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Sertac Eroglu

Publications and source records attributed to Sertac Eroglu.

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Nonextensive Statistical Signatures of the Bilaterian Transition in Proteome Length Distributions

Protein length distributions across the tree of life carry a quantitative signature of organismal complexity. Nonextensive statistical mechanics, through the Tsallis generalized entropy formalism, provides a natural framework for describing complex systems characterized by long-range correlations, scale invariance, and hierarchical organization -- features that classical Boltzmann-Gibbs statistics cannot accommodate. In this work, the complementary cumulative distribution function (CCDF) of protein lengths is analyzed within this framework for the reference proteomes of 22 fully sequenced organisms spanning the domains Archaea, Bacteria, and Eukarya, with deliberate sampling across the animal transition zone from sponges and cnidarians to higher bilaterians. Maximum likelihood (MLE) fitting of truncated discrete q-exponential distributions, with bootstrap 95% confidence intervals (CIs) reveals that the entropic index q resolves into three statistically distinct regimes: superextensive (q < 1) for prokaryotes, unicellular and non-animal multicellular eukaryotes, and basal animals; a boundary regime (CI on spanning unity) for the two cnidarians studied and the basal bilaterian C. teleta; and subextensive (q > 1) for all higher bilaterians, with q increasing monotonically across the four deuterostomes sampled from S. purpuratus (1.033) to H. sapiens (1.147). The q-exponential outperforms the ordinary exponential distribution across all 22 proteomes and becomes progressively more competitive against alternative two-parameter distributions as proteome complexity increases. These results identify the Tsallis entropic index as a continuous, physically interpretable indicator of proteome organizational complexity and extend the applicability of nonextensive statistical mechanics to proteomic systems.

cond-mat.stat-mech

Parameters of the Menzerath-Altmann law: Statistical mechanical interpretation as applied to a linguistic organization

The distribution behavior dictated by the Menzerath-Altmann (MA) law is frequently encountered in linguistic and natural organizations at various structural levels. The mathematical form of this empirical law comprises three fitting parameters whose values tend to be elusive, especially in inter-organizational studies. To allow interpretation of these parameters and better understand such distribution behavior, we present a statistical mechanical approach based on an analogy between the classical particles of a statistical mechanical organization and the number of distinct words in a textual organization. With this derivation, we achieve a transformed (generalized) form of the MA model, termed the statistical mechanical Menzerath-Altmann (SMMA) model. This novel transformed model consists of four parameters, one of which is a structure-dependent input parameter, and three of which are free-fitting parameters. Using distinct word data sets from two text corpora, we verified that the SMMA model describes the same distribution as the MA model. We propose that the additional structure-dependent parameter of the SMMA model converts the three fitting parameters into structure-independent parameters. Moreover, the parameters of the SMMA model are associated with a corresponding physical interpretation that can lead to characterization of an organization' s thermodynamic properties. We also propose that many organizations presenting MA law behavior, whether linguistic or not, can be examined by the SMMA distribution model through the properly defined structural degeneracy parameter and the energy associated states.

stat.ME