Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

Abstract

BackgroundHigh-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.ResultsFour complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.ConclusionsThe NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

3-D nasal cultures: Systems toxicological assessment of a candidate modified-risk tobacco product

“…The NPA methodology (Hoeng et al, 2014;Martin et al, 2012Martin et al, , 2014 aims to contextualize high dimensional transcriptomics data by combining gene expression (log2) fold-changes into fewer differential node values (one value for each node of a causal biological network model). The collection of causal biological networks used in the study was the human network suite CBN v1.3 (Boué et al, 2015).…”

“…A network is considered to be significantly impacted if three values (the confidence interval, *O, and K* statistics) are below 0.05. The detailed methodology was described previously (Hoeng et al, 2014;Martin et al, 2012.…”

Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems

“…available to perform such a task from the OpenBel consortium webpage (www.openbel.org) (14). In a more complex example, we recently used the set of CBN models (v 1.0) with internally developed algorithms and the Selventa Knowledgebase to compute the network perturbation amplitude (NPA) following simple experimental exposures (25). In this example, we were able to detect dynamic changes in the amplitude of perturbation in a network model describing the tumor necrosis factor-nuclear factor kappa B (TNF-NFkB) signaling pathway following TNF treatment of normal human bronchial epithelial (NHBE) cells in vitro as measured by gene expression data.…”

Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

“…As shown in Figure 1c, it represents a key ingredient of the five-step workflow for toxicity assessment and constitutes a concrete application of network-based systems toxicology [8,15,22]. Here we focus on the particular type of "causal networks" for which a mathematically and statistically sound methodology has been recently developed [23,24]. Given a suitably organized collection of causal networks selected for a priori relevant biological mechanisms, the structure of the associated NPA results can be seen as a complexity reduction scheme starting from large experimental transcriptomics data.…”