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Systems biology for cancer
I. G. Khalil and C. Hill
Purpose of review
Significant insight can be gained into complex biologic
mechanisms of cancer via a combined computational and
experimental systems biology approach. This review
highlights some of the major systems biology efforts that
were applied to cancer in the past year.
Recent findings
Two main approaches to computational systems biology
are discussed: mechanistic dynamical simulations and
inferential data mining. Significant developments have
occurred in both areas. For example, mechanistic
simulations of the EGFR pathway are promoting
understanding of cancer, and Bayesian inference
approaches allow for the reconstruction of regulatory
networks. In addition, the article reports on advancements
in experimental systems biology for determining
protein–protein interactions and quantifying protein
expression to generate the necessary data for
computational modeling and inferential data mining.
Emerging approaches will further improve the ability to
bridge the gap between in vitro systems and in vivo
human biology. Technologies paving the way include in
vitro models that better reflect in vivo tumors,
microfabricated devices of human physiology, and
improved animal models.
Summary
An important challenge facing the field is how better to
translate in vitro discoveries to the clinic. Computational
systems biology approaches that use omic data to predict
biology along with novel experimental systems that better
represent human in vivo biology will prove useful in
bridging this gap. Although still early, the potential
application of systems biology and the future evolution of
the field will significantly affect understanding of cancer
disease mechanisms and the ability to devise effective
therapeutics.
Keywords
systems biology, computational biology, bioinformatics,
modeling, simulation, cancer pathways
Current Opinion in Oncology 2005, 17:44–48
Systems biology for cancer.pdf (257.85k) 看了您推荐的综述，是有关2004年肿瘤研究的system biology的最新进展，能否简单用中文介绍一下主要内容呢？: 建议看英文原版的吧！
hyslx wrote:
建议看英文原版的吧！
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