Ji Zhiwei, Yan Ke, Li Wenyang, Hu Haigen, Zhu Xiaoliang
School of Information & Electronic Engineering, Zhejiang Gongshang University, 18 Xuezheng Road, Hangzhou 310018, China.
College of Information Engineering, China Jiliang University, 258 Xueyuan Street, Hangzhou 310018, China.
Biomed Res Int. 2017;2017:5958321. doi: 10.1155/2017/5958321. Epub 2017 Mar 13.
The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
癌症进展所涉及的生物学过程和分子功能，对于生物学家和临床医生来说，仍然难以理解。高通量技术的最新发展促使系统生物学为复杂疾病建立更精确的模型。计算模型和数学模型正逐渐被用于帮助我们理解高通量实验技术产生的组学数据。在系统生物学中使用计算模型，使我们能够探索复杂疾病的发病机制，加深对潜在分子机制的理解，并促进治疗策略的优化和新药的发现。目前，迫切需要弥合高通量技术发展与癌症研究中生物过程的系统建模之间的差距。在这篇综述中，我们首先研究了不同尺度下生物系统的几种典型数学建模方法，并深入分析了它们的特点、优势、应用和局限性。接下来，总结了系统建模的三个潜在研究方向。总之，本综述提供了系统生物学中使用计算建模方法的重要解决方案的最新情况。
