The biological networks in studying cell signal transduction complexity: The examples of sperm capacitation and of endocannabinoid system

被引:12
|
作者
Bernabo, Nicola [1 ]
Barboni, Barbara [1 ]
Maccarrone, Mauro [2 ,3 ]
机构
[1] Univ Teramo, Fac Vet Med, Piazza Aldo Moro 45, I-64100 Teramo, Italy
[2] Campus Bio Med Univ Rome, Ctr Integrated Res, I-00128 Rome, Italy
[3] Santa Lucia Fdn, European Ctr Brain Res CERC, I-00143 Rome, Italy
来源
关键词
Systems biology; Biological networks; Network topology; Signal transduction; Spermatozoa; Endocannabinoid system;
D O I
10.1016/j.csbj.2014.09.002
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Cellular signal transduction is a complex phenomenon, which plays a central role in cell surviving and adaptation. The great amount of molecular data to date present in literature, together with the adoption of high throughput technologies, on the one hand, made available to scientists an enormous quantity of information, on the other hand, failed to provide a parallel increase in the understanding of biological events. In this context, a new discipline arose, the systems biology, aimed to manage the information with a computational modeling-based approach. In particular, the use of biological networks has allowed the making of huge progress in this field. Here we discuss two possible application of the use of biological networks to explore cell signaling: the study of the architecture of signaling systems that cooperate in determining the acquisition of a complex cellular function (as it is the case of the process of activation of spermatozoa) and the organization of a single specific signaling systems expressed by different cells in different tissues (i.e. the endocannabinoid system). In both the cases we have found that the networks follow a scale free and small world topology, likely due to the evolutionary advantage of robustness against random damages, fastness and specific of information processing, and easy navigability. (C) 2014 Bernabo et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license
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收藏
页码:11 / 21
页数:11
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