NDLI: Structure versus function: a topological perspective on immune networks

Content Provider	Springer Nature Link
Author	Hart, Emma Bersini, Hugues Santos, Francisco
Copyright Year	2009
Abstract	Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex network theory, particularly in the area of small-world and scale-free topologies. Computational studies which attempt to understand the structure–function relationship usually proceed by defining a representation of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the networks that can arise—furthermore, we show that although simple topologies can be produced via representation and affinity measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting scale-free functionality. In this paper, we introduce the concept of the potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free and regular topologies, finding that a key immunological property—tolerance—is promoted by bi-partite and heterogeneous topologies. The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial immune systems.
Starting Page	603
Ending Page	624
Page Count	22
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	9
Issue Number	3
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2009-06-17
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Topology Complex networks Artificial immune systems Tolerance Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Processor Architectures Evolutionary Biology Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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