In recent years, exploring the physical mechanisms of brain functions has been a hot topic in the fields of nonlinear dynamics and complex networks, and many important achievements have been made, mainly based on the characteristic features of time series of human brain. To speed up the further study of this problem, herein we make a brief review on these important achievements, which includes the aspects of explaining: (i) the mechanism of brain rhythms by network synchronization, (ii) the mechanism of unihemispheric sleep by chimera states, (iii) the fundamental difference between the structural and functional brain networks by remote synchronization, (iv) the mechanism of stronger detection ability of human brain to weak signals by remote firing propagation, and (v) the mechanism of dementia patterns by eigen-microstate analysis. As a brief review, we will mainly focus on the aspects of basic ideas, research histories, and key results but ignore the tedious mathematical derivations. Moreover, some outlooks will be discussed for future studies.
Sperm development is critical for male reproductive capability; any disruption during the process of spermatogenesis will result in male infertility. In this research, we used the C-Nap1 encoded by the gene of Cep250 knockout mouse line as the model to evaluate the impact of absent C-Nap1 on spermatogenesis. To investigate the interaction between C-Nap1 and spermatogenesis, we utilized single-cell RNA sequencing to analyze 10,332 C-Nap1+/+ and 13,308 C-Nap1−/− testicular cells. We identified five main cell types within seminiferous tubules, including spermatogonia, Sertoli cells, spermatogonia stem cells, Leydig cells, and spermatocytes. We found a critical reduction in testicular spermatogonia and spermatocytes in C-Nap1-null testes, compared to its C-Nap1+/+ controls. By combining uniform manifold approximation and projection clustering and psedotime ordering, we distinguished five spermatogonial stages/subtypes, demonstrating that type B spermatogonia differentiation and meiotic initiation are impaired during C-Nap1-null spermatogenesis. Following gene ontology enrichment analysis, meiosis-specific genes downregulated in the C-Nap1−/− testicular cells were further verified by reverse transcription polymerase chain reaction (RT-PCR). Based on the differential gene expression, certain downregulated genes such as Ctnnb1 and Aurka encoding C-Nap1-binding potential β-Catenin and Aurka are encountered, which may account for defective type B spermatogonia differentiation and meiotic entry in C-Nap1-null testes.
Multifunctional therapeutic peptides (MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The AMHF-TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and self-attention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods, the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP’s exceptional performance and stability in recognition tasks.
Cell senescence has attracted much attention in the long history of human beings, and telomere shortening (TS) is one of the main concerns in the study of cell senescence. To reveal the microscopic mechanism of TS process, we model it based on molecular stochastic process from the perspective of nonequilibrium statistical physics. We associate the TS process with the continuous time random walk and derive the Fokker–Planck equation to describe the length distribution of the TS. We further modify the model describing the TS process, similar to the anomalous tempered diffusion, and derive the Feynman–Kac equation characterizing the functional distribution of the TS process. Finally, we study the statistics related to the critical telomere length Ic, including the occupation time and first passage time. These two kinds of statistics help us understand the time scale of cell senescence.
Living systems operate within physical constraints imposed by nonequilibrium thermodynamics. This review explores recent advancements in applying these principles to understand the fundamental limits of biological functions. We introduce the framework of stochastic thermodynamics and its recent developments, followed by its application to various biological systems. We emphasize the interconnectedness of kinetics and energetics within this framework, focusing on how network topology, kinetics, and energetics influence functions in thermodynamically consistent models. We discuss examples in the areas of molecular machine, error correction, biological sensing, and collective behaviors. This review aims to bridge physics and biology by fostering a quantitative understanding of biological functions.
Explaining biodiversity is the central focus in theoretical ecology. A significant obstacle arises from the competitive exclusion principle (CEP), which states that two species competing for the same type of resources cannot coexist at constant population densities, or more generally, the number of consumer species cannot exceed that of resource species at steady states. The conflict between CEP and biodiversity is exemplified by the paradox of the plankton, where a few types of limiting resources support a plethora of plankton species. In this review, we introduce mechanisms proposed over the years for promoting biodiversity in ecosystems, with a special focus on those that alleviate the constraints imposed by the CEP, including mechanisms that challenge the CEP in well-mixed systems at a steady state or those that circumvent its limitations through contextual differences.
Self-organized pattern formation is common in biological systems. Microbial populations can generate spatiotemporal patterns through various mechanisms, such as chemotaxis, quorum sensing, and mechanical interactions. When their motile behavior is coupled to a gravitational potential field, swimming microorganisms display a phenomenon known as bioconvection, which is characterized by the pattern formation of active cellular plumes that enhance material mixing in the fluid. While bioconvection patterns have been characterized in various organisms, including eukaryotic and bacterial microswimmers, the dynamics of bioconvection pattern formation in bacteria is less explored. Here, we study this phenomenon using suspensions of a chemotactic bacterium Bacillus subtilis confined in closed three-dimensional (3D) fluid chambers. We discovered an active plume lattice pattern that displays hexagonal order and emerges via a self-organization process. By flow field measurement, we revealed a toroidal flow structure associated with individual plumes. We also uncovered a power-law scaling relation between the lattice pattern’s wavelength and the dimensionless Rayleigh number that characterizes the ratio of buoyancy-driven convection to diffusion. Taken together, this study highlights that coupling between chemotaxis and external potential fields can promote the self-assembly of regular spatial structures in bacterial populations. The findings are also relevant to material transport in surface water environments populated by swimming microorganisms.
Gene transcription is a stochastic process characterized by fluctuations in mRNA levels of the same gene in isogenic cell populations. A central question in single-cell studies is how to map transcriptional variability to phenotypic differences between isogenic cells. We introduced a measurable and statistical transcription threshold I for critical genes that determine the entry level of Waddington’s canal toward a specific cell fate. Subsequently, JI, which is the probability that a cell has at least I mRNA molecules of a given gene, approximates the likelihood of a cell committing to the corresponding fate. In this study, we extended the previous results of JI of the classical telegraph model by considering more complex models with different gene activation frameworks. We showed that (a) the upregulation of the critical gene may significantly suppress cell fate change and (b) increasing transcription noise performs a bidirectional role that can either enhance or suppress the cell fate change. These observations matched accurately with the data from bacterial, yeast, and mammalian cells. We estimated the threshold I from these data and predicted that (a) the traditional human immunodeficiency virus (HIV) activators that modulate gene activation frequency at high doses may largely suppress HIV reactivation and (b) the cells may favor noisier (or less noisy) regulation of stress genes under high (or low) environmental pressures to maintain cell viability.
Embryogenesis is the most basic process in developmental biology. Effectively and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic cells. Traditional descriptors such as volume, surface area, and mean curvature often fall short, providing only a global view and lacking in local detail and reconstruction capability. Addressing this, we introduce an effective integrated method, 3D Cell Shape Quantification (3DCSQ), for transforming digitized 3D cell shapes into analytical feature vectors, named eigengrid (proposed grid descriptor like eigen value), eigenharmonic, and eigenspectrum. We uniquely combine spherical grids, spherical harmonics, and principal component analysis for cell shape quantification. We demonstrate 3DCSQ’s effectiveness in recognizing cellular morphological phenotypes and clustering cells. Applied to Caenorhabditis elegans embryos of 29 living embryos from 4- to 350-cell stages, 3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell deformations. We also provide automatically cell shape lineaging analysis program. This method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes, presenting an advancement in biological imaging and analysis.