Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability: Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.

A methodology to infer gene networks from spatial patterns of expression--an application to fluorescence in situ hybridization images.

The proper functional development of a multicellular organism depends on an intricate network of interacting genes that are expressed in accurate temporal and spatial patterns across different tissues. Complex inhibitory and excitatory interactions among genes control the territorial differences that explain specialized cell fates, embryo polarization and tissues architecture in metazoans. Given the nature of the regulatory gene networks, similarity of expression patterns can identify genes with similar roles. The inference and analysis of the gene interaction networks through complex network tools can reveal important aspects of the biological system modeled. Here we suggest an image analysis pipeline to quantify co-localization patterns in in situ hybridization images of Drosophila embryos and, based on these patterns, infer gene networks. We analyze the spatial dispersion of the gene expression and show the gene interaction networks for different developmental stages. Our results suggest that the inference of developmental networks based on spatial expression data is biologically relevant and represents a potential tool for the understanding of animal development.