We describe the development of a very large-scale causal, computable model of biology and its specific application in the identification of molecular cause and effect hypotheses of mechanisms underlying the effects of androgen stimulation in the LNCaP prostate carcinoma cell line. In contrast to previous LNCaP studies in which genes have been hierarchically clustered by their pattern of response to androgen, our causal reasoning methodology identifies possible explanations in terms of discrete and testable molecular mechanisms. We have inferred changes in cell proliferation and fatty-acid synthesis transcriptional control mechanisms based on gene expression changes in transcriptional targets of proteins such as RBI, E2F1,2,3, and SREBF1,2. Further analysis has identified multiple causal pathways linking the activity of the androgen receptor (AR) to these processes, providing succinct, testable hypotheses for subsequent experimentation. This study identified new targets for RNAi-mediated gene knockdowns to study androgen control of cell proliferation in prostate cancer. Causal Reasoning and modeling provides a valuable tool for the analysis of molecular profiling data, can identify distinct mechanisms of action and can be used to design experiments to probe those mechanisms. Causal Reasoning is effective in the study of cellular models of prostate cancer, and this systems approach is broadly applicable to identifying the molecular mechanisms of cancer
"Reverse Causal Reasoning (RCR)  was applied to identify statistically significant predictions of the activity states of biological mechanisms ("hypotheses") that are consistent with the measurements taken for a given systems biology data set. RCR on these four data sets identified upstream hypotheses which can explain the significant mRNA State Changes in each cell stress transcriptomic data set, enabling a deeper mechanistic understanding of the biological network perturbed by the experimental conditions, beyond the mere identification of significantly changing mRNAs [26,27]. These hypotheses represent mechanisms involved in the response to the various stressors used in the experiments. "
[Show abstract][Hide abstract] ABSTRACT: Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate.
We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung.
The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.
BMC Systems Biology 10/2011; 5(1):168. DOI:10.1186/1752-0509-5-168 · 2.44 Impact Factor
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