Selventa, One Alewife Center, Cambridge, MA 02140, USA.
Toxicol Appl Pharmacol. 2013 Nov 1;272(3):863-78. doi: 10.1016/j.taap.2013.07.007. Epub 2013 Aug 8.
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.
暴露于治疗性药物或环境毒物等生物活性物质会在多个层面上影响生物系统，影响单个分子、信号通路和整体细胞过程。要从产生的系统响应中得出机制见解，需要将实验测量与系统和其中相互作用的分子的先验知识相结合。我们开发了一种新的基于系统生物学的方法，该方法利用机制网络模型和转录组数据来定量评估活性物质暴露对生物的影响。首先构建层次化的网络模型，为研究分子、通路和过程层面暴露的影响提供一个连贯的框架。然后，我们使用新的和以前发表的实验验证了我们的方法。对于具有简单暴露的体外系统和具有复杂暴露的体内系统，我们的方法能够再现与预期或测量表型匹配的已知生物学反应。此外，对于许多被评估的机制效应的定量结果与实验终点数据一致，这进一步客观地证实了该方法的有效性。我们得出的结论是，我们的方法以客观、系统和可量化的方式评估暴露对生物的影响，能够计算给定活性物质或混合物的系统范围和全面的机制生物学影响度量。我们的结果表明，从药物开发到消费品测试和环境影响分析等人类疾病研究的各个领域都可以从使用这种方法中受益。
