Differential Effector Engagement by Oncogenic KRAS.

KRAS can bind numerous effector proteins, which activate different downstream signaling events. The best known are RAF, phosphatidylinositide (PI)-3' kinase, and RalGDS families, but many additional direct and indirect effectors have been reported. We have assessed how these effectors contribute to several major phenotypes in a quantitative way, using an arrayed combinatorial siRNA screen in which we knocked down 41 KRAS effectors nodes in 92 cell lines.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.

Enhancement of COPD biological networks using a web-based collaboration interface.

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development.

Recent advances in modeling languages for pathway maps and computable biological networks.

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards.

Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.

Background: Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action.

Results: We present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data.

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models.

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances.

A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue.

Exposure to environmental stressors such as cigarette smoke (CS) elicits a variety of biological responses in humans, including the induction of inflammatory responses. These responses are especially pronounced in the lung, where pulmonary cells sit at the interface between the body's internal and external environments. We combined a literature survey with a computational analysis of multiple transcriptomic data sets to construct a computable causal network model (the Inflammatory Process Network (IPN)) of the main pulmonary inflammatory processes.

Company Profile: Selventa, Inc.

Selventa, Inc. (MA, USA) is a biomarker discovery company that enables personalized healthcare. Originally founded as Genstruct, Inc.

Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population.

Chemogenetic analysis of human protein methyltransferases.

A survey of the human genome was performed to understand the constituency of protein methyltransferases (both protein arginine and lysine methyltransferases) and the relatedness of their catalytic domains. We identified 51 protein lysine methyltransferase proteins based on similarity to the canonical Drosophila Su(var)3-9, enhancer of zeste (E(z)), and trithorax (trx) domain. Disruptor of telomeric silencing-1-like, a known protein lysine methyltransferase, did not fit within the protein lysine methyltransferase family, but did group with the protein arginine methyltransferases, along with 44 other proteins, including the METTL and NOP2/Sun domain family proteins.

SIRT1 promotes differentiation of normal human keratinocytes.

Sir2 regulates lifespan in model organisms, which has stimulated interest in understanding human Sir2 homolog functions. The human Sir2 gene family comprises seven members (SIRT1-SIRT7). SIRT1, the human ortholog of the yeast Sir2 by closest sequence similarity, is a nicotinamide adenine dinucleotide (NAD(+))-dependent deacetylase with enzymatic properties indistinguishable from the yeast enzyme.

Applying a causal framework to system modeling.

The emerging field of systems biology represents a revolution in our ability to understand biology. Perhaps for the first time in history we have the capacity to pursue biological understanding using a computer-aided integrative approach in conjunction with classical reductionist approaches. Technology has given us not only the ability to identify and measure the individual molecules of life and the way they change, but also the power to study these molecules and their changes in the context of a big picture.

Failure of triazolam to alter circadian reentrainment rates in squirrel monkeys.

The short-acting benzodiazepine triazolam has been shown to cause phase-dependent phase shifts of the circadian activity rhythms of squirrel monkeys maintained in constant light. The present study sought to determine whether properly timed triazolam administration can also accelerate the reentrainment of circadian rhythms following phase shifts of the daily light-dark (LD) cycle. Circadian rhythms of telemetered body temperature and locomotor activity were recorded from squirrel monkeys exposed to an 8-h phase advance of the LD cycle, followed 16 days later by an 8-h phase delay.