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

Nicola Bernabò, Barbara Barboni, Mauro Maccarrone

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)11-21
Number of pages11
JournalComputational and Structural Biotechnology Journal
Volume11
Issue number18
DOIs
Publication statusPublished - Aug 1 2014

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Keywords

  • Biological networks
  • Endocannabinoid system
  • Network topology
  • Signal transduction
  • Spermatozoa
  • Systems biology

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Biophysics
  • Structural Biology
  • Genetics
  • Computer Science Applications

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