Drug target inference through pathway analysis of genomics data.

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues in this field, covering methodological developments, their pros and cons, as well as future research directions.

FacPad: Bayesian sparse factor modeling for the inference of pathways responsive to drug treatment.

MOTIVATION: It is well recognized that the effects of drugs are far beyond targeting individual proteins, but rather influencing the complex interactions among many relevant biological pathways. Genome-wide expression profiling before and after drug treatment has become a powerful approach for capturing a global snapshot of cellular response to drugs, as well as to understand drugs' mechanism of action. Therefore, it is of great interest to analyze this type of transcriptomic profiling data for the identification of pathways responsive to different drugs. However, few computational tools exist for this task. RESULTS: We have developed FacPad, a Bayesian sparse factor model, for the inference of pathways responsive to drug treatments. This model represents biological pathways as latent factors and aims to describe the variation among drug-induced gene expression alternations in terms of a much smaller number of latent factors. We applied this model to the Connectivity Map data set (build 02) and demonstrated that FacPad is able to identify many drug-pathway associations, some of which have been validated in the literature. Although this method was originally designed for the analysis of drug-induced transcriptional alternation data, it can be naturally applied to many other settings beyond polypharmacology. AVAILABILITY AND IMPLEMENTATION: The R package 'FacPad' is publically available at: http://cran.open-source-solution.org/web/packages/FacPad/.

iFad: an integrative factor analysis model for drug-pathway association inference.

MOTIVATION: Pathway-based drug discovery considers the therapeutic effects of compounds in the global physiological environment. This approach has been gaining popularity in recent years because the target pathways and mechanism of action for many compounds are still unknown, and there are also some unexpected off-target effects. Therefore, the inference of drug-pathway associations is a crucial step to fully realize the potential of system-based pharmacological research. Transcriptome data offer valuable information on drug-pathway targets because the pathway activities may be reflected through gene expression levels. Hence, it is of great interest to jointly analyze the drug sensitivity and gene expression data from the same set of samples to investigate the gene-pathway-drug-pathway associations. RESULTS: We have developed iFad, a Bayesian sparse factor analysis model to jointly analyze the paired gene expression and drug sensitivity datasets measured across the same panel of samples. The model enables direct incorporation of prior knowledge regarding gene-pathway and/or drug-pathway associations to aid the discovery of new association relationships. We use a collapsed Gibbs sampling algorithm for inference. Satisfactory performance of the proposed model was found for both simulated datasets and real data collected on the NCI-60 cell lines. Our results suggest that iFad is a promising approach for the identification of drug targets. This model also provides a general statistical framework for pathway-based integrative analysis of other types of -omics data. AVAILABILITY: The R package 'iFad' and real NCI-60 dataset used are available at http://bioinformatics.med.yale.edu/group.

COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method.

MOTIVATION: The identification of condition specific sub-networks from gene expression profiles has important biological applications, ranging from the selection of disease-related biomarkers to the discovery of pathway alterations across different phenotypes. Although many methods exist for extracting these sub-networks, very few existing approaches simultaneously consider both the differential expression of individual genes and the differential correlation of gene pairs, losing potentially valuable information in the data. RESULTS: In this article, we propose a new method, COSINE (COndition SpecIfic sub-NEtwork), which employs a scoring function that jointly measures the condition-specific changes of both 'nodes' (individual genes) and 'edges' (gene-gene co-expression). It uses the genetic algorithm to search for the single optimal sub-network which maximizes the scoring function. We applied COSINE to both simulated datasets with various differential expression patterns, and three real datasets, one prostate cancer dataset, a second one from the across-tissue comparison of morbidly obese patients and the other from the across-population comparison of the HapMap samples. Compared with previous methods, COSINE is more powerful in identifying truly significant sub-networks of appropriate size and meaningful biological relevance. AVAILABILITY: The R code is available as the COSINE package on CRAN: http://cran.r-project.org/web/packages/COSINE/index.html.

Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch.

Design and synthesis of basic functional circuits are the fundamental tasks of synthetic biologists. Before it is possible to engineer higher-order genetic networks that can perform complex functions, a toolkit of basic devices must be developed. Among those devices, sequential logic circuits are expected to be the foundation of the genetic information-processing systems. In this study, we report the design and construction of a genetic sequential logic circuit in Escherichia coli. It can generate different outputs in response to the same input signal on the basis of its internal state, and 'memorize' the output. The circuit is composed of two parts: (1) a bistable switch memory module and (2) a double-repressed promoter NOR gate module. The two modules were individually rationally designed, and they were coupled together by fine-tuning the interconnecting parts through directed evolution. After fine-tuning, the circuit could be repeatedly, alternatively triggered by the same input signal; it functions as a push-on push-off switch.