Dexter Pratt

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

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 reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693.

BackgroundInappropriate activation of AKT signaling is a relatively common occurrence in human tumors, and can be caused by activation of components of, or by loss or decreased activity of inhibitors of, this signaling pathway. A novel, pan AKT kinase inhibitor, GSK690693, was developed in order to interfere with the inappropriate AKT signaling seen in these human malignancies. Causal network modeling is a systematic computational analysis that identifies upstream changes in gene regulation that can serve as explanations for observed changes in gene expression. In this study, causal network modeling is employed to elucidate mechanisms of action of GSK690693 that contribute to its observed biological effects. The mechanism of action of GSK690693 was evaluated in multiple human tumor cell lines from different tissues in 2-D cultures and xenografts using RNA expression and phosphoproteomics data. Understanding the molecular mechanism of action of novel targeted agents can enhance our understanding of various biological processes regulated by the intended target and facilitate their clinical development.ResultsCausal network modeling on transcriptomic and proteomic data identified molecular networks that are comprised of activated or inhibited mechanisms that could explain observed changes in the sensitive cell lines treated with GSK690693. Four networks common to all cell lines and xenografts tested were identified linking GSK690693 inhibition of AKT kinase activity to decreased proliferation. These networks included increased RB1 activity, decreased MYC activity, decreased TFRC activity, and increased FOXO1/FOXO3 activity.ConclusionAKT is involved in regulating both cell proliferation and apoptotic pathways; however, the primary effect with GSK690693 appears to be anti-proliferative in the cell lines and xenografts evaluated. Furthermore, these results indicate that anti-proliferative responses to GSK690693 in either 2-D culture or xenograft models may share common mechanisms within and across sensitive cell lines.