Extending Tlusty's rate distortion index theorem method to the glycome: Do even 'low level' biochemical phenomena require sophisticated cognitive paradigms?

Unlike the universal genetic code and ordered protein folding, direct application of Tlusty's method to the glycome produces a reducto ad absurdum: From the beginning a complicated system of chemical cognition is needed so that external information constrains and tunes what would otherwise be a monstrously large 'glycan code error network'. Further, the glycan manufacture machinery itself must be regulated by yet other levels of chemical cognition to ensure that what is produced matches what was chosen for production. Application of a rate distortion index theorem/operator method at this second stage appears possible, permitting analytic characterization of the complicated 'glycan spectra' associated with cellular interactions and their dynamics. The regulation of 'low level' biochemical processes, ranging from gene expression and protein folding through the production of flexible glycan surface signalling fronds, appears to require systems of chemical cognition whose sophistication may rival that of high order neural process.

[1]  Rodrick Wallace,et al.  Rate Distortion Manifolds as Model Spaces for Cognitive Information , 2009, Informatica.

[2]  Michael Atiyah,et al.  The index of elliptic operators on compact manifolds , 1963 .

[3]  L. H. Bradley,et al.  De novo proteins from designed combinatorial libraries , 2004, Protein science : a publication of the Protein Society.

[4]  Michael H. Hecht,et al.  Generic hydrophobic residues are sufficient to promote aggregation of the Alzheimer's Aβ42 peptide , 2006, Proceedings of the National Academy of Sciences.

[5]  Gerald W. Hart,et al.  Glycomics Hits the Big Time , 2010, Cell.

[6]  A. Varki,et al.  The sialome--far more than the sum of its parts. , 2010, Omics : a journal of integrative biology.

[7]  I. Cohen,et al.  Immune information, self-organization and meaning. , 1998, International immunology.

[8]  Rodrick Wallace,et al.  Language and Coherent Neural Amplification in Hierarchical Systems: Renormalization and the Dual Information Source of a generalized Spatiotemporal Stochastic Resonance , 2000, Int. J. Bifurc. Chaos.

[9]  C. Anfinsen.  Principles that govern the folding of protein chains. , 1973, Science.

[10]  A. Surolia,et al.  Lectin microarrays for glycomic analysis. , 2010, Omics : a journal of integrative biology.

[11]  P. Mazur,et al.  Non-equilibrium thermodynamics, , 1963 .

[12]  Rodrick Wallace,et al.  Gene Expression and Its Discontents: The Social Production of Chronic Disease , 2009 .

[13]  G M Maggiora,et al.  Domain structural class prediction. , 1998, Protein engineering.

[14]  R. Wallace.  Protein folding disorders: toward a basic biological paradigm. , 2010, Journal of theoretical biology.

[15]  Rodrick Wallace,et al.  Structure and dynamics of the 'protein folding code' inferred using Tlusty's topological rate distortion approach , 2011, Biosyst..

[16]  R. Wallace.  Consciousness: A Mathematical Treatment of the Global Neuronal Workspace Model , 2005 .

[17]  I. Mian,et al.  Communication theory and multicellular biology. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[18]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[19]  G. Ringel,et al.  Solution of the heawood map-coloring problem. , 1968, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Tsvi Tlusty,et al.  A model for the emergence of the genetic code as a transition in a noisy information channel , 2007, Journal of theoretical biology.

[21]  Rodrick Wallace,et al.  Code, Context, and Epigenetic Catalysis in Gene Expression , 2009, Trans. Comp. Sys. Biology.

[22]  Yukio Matsumoto.  An Introduction to Morse Theory , 2001 .

[23]  C. Chothia,et al.  Structural patterns in globular proteins , 1976, Nature.

[24]  Konrad Engel,et al.  Encyclopaedia of Mathematics, Supplement III , 2002 .

[25]  Richard D. Cummings,et al.  The repertoire of glycan determinants in the human glycome. , 2009, Molecular bioSystems.

[26]  Aleksandr Yakovlevich Khinchin,et al.  Mathematical foundations of information theory , 1959 .

[27]  S. Varadhan.  Large Deviations and Applications , 1984 .

[28]  Tsvi Tlusty,et al.  A simple model for the evolution of molecular codes driven by the interplay of accuracy, diversity and cost , 2008, Physical biology.

[29]  Rodrick Wallace,et al.  Punctuated Equilibrium in Statistical Models of Generalized Coevolutionary Resilience: How Sudden Ecosystem Transitions Can Entrain Both Phenotype Expression and Darwinian Selection , 2009, Trans. Comp. Sys. Biology.

[30]  R. Ellis,et al.  Entropy, large deviations, and statistical mechanics , 1985 .

[31]  M. Gruebele.  Downhill protein folding: evolution meets physics. , 2005, Comptes rendus biologies.

[32]  R. Wallace.  Culture and inattentional blindness: a global workspace perspective. , 2007, Journal of Theoretical Biology.

[33]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .