Dataset Information

Inferring propagation paths for sparsely observed perturbations on complex networks.

ABSTRACT: In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space" (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.

SUBMITTER: Massucci FA

PROVIDER: S-EPMC5088640 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Inferring propagation paths for sparsely observed perturbations on complex networks.

Massucci Francesco Alessandro FA Wheeler Jonathan J Beltrán-Debón Raúl R Joven Jorge J Sales-Pardo Marta M Guimerà Roger R

Science advances 20161021 10

In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space" (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observation ...[more]

PMID: 27819038

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Project description:This dataset contains transcription profiles of TOP10 E.coli transformed with 85 rewired network plasmids first described in: Evolvability and hierarchy in rewired bacterial gene networks. Nature 452:840-5 (2008). The data are described and analysed in an accompanying paper Baumstark et al.,The propagation of perturbations in shuffled bacterial gene networks (Submitted, 2015). 260 raw data microarrays are provided in total, representing biological triplicates (a,b and c; independent colonies grown from the same transformation, under standard growth conditions; LB, 16h). This is done for each of 255 promoter-ORF constructs (e.g. appY-crp, etc.) and 5 control construct microarrays (empty plasmid; Co). Co controls are provided for comparison, to show the relative effect of the rewiring genetic perturbation. The final processed data compares the number of perturbed genes, comparing between the average expression values of each rewired construct and Co. Methods: The 85 rewiring plasmids (Isalan et al, 2008) were transformed into E. coli TOP10 cells and grown under standardised conditions: bacteria were freshly plated onto LB Agar plates (with 100 μg/ml Ampicillin and 50 μg/ml Streptomycin) and incubated overnight at 37oC to form colonies. Single colonies (<3 days old) were used to inoculate 2 ml of LB in 14 ml culture tubes, containing 100 μg/ml Ampicillin and 50 μg/ml Streptomycin. Constructs were grown for 16h at 37oC, at 220 rpm in an orbital shaker. 10 μg of extracted total bacterial RNA (integrity number > 7.0) was used with Affymetrix GeneChip E. coli Genome 2.0 Arrays. Key to names: RAW DATA samples 1-260 _Co_1a.CEL = Control Co, colony a _Co_1b.CEL = Control Co, colony b etc. appY_O_30a.CEL = appY-promoter only, colony a appY_O_30b.CEL = appY-promoter only, colony b etc. appY_crp_34a.CEL = rewired construct, appY-promoter expressing crp ORF, colony a appY_crp_34b.CEL = rewired construct, appY-promoter expressing crp ORF, colony b appY_crp_34b.CEL = rewired construct, appY-promoter expressing crp ORF, colony c etc. PROCESSED DATA sample 261: probe_set_expression_value_norm_all - normalised data for all samples, for all E. coli K12 genes sample 262: probe_set_expression_value_norm_MG1655 - normalised data for all samples, for E. coli MG1655 subset of genes

Dataset Information

Inferring propagation paths for sparsely observed perturbations on complex networks.

Publications

Inferring propagation paths for sparsely observed perturbations on complex networks.

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