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Study of biological networks using graph theory.

Wei Gao1, Hualong Wu1, Muhammad Kamran Siddiqui2

  • 1School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.

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Graph theory models biological networks, using topological indices to measure molecular structures like cells and genes. This study computes eccentric indices for hypertree and X-tree networks, showing bioengineering

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Area of Science:

  • Computational Biology
  • Graph Theory Applications
  • Bioinformatics

Background:

  • Graph theory is a vital tool in mathematical biology for analyzing complex biological systems.
  • Graphs represent molecular structures, with vertices as cells, genes, or proteins and edges as connections.
  • Topological indices derived from graphs quantify biological activity.

Purpose of the Study:

  • To investigate the biological features of biological networks using eccentric topological indices.
  • To determine exact expressions for eccentric related indices in specific network structures.

Main Methods:

  • Utilizing graph structure analysis.
  • Employing distance calculations within network graphs.
  • Computing eccentric topological indices for hypertree and X-tree networks.

Main Results:

  • Derived exact expressions for key eccentric topological indices in hypertree networks.
  • Derived exact expressions for key eccentric topological indices in X-tree networks.
  • Quantified biological activity characteristics through computed indices.

Conclusions:

  • The study provides precise mathematical insights into the topology of biological networks.
  • Eccentric topological indices offer a method for analyzing biological network characteristics.
  • Findings suggest significant potential for graph theory applications in bioengineering.