Understanding cancer mechanisms through network dynamics

被引:32
作者
Cheng, Tammy M. K. [1 ]
Gulati, Sakshi [1 ]
Agius, Rudi [1 ]
Bates, Paul A. [1 ]
机构
[1] Canc Res UK London Res Inst, Biomol Modelling Lab, London WC2A 3LY, England
关键词
cancer; protein-protein interactions; dynamic network analysis; Boolean networks; ordinary differential equations; network modelling; PROTEIN INTERACTION NETWORK; GENE REGULATORY NETWORKS; MATHEMATICAL-MODEL; TUMOR-GROWTH; DRUG-RESISTANCE; IMMUNE-SYSTEM; PETRI-NET; ANTIANGIOGENIC THERAPY; MIXED IMMUNOTHERAPY; BOOLEAN NETWORKS;
D O I
10.1093/bfgp/els025
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Cancer is a complex, multifaceted disease. Cellular systems are perturbed both during the onset and development of cancer, and the behavioural change of tumour cells usually involves a broad range of dynamic variations. To an extent, the difficulty of monitoring the systemic change has been alleviated by recent developments in the high-throughput technologies. At both the genomic as well as proteomic levels, the technological advances in microarray and mass spectrometry, in conjunction with computational simulations and the construction of human interactome maps have facilitated the progress of identifying disease-associated genes. On a systems level, computational approaches developed for network analysis are becoming especially useful for providing insights into the mechanism behind tumour development and metastasis. This review emphasizes network approaches that have been developed to study cancer and provides an overview of our current knowledge of protein-protein interaction networks, and how their systemic perturbation can be analysed by two popular network simulation methods: Boolean network and ordinary differential equations.
引用
收藏
页码:543 / 560
页数:18
相关论文
共 196 条
[111]  
Landman KA, 2001, IMA J MATH APPL MED, V18, P131
[112]   Analysis of a cell-cycle specific model for cancer chemotherapy [J].
Ledzewicz, U ;
Schättler, H .
JOURNAL OF BIOLOGICAL SYSTEMS, 2002, 10 (03) :183-206
[113]  
Ledzewicz U, 2009, DEC CONTR 2009 HELD
[114]   Optimal and suboptimal protocols for a class of mathematical models of tumor anti-angiogenesis [J].
Ledzewicz, Urszula ;
Schattler, Heinz .
JOURNAL OF THEORETICAL BIOLOGY, 2008, 252 (02) :295-312
[115]   Antiangiogenic therapy in cancer treatment as an optimal control problem [J].
Ledzewicz, Urszula ;
Schattler, Heinz .
SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2007, 46 (03) :1052-1079
[116]   Optimal controls for a model with pharmacokinetics maximizing bone marrow in cancer chemotherapy [J].
Ledzewicz, Urszula ;
Schattler, Heinz .
MATHEMATICAL BIOSCIENCES, 2007, 206 (02) :320-342
[117]  
Lehne Benjamin, 2009, Human Genomics, V3, P291
[118]   Computational Modeling and Analysis of Insulin Induced Eukaryotic Translation Initiation [J].
Lequieu, Joshua ;
Chakrabarti, Anirikh ;
Nayak, Satyaprakash ;
Varner, Jeffrey D. .
PLOS COMPUTATIONAL BIOLOGY, 2011, 7 (11)
[119]  
Levasseur LM, 1998, CANCER RES, V58, P5749
[120]   Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension [J].
Li, Chen ;
Nagasaki, Masao ;
Ueno, Kazuko ;
Miyano, Satoru .
BMC SYSTEMS BIOLOGY, 2009, 3