Systems biology;
Molecular mechanistic models;
Physiological models;
Top-down systems biology;
Bottom-up systems biology;
MODEL;
SIMULATION;
GENERATION;
PREDICTION;
DISCOVERY;
NETWORKS;
DATABASE;
ERBB3;
D O I:
10.1016/j.bmcl.2012.12.031
中图分类号:
R914 [药物化学];
学科分类号:
100701 ;
摘要:
Systems biology aims to provide a holistic and in many cases dynamic picture of biological function and malfunction, in case of disease. Technology developments in the generation of genome-wide datasets and massive improvements in computer power now allow to obtain new insights into complex biological networks and to copy nature by computing these interactions and their kinetics and by generating in silico models of cells, tissues and organs. The expectations are high that systems biology will pave the way to the identification of novel disease genes, to the selection of successful drug candidates-that do not fail in clinical studies due to toxicity or lack of human efficacy-and finally to a more successful discovery of novel therapeutics. However, further research is necessary to fully unleash the potential of systems biology. Within this review we aim to highlight the most important and promising top-down and bottom-up systems biology applications in drug discovery. (C) 2012 Elsevier Ltd. All rights reserved.
机构:
Shanghai Univ, Inst Syst Biol, Shanghai, Peoples R China
Shanghai Univ Sci & Technol, Sch Business, Shanghai 201800, Peoples R China
Shanghai Univ, Sch Commun & Informat Engn, Shanghai, Peoples R ChinaShanghai Univ, Inst Syst Biol, Shanghai, Peoples R China
Wu, Zikai
Zhao, Xing-Ming
论文数: 0引用数: 0
h-index: 0
机构:
Shanghai Univ, Inst Syst Biol, Shanghai, Peoples R ChinaShanghai Univ, Inst Syst Biol, Shanghai, Peoples R China
Zhao, Xing-Ming
Chen, Luonan
论文数: 0引用数: 0
h-index: 0
机构:
Chinese Acad Sci, Shanghai Inst Biol Sci, SIBS Novo Nordisk Translat Res Ctr PreDiabet, Key Lab Syst Biol, Shanghai, Peoples R China
Natl Inst Adv Ind Sci & Technol, Computat Biol Res Ctr, Tokyo, JapanShanghai Univ, Inst Syst Biol, Shanghai, Peoples R China