Aksenov Sergej V, Church Bruce, Dhiman Anjali, Georgieva Anna, Sarangapani Ramesh, Helmlinger Gabriel, Khalil Iya G
Gene Network Sciences, Inc. 31 Dutch Mill Road, Ithaca, NY 14850, USA.
FEBS Lett. 2005 Mar 21;579(8):1878-83. doi: 10.1016/j.febslet.2005.02.012.
An important challenge facing researchers in drug development is how to translate multi-omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.
药物研发领域的研究人员面临的一个重要挑战是如何将多组学测量结果转化为生物学见解,以助力药物通过临床试验。计算生物学策略是一种很有前景的方法,可用于系统地捕捉特定药物对复杂分子网络和人体生理学的影响。本文讨论了一种双管齐下的策略,该策略用于从大规模多组学测量中推断生物学相互作用,并通过对信号通路、细胞以及器官和组织水平模型进行机制动力学模拟来考虑已知生物学知识。这些方法已经在通过提供一个合理且系统的计算框架来推动药物研发方面发挥作用。