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From gene expression to gene regulatory networks in Arabidopsis thaliana

<28>Inferring gene networks from gene activity patterns is massively underdetermined and computationally demanding. Probabilistic model-learning approaches were hitherto less successful than competing methods. We show that learning probabilistic models incrementally overcomes these limitations. Significant impact is due to the predictive power, and automatically generated hypotheses. E.g., doi:10.1371/journal.pone.0026765 validated our hypothesis regarding two genes, leading to major progress in understanding the regulation of plant growth and culminating in the description of these genes as “master transcriptional regulators of chloroplast biogenesis” (doi/10.1104/pp.112.198705). Such successes contribute to the growing reliance of biological research on computational approaches and their integration in the scientific process.