A simple model for the evolution of molecular codes driven by the interplay of accuracy, diversity and cost

What can information-asymmetric games tell us about the context of Crick's 'frozen accident'?

This paper describes a novel application of information-asymmetric (signalling) games to molecular biology in which utility is determined by the message complexity (rate) in addition to the error in information transfer (distortion). We show using a computational model how it is possible for the agents in one such game to evolve a signalling convention (separating equilibrium) that is suboptimal in terms of information transfer, but is nonetheless stable. In the context of an RNA world merging with a nascent amino acid one, such a game's equilibrium is alluded to by the genetic code, which is nearly optimal in terms of information transfer, but is also near-universal and nearly immutable. Such a framework suggests that cellularity may have emerged to encourage coordination between RNA species and sheds light on other aspects of RNA world biochemistry yet to be fully understood.

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.

Cells as semantic systems

BACKGROUND

We consider cells as biological systems that process information by means of molecular codes. Many studies analyze cellular information processing exclusively in syntactic terms (e.g., by measuring Shannon entropy of sets of macromolecules), and abstract completely from semantic aspects that are related to the meaning of molecular information.

METHODS

This mini-review focuses on semantic aspects of molecular information, particularly on codes that organize the semantic dimension of molecular information. First, a general conceptual framework for describing molecular information is proposed. Second, some examples of molecular codes are presented. Third, a mathematical approach that makes the identification of molecular codes in reaction networks possible, is developed.

RESULTS

By combining a systematic conceptual framework for describing molecular information and a mathematical approach to identify molecular codes, it is possible to give a formally consistent and empirically adequate model of the code-based semantics of molecular information in cells.

GENERAL SIGNIFICANCE

Research on the semantics of molecular information is of great importance particularly to systems biology since molecular codes embedded in systems of interrelated codes govern main traits of cells. Describing cells as semantic systems may thus trigger new experiments and generate new insights into the fundamental processes of cellular information processing. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Strong short-range correlations and dichotomic codon classes in coding DNA sequences

The study of correlation structures in DNA sequences is of great interest because it allows us to obtain structural and functional information about underlying genetic mechanisms. In this paper we present a study of the correlation structure of protein coding sequences of DNA based on a recently developed mathematical representation of the genetic code. A fundamental consequence of such representation is that codons can be assigned a parity class (odd-even). Such parity can be obtained by means of a nonlinear algorithm acting on the chemical character of the codon bases. In the same setting the Rumer's class can be naturally described and a new dichotomic class, the hidden class, can be defined. Moreover, we show that the set of DNA's base transformations associated to the three dichotomic classes can be put in a compact group-theoretic framework. We use the dichotomic classes as a coding scheme for DNA sequences and study the mutual dependence between such classes. The same analysis is carried out also on the chemical dichotomies of DNA bases. In both cases, the statistical analysis is performed by using an entropy-based dependence metric possessing many desirable properties. We obtain meaningful tests for mutual dependence by using suitable resampling techniques. We find strong short-range correlations between certain combinations of dichotomic codon classes. These results support our previous hypothesis that codon classes might play an active role in the organization of genetic information.

Casting polymer nets to optimize noisy molecular codes

Life relies on the efficient performance of molecular codes, which relate symbols and meanings via error-prone molecular recognition. We describe how optimizing a code to withstand the impact of molecular recognition noise may be understood from the statistics of a two-dimensional network made of polymers. The noisy code is defined by partitioning the space of symbols into regions according to their meanings. The "polymers" are the boundaries between these regions, and their statistics define the cost and the quality of the noisy code. When the parameters that control the cost-quality balance are varied, the polymer network undergoes a transition, where the number of encoded meanings rises discontinuously. Effects of population dynamics on the evolution of molecular codes are discussed.