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

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• 作者：Ty M. Thomson ; Alain Sewer ; Florian Martin ; Vincenzo Belcastro ; Brian P. Frushour ; Stephan Gebel ; Jennifer Park ; Walter K. Schlage ; Marja Talikka ; Dmitry M. Vasilyev ; Jurjen W. Westra ; Julia Hoeng ; Manuel C. Peitsch
• 关键词：Quantification ; Biological impact ; Bioactive substance ; Transcriptome ; Network model ; Mechanisms
• 刊名：Toxicology and Applied Pharmacology
• 出版年：2013
• 出版时间：1 November, 2013
• 年：2013
• 卷：272
• 期：3
• 页码：863-878
• 全文大小：879 K

文摘

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. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology.