Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis’, ‘Mechanistic Networks’, ‘Causal Network Analysis’ and ‘Downstream Effects Analysis’ are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.

/pdf/causal-analysis-approaches-in-ingenuity-pathway-analysis-5fgp4yj97u.pdf

Causal analysis approaches in Ingenuity Pathway Analysis

CRISPR/Cas9 has revolutionized our ability to engineer genomes and conduct genome-wide screens in human cells1–3. Whereas some cell types are amenable to genome engineering, genomes of human pluripotent stem cells (hPSCs) have been difficult to engineer, with reduced efficiencies relative to tumour cell lines or mouse embryonic stem cells3–13. Here, using hPSC lines with stable integration of Cas9 or transient delivery of Cas9-ribonucleoproteins (RNPs), we achieved an average insertion or deletion (indel) efficiency greater than 80%. This high efficiency of indel generation revealed that double-strand breaks (DSBs) induced by Cas9 are toxic and kill most hPSCs. In previous studies, the toxicity of Cas9 in hPSCs was less apparent because of low transfection efficiency and subsequently low DSB induction
 3
 . The toxic response to DSBs was P53/TP53-dependent, such that the efficiency of precise genome engineering in hPSCs with a wild-type P53 gene was severely reduced. Our results indicate that Cas9 toxicity creates an obstacle to the high-throughput use of CRISPR/Cas9 for genome engineering and screening in hPSCs. Moreover, as hPSCs can acquire P53 mutations
 14
 , cell replacement therapies using CRISPR/Cas9-enginereed hPSCs should proceed with caution, and such engineered hPSCs should be monitored for P53 function.

/pdf/p53-inhibits-crispr-cas9-engineering-in-human-pluripotent-5dbbwoldxu.pdf

p53 inhibits CRISPR–Cas9 engineering in human pluripotent stem cells

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

In silico methods for drug repurposing and pharmacology

We consider the point-to-point (approximate) shortest-path query problem, which is the following generalization of the classical single-source (SSSP) and all-pairs shortest-path (APSP) problems: we are first presented with a network (graph). A so-called preprocessing algorithm may compute certain information (a data structure or index) to prepare for the next phase. After this preprocessing step, applications may ask shortest-path or distance queries, which should be answered as fast as possible.Due to its many applications in areas such as transportation, networking, and social science, this problem has been considered by researchers from various communities (sometimes under different names): algorithm engineers construct fast route planning methods; database and information systems researchers investigate materialization tradeoffs, query processing on spatial networks, and reachability queries; and theoretical computer scientists analyze distance oracles and sparse spanners. Related problems are considered for compact routing and distance labeling schemes in networking and distributed computing and for metric embeddings in geometry as well.In this survey, we review selected approaches, algorithms, and results on shortest-path queries from these fields, with the main focus lying on the tradeoff between the index size and the query time. We survey methods for general graphs as well as specialized methods for restricted graph classes, in particular for those classes with arguable practical significance such as planar graphs and complex networks.

/pdf/shortest-path-queries-in-static-networks-t3dlcej7at.pdf

Shortest-path queries in static networks

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.

/pdf/drug-target-prediction-and-repositioning-using-an-integrated-2d3413oak7.pdf

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

Motivation: The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges.

Methods: We integrate available biological knowledge by constructing a network of molecular interactions of a specific kind: causal interactions. The resulting causal graph can be queried to suggest molecular hypotheses that explain the variations observed in a high-throughput gene expression experiment. We show that a simple scoring function can discriminate between a large number of competing molecular hypotheses about the upstream cause of the changes observed in a gene expression profile. We then develop an analytical method for computing the statistical significance of each score. This analytical method also helps assess the effects of random or adversarial noise on the predictive power of our model.

Results: Our results show that the causal graph we constructed from known biological literature is extremely robust to random noise and to missing or spurious information. We demonstrate the power of our causal reasoning model on two specific examples, one from a cancer dataset and the other from a cardiac hypertrophy experiment. We conclude that causal reasoning models provide a valuable addition to the biologist's toolkit for the interpretation of gene expression data.

Availability and implementation: R source code for the method is available upon request.

Contact: daniel.ziemek@pfizer.com

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/causal-reasoning-on-biological-networks-2j5733f62h.pdf

Causal reasoning on biological networks

Over the past decade gene expression data sets have been generated at an increasing pace. In addition to ever increasing data generation, the biomedical literature is growing exponentially. The PubMed database (Sayers et al., 2010) comprises more than 20 million citations as of October 2010. The goal of our method is the prediction of putative upstream regulators of observed expression changes based on a set of over 400,000 causal relationships. The resulting putative regulators constitute directly testable hypotheses for follow-up.

Causal reasoning on biological networks: interpreting transcriptional changes

Motivation: Models of regulatory networks become more difficult to construct and understand as they grow in size and complexity. Modelers naturally build large models from smaller components that each represent subsets of reactions within the larger network. To assist modelers in this process, we present model aggregation, which defines models in terms of components that are designed for the purpose of being combined.

Results: We have implemented a model editor that incorporates model aggregation, and we suggest supporting extensions to the Systems Biology Markup Language (SBML) Level 3. We illustrate aggregation with a model of the eukaryotic cell cycle ‘engine’ created from smaller pieces.

Availability: Java implementations are available in the JigCell Aggregation Connector. See http://jigcell.biol.vt.edu.

Contact: [email protected]

/pdf/model-aggregation-a-building-block-approach-to-creating-3zvpssye9y.pdf

Model aggregation: a building-block approach to creating large macromolecular regulatory networks

Today's macromolecular regulatory network models are small compared to the amount of information known about a particular cellular pathway, in part because current modeling languages and tools are unable to handle significantly larger models. Thus, most pathway modeling work today focuses on building small models of individual pathways since they are easy to construct and manage. The hope is someday to put these pieces together to create a more complete picture of the underlying molecular machinery. While efforts to make large models benefit from reusing existing components, unfortunately, there currently exists little tool or representational support for combining or composing models. We have identified four distinct modeling processes related to model composition: fusion, composition, aggregation, and flattening. We present concrete proposals for implementing all four processes in the context of the Systems Biology Markup Language (SBML).

/pdf/the-role-of-composition-and-aggregation-in-modeling-474sswx9c7.pdf

The role of composition and aggregation in modeling macromolecular regulatory networks

We describe procedures for converting a macromolecular regulatory model from the most common deterministic formulation to one suitable for stochastic simulation. To avoid error, we seek to automate as much of the process as possible. However, deterministic models often omit key information necessary to a stochastic formulation. In this paper we introduce how we implement conversion in the JigCell modeling environment. Our tool makes it easier for the modeler to include complete details. Stochastic simulations are known for being computationally intensive, and thus require high performance computing facilities to be practical. We provide the first stochastic simulation results for realistic cell cycle models, using Virginia Tech's System X supercomputer.

Converting macromolecular regulatory models from deterministic to stochastic formulation

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