Search arXiv⌕ Search

arXiv · physics/0108055

Measure representation and multifractal analysis of complete genomes

Abstract

This paper introduces the notion of measure representation of DNA sequences. Spectral analysis and multifractal analysis are then performed on the measure representations of a large number of complete genomes. The main aim of this paper is to discuss the multifractal property of the measure representation and the classification of bacteria. From the measure representations and the values of the $D_{q}$ spectra and related $C_{q}$ curves, it is concluded that these complete genomes are not random sequences. In fact, spectral analyses performed indicate that these measure representations considered as time series, exhibit strong long-range correlation. For substrings with length K=8, the $D_{q}$ spectra of all organisms studied are multifractal-like and sufficiently smooth for the $C_{q}$ curves to be meaningful. The $C_{q}$ curves of all bacteria resemble a classical phase transition at a critical point. But the 'analogous' phase transitions of chromosomes of non-bacteria organisms are different. Apart from Chromosome 1 of {\it C. elegans}, they exhibit the shape of double-peaked specific heat function.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zu-Guo Yu, Vo Anh, Ka-Sing Lau. 2001-08-28. Measure representation and multifractal analysis of complete genomes. https://doi.org/10.1103/physreve.64.031903

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Decoding cell signaling via optimal transport and information theory

Cellular signal processing performs reliably despite molecular noise. Mutual information (MI) is widely used to quantify signaling fidelity, capturing how well outputs discriminate input states. However, it fails to capture whether the output preserves the statistical structure of the input, a property crucial in morphogen patterning and dose-dependent signaling. To address this gap, we introduce the 2-Wasserstein (2-WD) distance, which provides a geometric basis for comparing input and output distributions. We define MI as informational fidelity (INF) and the inverse of the 2-WD as geometric fidelity (GMF). Applying this dual-fidelity framework to canonical regulatory motifs under Gaussian channel approximation reveals topology-dependent trade-offs: coherent feed-forward loops can perform well in both dimensions, whereas feedback architectures reduce INF to enhance GMF. Experimental analysis of tumor necrosis factor signaling reveals dual-fidelity behavior qualitatively consistent with feedback regulation. RAS-MAPK data analysis further shows that jointly considering INF and GMF better characterizes intracellular signal relay than INF alone. Our results thus indicate that these signaling behaviors are not fully characterized by MI alone; instead, distributional correspondence provides a complementary dimension of signaling fidelity. Our study provides a practical framework for analyzing natural networks and guiding the design of task-specific synthetic circuits.

physics.bio-ph↗

Optimizing Evolution Under Cyclic Selection: the Case of B Cells

Rapid evolutionary adaptation may be necessary for organisms facing strong environmental pressures. Although selection often acts continuously, in the case of B cells evolving high affinity antibodies in germinal centers, selection can take place after multiple rounds of division. To study how the number of divisions between selection events affects adaptation, we develop a minimal computational model of cyclic selection. We find that the optimal average mutation rate per division and the corresponding adaptation velocity are nearly invariant across number of divisions between selections, and we quantitatively explain this behavior with a theory based on the dynamics of the "nose", i.e. the fittest individuals in the population. We also find that when the number of divisions between selection events is large, then mutating every division results in slower adaptation than mutating only at the last division before selection (as is the case for B cells). Our model provides a framework for understanding adaptation in natural and experimental systems subject to cyclic selection.

physics.bio-ph↗

Data-driven stochastic reconstruction of dengue epidemic dynamics: state-dependent stochasticity and temporal organization

Dengue epidemics exhibit strong stochastic variability, temporal memory and complex temporal organization that are difficult to capture using conventional deterministic epidemic models. However, the dependence of epidemic fluctuations on the underlying dynamical state and their influence on reconstruction across temporal scales remain insufficiently understood. In this work, we develop a data-driven stochastic framework to investigate dengue epidemic dynamics using daily infection data from Imphal West District, Manipur, India, for 2023-2025. We use Singular Spectrum Analysis (SSA) to separate deterministic and stochastic epidemic components. Logarithmic regression and autocorrelation analyses reveal empirically state-dependent noise amplitudes and persistent temporal correlations, supporting a phenomenological description involving multiplicative, temporally structures stochasticity. Polynomial and logistic-type drift functions are subsequently fitted to the deterministic components and integrated to reconstruct the epidemic incidence trajectories. Although polynomial drift provides high in-sample agreement with the deterministic dynamics, complexity-entropy analysis reveals that the reconstruction depends on the epidemic year and the embedding delay. A paired circular moving-block bootstrap further demonstrates that observed minimizing delays exhibit different levels of stability across the various reconstructions and years. Finally, we conducted a noise-amplitude sensitivity analysis, and it reveals pronounced changes in the optimal embedding delay and minimum Euclidean distance in the complexity-entropy plane. The proposed framework provides a data-driven approach to characterizing structured stochastic fluctuations and assessing the temporal organization of epidemic dynamics.

physics.bio-ph↗