Atmospheric Reaction Systems as Null-Models to Identify Structural Traces of Evolution in Metabolism

The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.

[1]  Petter Holme,et al.  Signatures of currency vertices , 2008, 0812.3487.

[2]  Yoichi Takenaka,et al.  A graph analysis method to detect metabolic sub-networks based on phylogenetic profile , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[3]  H. Bethe.  Energy production in stars. , 1968, Science.

[4]  Petter Holme,et al.  Substance graphs are optimal simple-graph representations of metabolism , 2008, 0806.2763.

[5]  R. Solé,et al.  The large-scale organization of chemical reaction networks in astrophysics , 2004, cond-mat/0406137.

[6]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[7]  Y. Le Teuff,et al.  The UMIST database for astrochemistry , 2000 .

[8]  M. Serrano,et al.  Network-based confidence scoring system for genome-scale metabolic reconstructions , 2010, 1008.3166.

[9]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[10]  H. Krebs,et al.  The role of citric acid in intermediate metabolism in animal tissues , 1937, FEBS letters.

[11]  W. Demore,et al.  Photochemistry of Planetary Atmospheres , 1998 .

[12]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[13]  R. Tsien,et al.  Specificity and Stability in Topology of Protein Networks , 2022 .

[14]  Petter Holme,et al.  Subnetwork hierarchies of biochemical pathways , 2002, Bioinform..

[15]  Benjamin H. Good,et al.  Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[17]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[18]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[19]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[20]  Petter Holme.  Metabolic Robustness and Network Modularity: A Model Study , 2010, PloS one.

[21]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[22]  Steffen Klamt,et al.  Hypergraphs and Cellular Networks , 2009, PLoS Comput. Biol..

[23]  L. Lara,et al.  Photochemistry of Planetary Atmospheres , 2002 .

[24]  Petter Holme,et al.  Currency and commodity metabolites: their identification and relation to the modularity of metabolic networks. , 2006, IET systems biology.

[25]  A. J. Markwick-Kemper,et al.  The UMIST Database for Astrochemistry 2006 , 2007 .

[26]  A graph analysis method to detect metabolic sub-networks based on phylogenetic profile , 2004 .

[27]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[28]  Joel S. Bader,et al.  Predicting functional associations from metabolism using bi-partite network algorithms , 2010, BMC Systems Biology.