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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

CSD homomorphisms between phylogenetic networks.

Stephen J Willson¹

¹Department of Mathematics, Iowa State University, Ames, IA 50011, USA. swillson@iastate.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|April 11, 2012

Summary

Species trees are insufficient for complex evolutionary relationships. Connected surjective digraph maps (CSDs) offer a new way to map complex biological networks, improving evolutionary studies.

Area of Science:

Evolutionary biology
Network theory
Graph theory

Background:

Traditional species trees simplify complex evolutionary histories.
Hybridization and lateral gene transfer challenge tree-based models.
Need for models that capture complex biological networks beyond simple trees.

Purpose of the Study:

Introduce and define Connected Surjective Digraph (CSD) maps.
Explore the properties and applications of CSD maps in network analysis.
Establish CSD maps as a tool for understanding complex evolutionary relationships.

Main Methods:

Formal definition of Connected Surjective Digraph (CSD) maps.
Analysis of CSD map composition properties.
Investigation of lifting undirected graphs using CSD maps.

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Main Results:

CSD maps are well-behaved under composition.
A CSD map from network N to M allows lifting M into N.
CSD maps impose significant constraints on the structure of network N.

Conclusions:

CSD maps provide a framework for analyzing biological networks more complex than trees.
This approach can help reconcile complex evolutionary events like hybridization with network models.
Studying classes of networks with CSD maps offers a path towards more accurate evolutionary representations.