Diego L. Gonzalez. Dipartimento di Statistica Università di Bologna. CNR-IMM Istituto per la Microelettronica e i Microsistemi
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1 Diego L. Gonzalez CNR-IMM Istituto per la Microelettronica e i Microsistemi Dipartimento di Statistica Università di Bologna 1
2 Biological Codes Biological codes are the great invariants of life, the sole entities that have been conserved in evolution while everything else has changed, Barbieri, What has been conserved in biological codes? Adaptors, in short, are the key molecules in all organic codes. They are the molecular fingerprints of the codes. It is one of those facts that have extraordinary theoretical implications If adaptors are the key molecules, the origin of the codes should be described by the properties of the adaptors? In biology, conserved quantities are related to relevant biological functions; in physics conserved quantities are a consequence of symmetry. In a physical paradigm, symmetry is our first ingredient in the search for conserved quantities in biological codes. 2
3 Degeneracy Focusing on the genetic code: is it possible to construct a model of the genetic code based on adaptors and through this model is it possible to study conserved quantities? What is degeneracy? Degeneracy is a concept that comes from quantum mechanics. It refers to the fact that the same total energy can characterize more that one different quantum-state. By analogy we say that an amino acid is degenerate because it is associated to more than one different codon. Degeneracy is the main mathematical property of the genetic code 3
4 The Genetic Code as a Mapping 64 codons; 4x4x4 = 64 (Words of three letters from an alphabet of four, i.e., A, T, C, G) 20 amino acids + Stop codon (Methyonine represents also the synthesis start signal) Redundancy and Degeneracy follow 4
5 Degeneracy Distribution of the Nuclear Euplotes Genetic Code Degeneracy distribution inside quartets Cys Euplotes nuclear genetic code 5
6 Mathematical Modelling of the Genetic Code In the last years we have developed a mathematical model of the genetic code that explains the degeneracy distribution of both variants of the genetic code, nuclear and mitochondrial. The model is based on number representation systems. Numbers are usually represented in an univocal way: any number has only one representation and any representation corresponds to only one number. This is the case, for example, of the common decimal representation system. This univocity is due to the characteristics of the positional values and digits used in the so called power numeration systems (because the positional values of the representation correspond to the powers of a given base, i.e., 10 in the decimal system). As the genetic code is degenerate and redundant we are interested in non-univocal representation systems 6
7 Mathematical model: integer number representation systems Usual positional notation power representation systems base k d 5 d 4 d 3 d 2 d 1 d 0 Digits: d i (0, (k-1)) k 5 k 4 k 3 k 2 k 1 k 0 R (integer) = d 5 k 5 + d 4 k 4 + d 3 k 3 + d 2 k 2 + d 1 k 1 + d 0 k 0 Univocity condition: Example 1: k=10; decimal system Digits: d i (0, 9) R = = 19 7
8 Redundant representation systems Example 2: Binary power system Digits: d i (0, 1) R = = 19 Redundant Power Representations Using Out-of-Range Digits Example 3: Binary signed digits with three possible values: -1, 0, R = = 19 Redundant 8
9 Non-power redundant representation systems positional bases grow slowly than the powers of k Example 4: k=2; Fibonacci system Digits: d i (0, 1) R = = 19 Non-power representation systems are redundant, for example, the number 19 can be represented in the Fibonacci system in another alternative way: Maya serpent numbers R = = 19 9
10 Fibonacci s non-power representation system Degeneracy Number Signed Binary Fibonacci System Genetic Code
11 Non-power representation system 1,1,2,4,7,8 Unique solution 1,1,2,4,7,8 11
12 From a structural isomorphism to a true model GENETIC CODE 64 Codons 24 (aa + signs) NON-POWER REPRESENTATION SYMMETRY 64 Binary strings 24 Integer numbers 12
13 The Model of the Euplotes Nuclear Genetic Code Can the genetic code be mathematically described?, Diego L. Gonzalez, Medical Science Monitor, v.10, n.4, HY11-17 (2004). The Mathematical Structure of the Genetic Code D.L. Gonzalez, The Codes of Life, M.. Barbieri Editor, Springer Verlag (2008) 13
14 Symmetry of U C exchange NNU and NNC codify the same amino acid 16 pairs of codons NNU - NNC Common to all known versions of the genetic code 14
15 Symmetry of U C exchange NNU and NNC codify the same amino acid 16 pairs of codons NNU - NNC Common to all known versions of the genetic code 15
16 Non-power representation system 1,1,2,4,7,8 Unique solution 1,1,2,4,7,8 16
17 Symmetry of xxxx01 xxxx10 exchange xxxx01 and xxxx10 codify the same integer number 16 pairs of strings xxxx01 xxxx10 17
18 Symmetry of xxxx01 xxxx10 exchange xxxx01 and xxxx10 codify the same integer number 16 pairs of strings xxxx01 xxxx10 18
19 Parity Coding The parity of the strings can be described in terms of biochemical properties Parity of a string = parity of the # of 1 s, i.e., 0,1,1,0,1,0 = odd string The complete set of parity rules: Codons ending in A are odd (grey boxes); codons ending in G are even (white boxes) Codons ending in T or C are odd if the second letter is T or G (Keto) Codons ending in T or C are even if the second letter is A or C (Amino) Dichotomic classes: Parity, Rumer, Hidden Actual sequences can be binary coded and real sequences explored by statistical methods: short-range correlations, circular codes, etc. 19
20 Vertebrate mitochondrial genetic code Distribution of Degeneracy Vertebrate Mitochondrial Degeneracy number of synonymous codons Number of amino acids sharing this degeneracy Is it possible to describe the degeneracy distribution of the vertebrate mitochondrial genetic code by means of a non-power number representation? In yellow are evidenced the differences with the standard nuclear genetic code 20
21 The 8,8,4,2,1,0 non-power solution for mitochondrial degeneracy Degeneracy Number Non-Power representation Mitochondrial Genetic Code Euplotes Nuclear Genetic Code
22 Symmetry of A G exchange (Mitochondrial Code) NNA and NNG codify the same amino acid 16 pairs of codons NNA - NNG Common to many mitochondrial versions of the genetic code 22
23 Symmetry of A G exchange (Mitochondrial Code) NNA and NNG codify the same amino acid 16 pairs of codons NNA - NNG Common to many mitochondrial versions of the genetic code 23
24 Origin of degeneracy in amino acid coding Hypothesis of reversible ancient trnas T A C A T G C A T G T A T A T T A T S. Nikolajewa. M. Friedel, A. Beyer, and T. Wilhelm, The new classification scheme of the genetic code, its early evolution, and trna usage, Journal of Bioinformatics and Computational Biology, 12, 54 (2005) 1-12 A T A A T A 24
25 Half the Degeneracy of the Mitochondrial Genetic Code Half the degeneracy of the genetic code Obtained by excluding the 0 non-power base of the representation (8,8,4,2,1) 25
26 The Complete Tesserae Representation Group properties of the four symmetric transformations, i.e., I, C, YR and KM Klein 4-group V 26
27 Origin of degeneracy in amino acid coding Degeneracy as plurality of states characterized by the same interaction energy D.L. Gonzalez, S. Giannerini, and R. Rosa, On the origin of the mitochondrial genetic code: Towards a Unified mathematical framework for the management of genetic information, Nature Precedings, < (2012) H. Seligman, Pocketknife trna hypothesis: anticodons in mammal mitochondrial trna side-arm loops translate proteins? Biosystems (2013) 27
28 Origin of the mitochondrial genetic code Distribution of Degeneracy Vertebrate Mitochondrial Distribution of Degeneracy Non-power system 8,8,4,2,1,0 Distribution of Degeneracy Tesserae Degeneracy number of synonymous codons Number of amino acids sharing this degeneracy 2 16 Degeneracy # of integers Degeneracy Number of synonymous tesserae Number of amino acids sharing this degeneracy
29 Tesserae Properties A,A,A,A -> A,A,T,A Point mutation immunity, +1 Frame-shift immunity, Rumer s transformation 29
30 Conserved Features of the Mitochondrial Genetic Code Genetic Code Non-power Representation Tesserae (di-dinucleotides) Symmetries Degeneracy Distribution Conserved Features Why these? Putative Ancient trna Adaptors 30
31 Publications Una Nuova Descrizione Matematica del Codice Genetico D.L. Gonzalez and M. Zanna, Systema Naturae, Annali di Biologia Teorica, V. 5, pp , (2003). Can the genetic code be mathematically described?, Diego L. Gonzalez, Medical Science Monitor, v.10, n.4, HY11-17 (2004). Detecting Structure in Parity Binary Sequences: Error Correction and Detection in DNA, Diego Luis Gonzalez, Simone Giannerini and Rodolfo Rosa, IEEE-EMB Magazine, Special Issue on Communication Theory, Coding Theory and Molecular Biology, pp , January/February (2006). Error Detection and Correction Codes D.L. Gonzalez, in The Codes of Life, chapter 6, Marcello Barbieri Editor, Springer Verlag (2008). The Mathematical Structure of the Genetic Code D.L. Gonzalez, in The Codes of Life, chapter 17, Marcello Barbieri Editor, Springer Verlag (2008). Strong Short-Range Correlations and Codon Class Dichotomies classes in Coding DNA Sequences, Diego L. Gonzalez, Simone Giannerini and Rodolfo Rosa, Physical Review E,78, n.5, 08481, (2008). The mathematical structure of the genetic code: a tool for inquiring on the origin of life Diego L. Gonzalez, S. Giannerini, and R. Rosa, Rivista di Statistica, Anno LXIX, n. 2-3, pp ,(2009). Circular codes revisited: a statistical approach Diego Luis Gonzalez, Simone Giannerini and Rodolfo Rosa, Journal of Theoretical Biology, vol. 275, n.1, pp21-28, (2011). "DNA, circular codes and dichotomic classes: a quasi-crystal framework", Diego Luis Gonzalez, Simone Giannerini and Rodolfo Rosa, Phil Trans A of the Royal Society, Beyond Crystals: the dialectic of information and structure, theme issue, 370, ; doi: /rsta , (2012). On the origin of the mitochondrial genetic code: Towards a unified mathematical framework for the management of genetic information Gonzalez, Diego Luis, Giannerini, Simone, and Rosa, Rodolfo. Available from Nature Precedings < (2012) Genome characterization through dichotomic classes: an analysis of the whole chromosome 1 of A. Thaliana, Enrico Properzi, Simone Giannerini, Diego L. Gonzalez, and Rodolfo Rosa, Mathematical Biosciences and Engineering, v. 10, n. 1, pp , (2013). 31
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