Informational laws of genome structures

Abstract

In recent years, the analysis of genomes by means of strings of length k occurring in the genomes, called k-mers, has provided important insights into the basic mechanisms and design principles of genome structures. In the present study, we focus on the proper choice of the value of k for applying information theoretic concepts that express intrinsic aspects of genomes. The value k = lg2(n), where n is the genome length, is determined to be the best choice in the definition of some genomic informational indexes that are studied and computed for seventy genomes. These indexes, which are based on information entropies and on suitable comparisons with random genomes, suggest five informational laws, to which all of the considered genomes obey. Moreover, an informational genome complexity measure is proposed, which is a generalized logistic map that balances entropic and anti-entropic components of genomes and is related to their evolutionary dynamics. Finally, applications to computational synthetic biology are briefly outlined.

An Informational Proof of H-Theorem

“…In contexts where emergence phenomena occur, classical interpretations of the second laws seem to contradict the mechanisms of complexity, evolvability and autonomy related to new paradigms of informational inference and of distributed autonomous agent computations. The genomic laws given in [15] are an indication toward more comprehensive ways of considering entropic concepts, where the informational perspective seems to provide new keys for the analysis of complex phenomena.…”

“…In [15] From the above Proposition it follows that the average length of strings encoding n objects is between…”

“…In many applications, where entropy is the empirical entropy computed from observed frequencies, the probability estimation is not the main aspect of the problem [15] [16]. For example, when we deal with genomic information as obtained by k -mer sequences of genomes [15] [19] other crucial aspects emerge such as: i) efficient methods for computing entropy, which is computationally prohibitive when k increases, and ii) the use of random genomes V. Manca 12/15 OALib Journal for determining anti-random aspects that concern with the biological functions emerging during evolution.…”

“…For example, when we deal with genomic information as obtained by k -mer sequences of genomes [15] [19] other crucial aspects emerge such as: i) efficient methods for computing entropy, which is computationally prohibitive when k increases, and ii) the use of random genomes V. Manca 12/15 OALib Journal for determining anti-random aspects that concern with the biological functions emerging during evolution. In conclusion, the discrete and computational perspectives are ingredients that are missing in classical discussions about information and physical entropies.…”

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

“…However, such approaches fail to discover genomic aspects of systemic nature regarding dynamics or resilience. An alternative framework is based on alignment-free methods of genome analysis, where global properties of genomes are investigated [11]. A key concept of informational analysis is that of probability distributions.…”