Physical biology最新文献

Understanding the link between structure and function in proteins is fundamental in molecular biology and proteomics. A central question in this context is whether allostery-where the binding of a molecule at one site affects the activity of a distant site-emerges as a further manifestation of the intricate interplay between structure, function, and intrinsic dynamics. This study explores how allosteric regulation is modified when intrinsic protein dynamics operates under out-of-equilibrium conditions. To this purpose, we introduce a simple non-equilibrium model of protein dynamics, inspired by active matter systems, by generalizing the widely employed Gaussian network model to incorporate non-thermal effects. Our approach underscores the advantage of framing allostery as a causal process by using, as a benchmark system, the second PDZ domain of the human phosphatase human Protein Tyrosine Phosphatase 1E that mediates protein-protein interactions. We employ causal indicators, such as response functions and transfer entropy, to identify the network of PDZ2 residues through which the allosteric signal propagates across the protein structure. These indicators reveal specific regions that align well with experimental observations. Furthermore, our results suggest that deviations from purely thermal fluctuations can significantly influence allosteric communication by introducing distinct timescales and memory effects. This influence is particularly relevant when the allosteric response unfolds on timescales incompatible with relaxation to equilibrium. Accordingly, non-thermal fluctuations may become essential for accurately describing protein responses to ligand binding and developing a comprehensive understanding of allosteric regulation.

Many biological systems regulate phenotypic heterogeneity as a fitness-maximising strategy in uncertain and dynamic environments. Analysis of such strategies is typically confined both to a discrete set of environmental conditions, and to a discrete (often binary) set of phenotypes specialised to each condition. In this work, we extend theory on both fronts to encapsulate a potentially continuous spectrum of phenotypes arising in response to environmental fluctuations that drive changes in the phenotype-dependent growth rate. We consider two broad classes of stochastic environment: those that are temporally uncorrelated (modelled by white-noise processes), and those that are correlated (modelled by Poisson and Ornstein-Uhlenbeck processes). For tractability, we restrict analysis to an exponential growth model, and consider biologically relevant simplifications that pertain to the timescale of phenotype switching relative to fluctuations in the environment. These assumptions yield a series of analytical and semi-analytical expressions that reveal environments in which phenotypic heterogeneity is evolutionarily advantageous.

An enormous amount of gene expression data is currently available online in repositories for several organisms. Microarray data can be used to identify co-expressed genes that may be involved in the same biological process. Therefore, the analysis and interpretation of this information could help organize and understand the knowledge it contains, representing a major challenge in the post-genomic era. Here, we grouped genes ofEscherichia coliK-12 using expression data to infer meaningful transcriptional regulatory information. Our method assumes that co-expressed genes reflect functional units, as evidenced by their genetic structure, including gene arrangement, regulation, and participation in defined biological processes. These functionally linked clusters were validated with curated transcriptional regulatory information from RegulonDB. From 907 growth conditions, 420 clusters were formed involving 1674 genes. Clusters contained from 2 to 64 genes. We found that co-expressed genes participate in related metabolic pathways and share similar types of regulation (through transcription factors,σ-factors, allosteric regulation, or micro-RNA regulation). This study is helpful for identifying novel transcriptional regulatory interactions.

Organelle abundance in cells is tightly regulated in response to external stimuli, but the underlying mechanisms remain poorly understood. Time-lapse imaging of fluorescently labelled organelles enables single-cell measurements of organelle copy numbers, revealing the time evolution of their distribution across a cell population. Building on a recently proposed kinetic model of organelle biogenesis, which incorporates de novo synthesis, fission, fusion, and degradation, we explore the time-dependent dynamics of organelle abundance. While previous studies focused on steady-state properties, here we calculate the first two moments of: 1) organelle copy numbers over time, and 2) first passage times to reach a specified organelle count. We show that these two moments provide a powerful means to discriminate between different mechanisms of organelle biogenesis. Notably, the time-dependent behaviour of organelle biogenesis reveals richer dynamics compared to the steady-state scenario. Our findings shed light on how cells attain steady-state organelle abundance after cell division and environmental perturbation.

Prolyl oligopeptidase (PREP) has gained attention for its role in neurodegenerative diseases, particularly through protein-protein interactions with amyloid proteins such as alpha-synuclein and Tau. Although significant research has focused on PPIs, the substrate-binding dynamics within the catalytic pocket of PREP is less understood. This study combines molecular docking and molecular dynamics simulations to investigate the behavior of known PREP substrates, including thyrotropin-releasing hormone. Our simulations reveal that TRH transitions between three preferred regions within the binding pocket, one of which is favorable for catalytic activity. The absence of a single fixed binding site near the catalytic triad region may suggest a dynamic substrate-processing mechanism. Additionally, the potential of the TRH precursor as a substrate is evaluated. Our findings highlight the utility of computational methods in the analysis of protein dynamics and enzymatic mechanisms, offering insights into the functional versatility of PREP.

Magnetotactic bacteria (MTB) are microorganisms that biomineralize intracellular magnetic nanoparticles inside a membrane vesicle/invagination. The set membrana + magnetic nanoparticle is known as magnetosome and generally magnetosomes are organized in linear chains in the cytoplasm, conferring a magnetic moment to the MTB. Due to their magnetic properties, MTB swim by following local magnetic field lines. This property makes MTB a suitable model to study bacterial movement. There are theoretical models to analyze the swimming of MTB, but the majority consider monotrichous bacteria. Only one model is related to the swimming of bilophotrichous bacteria, but they do not report the resultant trajectory parameters as a function of the magnetic field. Also, the literature lacks an experimental analysis of the trajectories of bilophotrichous MTB. The present study analyzes the movement of the bilphotrichous MTBMagnetofaba australisstrain IT-1 exposed to different magnetic field intensities. The trajectories are composed of two oscillations, one of low frequency and large amplitude and another of high frequency and small amplitude. The amplitudes show a magnetic field dependency, and the frequencies show to be magnetic field independent. The analysis of the trajectory orientation relative to the magnetic field direction shows that magnetotaxis ofM. australisfor low magnetic fields is not as efficient as expected, perhaps due to living in a liquid culture medium rich in nutrients. As far as we know, this is the first time that these movement data have been obtained, and they will be important to validate future theoretical models of movement for bilophotrichous MTB.

Bacterial biofilms, surface-attached microbial communities, grow horizontally across surfaces and vertically above them. Although a simple heuristic model for vertical growth was experimentally shown to accurately describe the behavior of diverse microbial species, the biophysical implications and theoretical basis for this empirical model were unclear. Here, we demonstrate that this heuristic model emerges naturally from fundamental principles of active fluid dynamics. By analytically deriving solutions for an active fluid model of vertical biofilm growth, we show that the governing equations reduce to the same form as the empirical model in both early- and late-stage growth regimes. Our analysis reveals that cell death and decay rates likely play key roles in determining the characteristic parameters of vertical growth. The active fluid model produces a single, simple equation governing growth at all heights that is surprisingly simpler than the heuristic model. With this theoretical basis, we explain why the vertical growth rate reaches a maximum at a height greater than the previously identified characteristic length scale. These results provide a theoretical foundation for a simple mathematical model of vertical growth, enabling deeper understanding of how biological and biophysical factors interact during biofilm development.

Although various empirical studies have reported a positive correlation between the specific growth rate and cell size across bacteria, it is currently unclear what causes this relationship. We conjecture that such scaling occurs because smaller cells have a larger surface-to-volume ratio and thus have to allocate a greater fraction of the total resources to the production of the cell envelope, leaving fewer resources for other biosynthetic processes. To test this theory, we developed a coarse-grained model of bacterial physiology composed of the proteome that converts nutrients into biomass, with the cell envelope acting as a resource sink. Assuming resources are partitioned to maximize the growth rate, the model predicts that the growth rate and ribosomal mass fraction scale negatively, while the mass fraction of envelope-producing enzymes scales positively with surface-to-volume. These relationships are compatible with growth measurements and quantitative proteomics data reported in the literature.

Unlike flocks of birds and schools of fish that show net motion and synchronized motion, insect mating swarms are stationary and lack velocity ordering. Their collective nature when unperturbed is instead evident in their spatial statistics. In stark contrast with bird flocks, wherein the number density can fluctuate enormously from flock to flock, the number density of individuals in laboratory swarms of the midgeChironomus ripariusis approximately constant. Nonetheless, as swarms grow more populous, individuals cluster more and more. Here with the aid of stochastic trajectory models I show that these two seemingly contradictory behaviours can be attributed to the presence of multiplicative noise. The modelling also predicts that swarms are most stable when they are asymptotically large.

The field of quantitative biology (q-bio) seeks to provide precise and testable explanations for observed biological phenomena by applying mathematical and computational methods. The central goals of q-bio are to (1) systematically propose quantitative hypotheses in the form of mathematical models, (2) demonstrate that these models faithfully capture a specific essence of a biological process, and (3) correctly forecast the dynamics of the process in new, and previously untested circumstances. Achieving these goals depends on accurate analysis and incorporating informative experimental data to constrain the set of potential mathematical representations. In this introductory tutorial, we provide an overview of the state of the field and introduce some of the computational methods most commonly used in q-bio. In particular, we examine experimental techniques in single-cell imaging, computational tools to process images and extract quantitative data, various mechanistic modeling approaches used to reproduce these quantitative data, and techniques for data-driven model inference and model-driven experiment design. All topics are presented in the context of additional online resources, including open-source Python notebooks and open-ended practice problems that comprise the technical content of the annual Undergraduate Quantitative Biology Summer School (UQ-Bio).