Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico.
Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico.
Semin Cancer Biol. 2015 Feb;30:79-87. doi: 10.1016/j.semcancer.2014.04.003. Epub 2014 Apr 18.
Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century.
鉴于癌症的多因素性质，揭示其代谢改变并评估其影响是生物医学科学中的一个主要挑战，这将有助于优化设计个性化治疗方案。高通量技术的进步为监测不同生物水平的活性提供了宝贵的机会，并阐明了癌症在药物治疗下如何起源、演变和响应。为此，研究人员面临两个基本问题：如何解释高通量数据，以及这些信息如何有助于为患者制定个性化治疗方案。系统生物学中的各种方案已经被提出，通过利用计算建模和高通量数据来描述与癌症相关的表型状态。这些理论方案的区别在于它们所代表的生物学机制的复杂性水平以及用于模拟它们的计算方法。值得注意的是，这些理论方法为探索癌症细胞中观察到的一些独特代谢机制提供了一个合适的框架，例如沃伯格效应。在这篇综述中，我们重点介绍了其中一些方法的概述，这些方法的应用和整合将在从癌症研究的局部结论到全局结论的转变中至关重要。我们相信，需要多学科方法来构建整合和个性化医学的基础，这一直是本世纪医学的一项基本任务。
