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A nonlinear mixed-effects approach for the mechanistic interpretation of time-series transcriptomics data
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Mechanistic models are essential to unravel the molecular mechanisms driving cellular responses. However, the integration of high-throughput data with mechanistic knowledge is limited by the availability of scalable computational approaches able to disentangle biological and technical sources of variation. Results: We present an approach based on nonlinear mixed-effects modelling for the parameter estimation of large-scale mechanistic models from time-series transcriptomics data. It allows to factor out technical variability, to compensate for the limited number of conditions and time points by a population approach, and it incorporates mechanistic details to gain insight on the molecular causes of biological variability. We applied our approach for the biological interpretation of microarray and RNA-Seq gene expression profiles, with different levels of technical noise, but it is generalisable to numerous types of data. When integrated in a model describing the degradation kinetics of all cellular mRNAs, the data allowed to identify the targets of post-transcriptional regulatory mechanisms. Our approach paves the way for the interpretation of high-throughput biological data with more comprehensive mechanistic models. Availability: The Monolix script for estimation and output files are freely available at https://gitlab.inria. fr/tetienne/eccb_script, together with the microarray data. The RNA-Seq dataset is being prepared for publication (Roux et al., in preparation) and will be made available on demand upon acceptance of the article.
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