Networks - Understanding Networks of Life Ankit Patel CPSC...

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Ankit Patel  CPSC 565: Networks Dr. Gustavo Caetano-Anolles April 12, 2009  Understanding Networks of Life There are networks in everything we do and see. Networks help us understand  relationships between objects and events. In biology, networks help us understating  linkages between functions, links to our past and more importantly, systems as a whole.  We can reconstruct the unknown based on our known networks.  Networks helps place the big picture of systems; however, they are complex. They  have structural complexity, where the diagrams have many tangles. They can also  change over time, and over connections may serve to be more important than another.  Despite this, the increasing availability of topological data on large networks, aided by  the computerization of data acquisition, had led to great advances in our understanding  of the generic aspects of network structure and development.  Many complex systems have a great degree of tolerance against error. Complex  communication networks display a surprising degree of robustness: although key  components regularly malfunction, local failures rarely lead to the loss of the global- information carrying ability of networks (Albert 2000). There are two basic types of  networks: scale-free networks and random/exponential networks. Scale free networks  are very large real networks (millions or billions of nodes and edges) and they occur in  nature, society, economy and technology. Random networks were created under the  assumption that networks have a fixed number of nodes, are connected by random  edges and most nodes have approximately equal number of attached edges. These  networks seem to have the same topological structures, mathematical structure and  behavioral properties.  Furthermore, the Albert group found that many systems that belong to the  inhomogeneous network or scale-free networks, decay as a power law. They then  looked at errors and attacks to networks, whereas, ‘errors’ are randomly broken vertices  and ‘attacks’ are deliberately broken vertices, to examine its robustness on the  networks. They found that scale-free networks display a surprisingly high degree of  tolerance against random failures, a property not shared by their exponential  counterparts. This robustness is probably the basis of the error tolerance of many  complex systems.   When this was applied to metabolic networks, the researchers found that 
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This note was uploaded on 06/11/2009 for the course CPSC 567 taught by Professor Gustavocaeteno-anolles during the Spring '09 term at University of Illinois at Urbana–Champaign.

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Networks - Understanding Networks of Life Ankit Patel CPSC...

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