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Shellman ER, Chen Y, Lin X, et al. Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study. Comput Biol Chem. 2014;53:242-250doi: 10.1016/j.compbiolchem.2014.09.006.
Shellman, E. R., Chen, Y., Lin, X., Burant, C. F., & Schnell, S. (2014). Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study. Computational biology and chemistry, 53242-250. https://doi.org/10.1016/j.compbiolchem.2014.09.006
Shellman, Erin R, et al. "Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study." Computational biology and chemistry vol. 53 (2014): 242-250. doi: https://doi.org/10.1016/j.compbiolchem.2014.09.006
Shellman ER, Chen Y, Lin X, Burant CF, Schnell S. Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study. Comput Biol Chem. 2014 Dec;53:242-250. doi: 10.1016/j.compbiolchem.2014.09.006. Epub 2014 Sep 21. PMID: 25462333; PMCID: PMC4254655.
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Comput Biol Chem. 2014 Dec;53:242-250. doi: 10.1016/j.compbiolchem.2014.09.006. Epub 2014 Sep 21.

Metabolic network motifs can provide novel insights into evolution: The evolutionary origin of Eukaryotic organelles as a case study.

Computational biology and chemistry

Erin R Shellman, Yu Chen, Xiaoxia Lin, Charles F Burant, Santiago Schnell

Affiliations

  1. Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
  2. Department of Chemical Engineering, University of Michigan School of Engineering, Ann Arbor, MI, USA.
  3. Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: [email protected].
  4. Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: [email protected].

PMID: 25462333 PMCID: PMC4254655 DOI: 10.1016/j.compbiolchem.2014.09.006

Abstract

Phylogenetic trees are typically constructed using genetic and genomic data, and provide robust evolutionary relationships of species from the genomic point of view. We present an application of network motif mining and analysis of metabolic pathways that when used in combination with phylogenetic trees can provide a more complete picture of evolution. By using distributions of three-node motifs as a proxy for metabolic similarity, we analyze the ancestral origin of Eukaryotic organelles from the metabolic point of view to illustrate the application of our motif mining and analysis network approach. Our analysis suggests that the hypothesis of an early proto-Eukaryote could be valid. It also suggests that a δ- or ϵ-Proteobacteria may have been the endosymbiotic partner that gave rise to modern mitochondria. Our evolutionary analysis needs to be extended by building metabolic network reconstructions of species from the phylum Crenarchaeota, which is considered to be a possible archaeal ancestor of the eukaryotic cell. In this paper, we also propose a methodology for constructing phylogenetic trees that incorporates metabolic network signatures to identify regions of genomically-estimated phylogenies that may be spurious. We find that results generated from our approach are consistent with a parallel phylogenetic analysis using the method of feature frequency profiles.

Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords: Enzyme classification; Eukaryotic cell; Evolution; Metabolism; Network motifs; Phylogenies

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