Literature DB >> 16849179

Examining the architecture of cellular computing through a comparative study with a computer.

Degeng Wang1, Michael Gribskov.   

Abstract

The computer and the cell both use information embedded in simple coding, the binary software code and the quadruple genomic code, respectively, to support system operations. A comparative examination of their system architecture as well as their information storage and utilization schemes is performed. On top of the code, both systems display a modular, multi-layered architecture, which, in the case of a computer, arises from human engineering efforts through a combination of hardware implementation and software abstraction. Using the computer as a reference system, a simplistic mapping of the architectural components between the two is easily detected. This comparison also reveals that a cell abolishes the software-hardware barrier through genomic encoding for the constituents of the biochemical network, a cell's "hardware" equivalent to the computer central processing unit (CPU). The information loading (gene expression) process acts as a major determinant of the encoded constituent's abundance, which, in turn, often determines the "bandwidth" of a biochemical pathway. Cellular processes are implemented in biochemical pathways in parallel manners. In a computer, on the other hand, the software provides only instructions and data for the CPU. A process represents just sequentially ordered actions by the CPU and only virtual parallelism can be implemented through CPU time-sharing. Whereas process management in a computer may simply mean job scheduling, coordinating pathway bandwidth through the gene expression machinery represents a major process management scheme in a cell. In summary, a cell can be viewed as a super-parallel computer, which computes through controlled hardware composition. While we have, at best, a very fragmented understanding of cellular operation, we have a thorough understanding of the computer throughout the engineering process. The potential utilization of this knowledge to the benefit of systems biology is discussed.

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Year:  2005        PMID: 16849179      PMCID: PMC1629074          DOI: 10.1098/rsif.2005.0038

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  12 in total

1.  From molecular to modular cell biology.

Authors:  L H Hartwell; J J Hopfield; S Leibler; A W Murray
Journal:  Nature       Date:  1999-12-02       Impact factor: 49.962

2.  The cell as the smallest DNA-based molecular computer.

Authors:  S Ji
Journal:  Biosystems       Date:  1999-10       Impact factor: 1.973

3.  Functional discovery via a compendium of expression profiles.

Authors:  T R Hughes; M J Marton; A R Jones; C J Roberts; R Stoughton; C D Armour; H A Bennett; E Coffey; H Dai; Y D He; M J Kidd; A M King; M R Meyer; D Slade; P Y Lum; S B Stepaniants; D D Shoemaker; D Gachotte; K Chakraburtty; J Simon; M Bard; S H Friend
Journal:  Cell       Date:  2000-07-07       Impact factor: 41.582

4.  Precision and functional specificity in mRNA decay.

Authors:  Yulei Wang; Chih Long Liu; John D Storey; Robert J Tibshirani; Daniel Herschlag; Patrick O Brown
Journal:  Proc Natl Acad Sci U S A       Date:  2002-04-23       Impact factor: 11.205

5.  The evolutionary origin of complex features.

Authors:  Richard E Lenski; Charles Ofria; Robert T Pennock; Christoph Adami
Journal:  Nature       Date:  2003-05-08       Impact factor: 49.962

6.  Introductory science and mathematics education for 21st-Century biologists.

Authors:  William Bialek; David Botstein
Journal:  Science       Date:  2004-02-06       Impact factor: 47.728

Review 7.  Network biology: understanding the cell's functional organization.

Authors:  Albert-László Barabási; Zoltán N Oltvai
Journal:  Nat Rev Genet       Date:  2004-02       Impact factor: 53.242

Review 8.  Hypothesis: hyperstructures regulate bacterial structure and the cell cycle.

Authors:  V Norris; S Alexandre; Y Bouligand; D Cellier; M Demarty; G Grehan; G Gouesbet; J Guespin; E Insinna; L Le Sceller; B Maheu; C Monnier; N Grant; T Onoda; N Orange; A Oshima; L Picton; H Polaert; C Ripoll; M Thellier; J M Valleton; M C Verdus; J C Vincent; G White; P Wiggins
Journal:  Biochimie       Date:  1999 Aug-Sep       Impact factor: 4.079

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Authors:  L M Adleman
Journal:  Science       Date:  1994-11-11       Impact factor: 47.728

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Authors:  Yaakov Benenson; Binyamin Gil; Uri Ben-Dor; Rivka Adar; Ehud Shapiro
Journal:  Nature       Date:  2004-04-28       Impact factor: 49.962

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  3 in total

1.  Discrepancy between mRNA and protein abundance: insight from information retrieval process in computers.

Authors:  Degeng Wang
Journal:  Comput Biol Chem       Date:  2008-07-16       Impact factor: 2.877

Review 2.  A comparative approach for the investigation of biological information processing: an examination of the structure and function of computer hard drives and DNA.

Authors:  David J D'Onofrio; Gary An
Journal:  Theor Biol Med Model       Date:  2010-01-21       Impact factor: 2.432

3.  Uncovering the cellular capacity for intensive and specific feedback self-control of the argonautes and MicroRNA targeting activity.

Authors:  Degeng Wang; Tingzeng Wang; Audrey Gill; Terrell Hilliard; Fengqian Chen; Andrey L Karamyshev; Fangyuan Zhang
Journal:  Nucleic Acids Res       Date:  2020-05-21       Impact factor: 16.971

  3 in total

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