Quantitative Biology最新文献

The transcriptome, the complete set of RNA molecules within a cell, plays a critical role in regulating physiological processes. The advent of RNA sequencing (RNA-seq) facilitated by Next Generation Sequencing (NGS) technologies, has revolutionized transcriptome research, providing unique insights into gene expression dynamics. This powerful strategy can be applied at both bulk tissue and single-cell levels. Bulk RNA-seq provides a gene expression profile within a tissue sample. Conversely, single-cell RNA sequencing (scRNA-seq) offers resolution at the cellular level, allowing the uncovering of cellular heterogeneity, identification of rare cell types, and distinction between distinct cell populations. As computational tools, machine learning techniques, and NGS sequencing platforms continue to evolve, the field of transcriptome research is poised for significant advancements. Therefore, to fully harness this potential, a comprehensive understanding of bulk RNA-seq and scRNA-seq technologies, including their advantages, limitations, and computational considerations, is crucial. This review provides a systematic comparison of the computational processes involved in both RNA-seq and scRNA-seq, highlighting their fundamental principles, applications, strengths, and limitations, while outlining future directions in transcriptome research.

The divergence rate between the alignable genomes of humans and chimpanzees is as little as 1.23%. Their phenotypical difference was hypothesized to be accounted for by gene regulation. We construct the cis-regulatory element frequency (CREF) matrix to represent the proximal regulatory sequences for each species. Each CREF matrix is further decomposed into dual eigen-modules. By comparing the CREF modules of four existing hominid species, we examine their quantitative and qualitative changes along evolution. We identified two saltations: one between the 4th and 5th, the other between the 9th and 10th eigen-levels. The cognition and intelligence unique to humans are thus found from the saltations at the molecular level. They include long-term memory, cochlea/inner ear morphogenesis that enables the development of human language/music, social behavior that allows us to live together peacefully and to work collaboratively, and visual/observational/associative learning. Moreover, we found exploratory behavior crucial for humans' creativity, the GABA-B receptor activation that protects our neurons, and serotonin biosynthesis/signaling that regulates our happiness. We observed a remarkable increase in the number of motifs present on Alu elements on the 4th/9th motif-eigenvectors. The cognition and intelligence unique to humans can, by and large, be identified using only the CREF profiles without any a priori. Although gradual evolution might be the only mode in the mutations of protein sequences, the evolution of gene regulation has both gradual and saltational modes, which could be explained by the framework of CREF eigen-modules.

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.

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.

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.

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 l _c , including the occupation time and first passage time. These two kinds of statistics help us understand the time scale of cell senescence.

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.

The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre-trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large-scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)-based models and open-source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API-based models GPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged behind their API-based counterparts, whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh-aza).