Abstract

Complex network theory has recently been used in System biology, but so far the resulting networks have only been analyzed statically. In this work, the biochemical reaction network (BRN) model is proposed based on the complex network theory and the dynamics of the network is analyzed on the molecular-scale. Given the initial ate and the evolution rules of the biochemical network, we demonstrated how the biochemical reaction network achieving homeostasis. The evolution of the biochemical reaction network is studied in perspective of average degree and edges. Comparing with the network features of random graphs, the network features from the proposed BRN model can reveal more biological sense.

Highlights

  • Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids

  • The Internet is a complex network of routers and computers linked by various physical or wireless links; fads and ideas spread on the social network, whose nodes are human beings and whose edges represent various social relationships; the World Wide Web is an enormous virtual network of Web pages connected by hyperlinks

  • We study the dynamics of the biochemical reaction network by analyzing the evolution of the average degrees and edges

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Summary

Introduction

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. The Internet is a complex network of routers and computers linked by various physical or wireless links; fads and ideas spread on the social network, whose nodes are human beings and whose edges represent various social relationships; the World Wide Web is an enormous virtual network of Web pages connected by hyperlinks. These systems represent just a few of the many examples that have recently prompted the scientific community to investigate the mechanisms that determine the topology of complex networks [9,10].

Problem Statement
Biochemical Reaction Network Model
Vmax d
Number of edges
Comparison with the Original Random Graph
Conclusions
The distribution of the proability of the degree
Full Text
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