 with an experimental scenario assuming that extensive knockout/ knockdown or time series experiments, could not be performed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
