Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

Edited* by Gregory A. Petsko, Brandeis University, Waltham, MA, and approved April 2, 2010 (received for review December 20, 2009)
May 3, 2010
107 (20) 9186-9191

Abstract

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

Continue Reading

Acknowledgments.

We thank the anonymous reviewers whose valuable suggestions helped to improve the quality of the manuscript. K.-K.Y. acknowledges Lucas Lochovsky for useful discussion and critical reading of an early manuscript. K.-K.Y. acknowledges Kevin Yip for useful discussion. This work is supported by the National Institutes of Health.

Supporting Information

Supporting Information (PDF)
Supporting Information

References

1
U Alon An Introduction to Systems Biology (Chapman & Hall/CRC, London, 2007).
2
A Barabási LINKED: The New Science of Networks (Perseus, Cambridge, MA, 2002).
3
H Yu, M Gerstein, Genomic analysis of the hierarchical structure of regulatory networks. Proc Natl Acad Sci USA 103, 14724–14731 (2006).
4
N Bhardwaj, KK Yan, M Gerstein, Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels. Proc Natl Acad Sci USA 107, 6841–6846 (2010).
5
MM Lehman, Programs, life cycles, and laws of software evolution. Proc IEEE 68, 1060–1076 (1980).
6
TI Lee, et al., Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).
7
H Bolouri, EH Davidson, Modeling transcriptional regulatory networks. Bioessays 24, 1118–1129 (2002).
8
A Barabási, ZN Oltvai, Network biology: Understanding the cell’s functional organization. Nat Rev Genet 5, 101–113 (2004).
9
MM Babu, NM Luscombe, L Aravind, M Gerstein, SA Teichmann, Structure and evolution of transcriptional regulatory networks. Curr Opin Struct Biol 14, 283–291 (2004).
10
NM Luscombe, et al., Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).
11
D Thieffry, AM Huerta, E Perez-Rueda, J Collado-Vides, From specific gene regulation to genomic networks: A global analysis of transcriptional regulation in Escherichia coli. Bioessays 20, 433–440 (1998).
12
SS Shen-Orr, R Milo, S Mangan, U Alon, Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31, 64–68 (2002).
13
H Ma, et al., An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. Nucleic Acids Res 32, 6643–6649 (2004).
14
AS Seshasayee, GM Fraser, MM Babu, NM Luscombe, Principles of transcriptional regulation and evolution of the metabolic system in E. coli. Genome Res 19, 79–91 (2009).
15
S Gama-Castro, et al., RegulonDB (version 6.0): Gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucleic Acids Res 36, D120–124 (2008).
16
S Maslov, K Sneppen, Computational architecture of the yeast regulatory network. Phys Biol 2, S94–100 (2005).
17
AL Barabasi, R Albert, Emergence scaling in random networks. Science 286, 509–512 (1999).
18
CR Myers, Software systems as complex networks: Structure, function, and evolvability of software collaboration graphs. Phys Rev E 68, 046116 (2003).
19
U Alon, Biological networks: The tinkerer as an engineer. Science 301, 1866–1867 (2003).
20
DL Parnas, On the criteria to be used in decomposing systems into modules. Commun ACM 15, 1053–1058 (1972).
21
G Balazsi, A Barabasi, ZN Oltvai, Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc Natl Acad Sci USA 102, 7841–7846 (2005).
22
G Fang, EPC Rocha, A Danchin, Persistence drives gene clustering in bacterial genomes. BMC Genomics 9, 4 (2008).
23
A Danchin, Bacteria as computers making computers. FEMS Microbiol Rev 33, 3–26 (2009).
24
HB Fraser, AE Hirsh, LM Steinmetz, C Scharfe, MW Feldman, Evolutionary rate in the protein interaction network. Science 296, 750–752 (2002).
25
PM Kim, JO Korbel, MB Gerstein, Positive selection at the protein network periphery: Evaluation in terms of structural constraints and cellular context. Proc Natl Acad Sci USA 104, 20274–20279 (2007).
26
H Yu, PM Kim, E Sprecher, V Trifonov, M Gerstein, The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3, e59 (2007).
27
S Maslov, S Krishna, TY Pang, K Sneppen, Toolbox model of evolution of prokaryotic metabolic networks and their regulation. Proc Natl Acad Sci USA 106, 9743–9748 (2009).
28
IK Jordan, IB Rogozin, YI Wolf, EV Koonin, Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res 12, 962–968 (2002).
29
PS Novichkov, I Ratnere, YI Wolf, EV Koonin, I Dubchak, ATGC: A database of orthologous genes from closely related prokaryotic genomes and a research platform for microevolution of prokaryotes. Nucleic Acids Res 37, D448–454 (2009).
30
M Suyama, D Torrents, P Bork, PAL2NAL: Robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res 34, W609–612 (2006).
31
Z Yang, PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol 24, 1586–1591 (2007).

Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 107 | No. 20
May 18, 2010
PubMed: 20439753

Classifications

Submission history

Published online: May 3, 2010
Published in issue: May 18, 2010

Keywords

  1. systems biology
  2. adaptive complex systems

Acknowledgments

We thank the anonymous reviewers whose valuable suggestions helped to improve the quality of the manuscript. K.-K.Y. acknowledges Lucas Lochovsky for useful discussion and critical reading of an early manuscript. K.-K.Y. acknowledges Kevin Yip for useful discussion. This work is supported by the National Institutes of Health.

Notes

*This Direct Submission article had a prearranged editor.

Authors

Affiliations

Koon-Kiu Yan
Department of Molecular Biophysics and Biochemistry, and
Gang Fang
Department of Molecular Biophysics and Biochemistry, and
Nitin Bhardwaj
Department of Molecular Biophysics and Biochemistry, and
Roger P. Alexander
Department of Molecular Biophysics and Biochemistry, and
Mark Gerstein1 [email protected]
Program in Computational Biology and Bioinformatics,
Department of Molecular Biophysics and Biochemistry, and
Department of Computer Science, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520

Notes

1
To whom correspondence should be addressed. E-mail: [email protected].
Author contributions: K.-K.Y., G.F., N.B., R.P.A., and M.G. designed research; K.-K.Y. performed research; G.F., N.B., and R.P.A. contributed new reagents/analytic tools; K.-K.Y. analyzed data; and K.-K.Y. and M.G. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

Metrics & Citations

Metrics

Note: The article usage is presented with a three- to four-day delay and will update daily once available. Due to ths delay, usage data will not appear immediately following publication. Citation information is sourced from Crossref Cited-by service.


Citation statements

Altmetrics

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

    Loading...

    View Options

    View options

    PDF format

    Download this article as a PDF file

    DOWNLOAD PDF

    Get Access

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Personal login Institutional Login

    Recommend to a librarian

    Recommend PNAS to a Librarian

    Purchase options

    Purchase this article to get full access to it.

    Single Article Purchase

    Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks
    Proceedings of the National Academy of Sciences
    • Vol. 107
    • No. 20
    • pp. 9023-9476

    Media

    Figures

    Tables

    Other

    Share

    Share

    Share article link

    Share on social media