Abstract: BACKGROUND: Biological networks keep their functions robust against perturbations. Many previous studies through simulations or experiments have shown that feedback loop (FBL) structures play an important role in controlling the network robustness without fully explaining how they do it. Hence, there is a pressing need to more rigorously analyze the influence of FBL structures on network robustness. RESULTS: In this paper, I propose a novel node classification notion based on the FBL structures involved. More specifically, I classify a node as a no-FBL-in-upstream (NFU) or no-FBL-in-downstream (NFD) node if no feedback loop is involved with any upstream or downstream path of the node, respectively. Based on those definitions, I first prove that every NFU node is eventually frozen in Boolean dynamics. Thus, NFU nodes converge to a fixed value determined by the upstream source nodes. Second, I prove that a network is robust against an arbitrary state perturbation subject to a non-source NFD node. This implies that a network state eventually sustains the attractor despite a perturbation subject to a non-source NFD node. Inspired by this result, I further propose a perturbation-sustainable probability that indicates how likely a perturbation effect is to be sustained through propagations. I show that genes with a high perturbation-sustainable probability are likely to be essential, disease, and drug-target genes in large human signaling networks. CONCLUSION: Taken together, these results will promote understanding of the effects of FBL on network robustness in a more rigorous manner.

Abstract: BACKGROUND: The mode of action of a drug on its targets can often be classified as being positive (activator, potentiator, agonist, etc.) or negative (inhibitor, blocker, antagonist, etc.). The signed edges of a drug-target network can be used to investigate the combined mechanisms of action of multiple drugs on the ensemble of common targets. RESULTS: In this paper it is shown that for the signed human drug-target network the majority of drug pairs tend to have synergistic effects on the common targets, i.e., drug pairs tend to have modes of action with the same sign on most of the shared targets, especially for the principal pharmacological targets of a drug. Methods are proposed to compute this synergism, as well as to estimate the influence of the drugs on the side effect of another drug. CONCLUSIONS: Enriching a drug-target network with information of functional nature like the sign of the interactions allows to explore in a systematic way a series of network properties of key importance in the context of computational drug combinatorics.

Abstract: UNLABELLED: MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. AVAILABILITY AND IMPLEMENTATION: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems; source code is freely available under the GPL3 license at http://mocat.embl.de CONTACT: : bork@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: BACKGROUND: Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important. RESULTS: We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. CONCLUSIONS: A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery.

Abstract: Haizao Yuhu Decoction (HYD), a famous multi-component herbal formula, has been widely used to treat various thyroid-related diseases, including iodine-deficient goiter. Herb pair Thallus Sargassi Pallidi (HZ) and Radix Glycyrrhizae (GC), one of the so-called "eighteen antagonistic medicaments", contains in HYD. To explore pharmacological mechanisms of HYD acting on iodine-deficient goiter and to provide evidence for potential roles of herb pair HZ and GC in HYD, our genome-wide microarray detection and network analysis identified a list of goiter-related genes, mainly involved into the alterations in hypothalamus-pituitary-thyroid/gonad/growth axes. Then, the disease genes-drug genes interaction network illustrated the links between HYD regulating genes and goiter-related genes, and identified the candidate targets of HYD acting on goiter. Functionally, these candidate targets were closely correlated with thyroid hormone synthesis. Moreover, the potential regulating genes of herb pair HZ and GC were revealed to be crucial components in the pathway of thyroid hormone synthesis. The prediction results were all verified by following experiments based on goiter rats. Collectively, this integrative study combining microarray gene expression profiling, network analysis and experimental validations offers the convincing evidence that HYD may alleviate iodine-deficient goiter via regulating thyroid hormone synthesis, and explains the necessity of herb pair HZ and GC in HYD. Our work provides a novel and powerful means to clarify the mechanisms of action for multi-component drugs such as herbal formulae in a holistic way, which may improve drug development and applications.

Abstract: Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Abstract: An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast approximately 169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize approximately 10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Abstract: MOTIVATION: Adverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all >20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections. AVAILABILITY AND IMPLEMENTATION: The interface for the adverse event predictions for the >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: This analysis attempts to answer the question of whether similar molecules crystallize in a similar manner. An analysis of structures in the Cambridge Structural Database shows that the answer is yes - sometimes they do, particularly for single-component structures. However, one does need to define what we mean by similar in both cases. Building on this observation we then demonstrate how this correlation between shape similarity and packing similarity can be used to generate potential lattices for molecules with no known crystal structure. Simple intermolecular interaction potentials can be used to minimize these potential lattices. Finally we discuss the many limitations of this approach.

Abstract: The evolution of technologies to explore parasite biology at a detailed level has made significant advances in recent years, particularly with the development of omic-based strategies. Whilst extensive efforts have been made in the past to develop therapeutic and prophylactic control strategies for trematode parasites, only the therapeutic anthelmintic approach can be regarded as usable in clinical practice. Currently, there is no commercialised prophylactic strategy (such as vaccination) for protection of the definitive host against any trematode parasite. Since 2010 in particular, the integration of omic technologies, including liquid chromatography-mass spectrometry (LC-MS) and next-generation sequencing (NGS), has been increasingly reported in trematode-related studies. Both LC-MS and NGS facilitate a better understanding of the biology of trematodes and provide a promising route to identifying clinically important biological characteristics of parasitic trematodes. In this review, we focus on the application, advantages, and disadvantages of omic technologies (LC-MS and NGS) in trematode research within the past 5 years and explore the use and translation of the omic-based research results into practical tools to deal with infection.