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

Abstract

BackgroundGene 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.ResultsWe present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These small directed networks are generated from a knowledge base of literature-curated qualitative biological cause-and-effect relationships expressed as a network. The small mechanism networks are evaluated as hypotheses to explain observed differential measurements. We provide a simple implementation of this methodology, Whistle, specifically geared towards the analysis of gene expression data and using prior knowledge expressed in Biological Expression Language (BEL). We present the Whistle analyses for three transcriptomic data sets using a publically available knowledge base. The mechanisms inferred by Whistle are consistent with the expected biology for each data set.ConclusionsReverse Causal Reasoning yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets. This reverse engineering algorithm provides an evidence-driven approach to the development of models of disease, drug action, and drug toxicity.

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

“…The inherent computability conferred by encoding networks in BEL allows them to be interrogated and traversed to explore relationships and identify pathways that connect biological entities. We have described previously the development of statistical approaches to predict and interpret biological hypotheses from high-dimensional data sets such as those derived from microarray profiling studies (14). available to perform such a task from the OpenBel consortium webpage (www.openbel.org) (14).…”

“…We have described previously the development of statistical approaches to predict and interpret biological hypotheses from high-dimensional data sets such as those derived from microarray profiling studies (14). 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).…”

“…3. In the network verification and extension step, RCR was used to mine molecular profiling data in the context of the Selventa Knowledgebase (14). RCR is a reverse engineering algorithm to identify biological mechanisms that are statistically significant explanations for differential measurements in a molecular profiling data set.…”

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

“…RCR has been used previously to predict upstream regulators from transcriptomic data 8 . Mechanisms that were predicted by RCR to be active and that were not already incorporated in the non-diseased networks were vetted on an individual basis to locate supporting literature for their potential involvement in COPD pathogenesis.…”

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

“…Clearly, these edges allow the transcriptomics data to connect to the mechanistic networks, and the NPA calculations will consist of the experimental differential gene expressions "propagating through the networks" to obtain the corresponding node-and network-level perturbations. In our assessment applications, we licensed the Selventa Knowledgebase to get a good coverage of the nodes of the causal network collection in terms of transcriptional footprints [28]. Other options are possible: the small "BEL corpus" derived from the Selventa Knowledgebase [29], the networks contained in our publications [23,24,30], or the commercial IPA® "causal analysis" knowledgebase [31].…”