How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Quantifying biosynthetic network robustness across the human oral microbiome

Metabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-cultured Saccharibacteria (TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.https://www.biorxiv.org/content/10.1101/392621v1First author draf

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001