Annual Review of Biophysics最新文献

Rigidity is an emergent property of materials-it is not a feature of individual components that compose the structure, but instead arises from interactions between many constituent parts. It has been recognized that floppy-rigid or fluid-solid transitions are harnessed by biological systems at all scales to drive form and function. This review focuses on the different mechanisms that can drive emergent rigidity transitions in biomechanical networks and describes how they arise in mathematical formalisms and how they are observed in practice in experiments. The goal is to aid researchers in identifying mechanisms governing rigidity in their biological systems of interest, highlight mechanical features that are universal across different systems, and help drive new scientific hypotheses for observed mechanical phenomena in biology. Looking forward, we also discuss how biological systems might tune themselves toward or away from such transitions over developmental or evolutionary timescales.

Since the beginning of this century, the emergence of systems biology, driven by technological, informatic, and theoretical advances, has led to an unprecedented generation of data and information about biological systems at multiple levels of organization. We now have access not only to components of living systems but also to some of the underlying principles governing their organization within networks. This review focuses on the systems biology of aging, metabolism, and mitochondria, along with the integration of experimental and computational systems biology approaches as applied to multilayered biological networks, spanning from the molecular-subcellular to the whole organism. Sections 2 and 3 provide an overview of the insights gained from systems biology and multi-omics approaches as applied to aging and metabolism. Using the spatiotemporal dynamics of biological networks as a unifying thread, Sections 4 and 5 explore how systems biology and current methods can leverage the understanding of complex biological phenomena through integrated experimental-computational strategies, utilizing iterative, verification-validation loops between experiments and models. Section 6 concludes by highlighting the autonomously dynamic, self-organizing, and self-regulating integrative nature of living systems and the need to address these properties at the emerging convergence of biology, medicine, physics, and powerful computational technologies that include artificial intelligence.

Intracellular protein patterns govern essential cellular functions by dynamically redistributing proteins between membrane-bound and cytosolic states, conserving their total numbers. This review presents a theoretical framework for understanding such patterns based on mass-conserving reaction-diffusion systems. The emergence, selection, and evolution of patterns are analyzed in terms of mass redistribution and interface motion, resulting in mesoscale laws of coarsening and wavelength selection. A geometric phase-space perspective provides a conceptual tool to link local reactive equilibria with global pattern dynamics through conserved mass fluxes. The Min protein system of Escherichia coli provides a paradigmatic example, enabling direct comparison between theory and experiment. Successive model refinements capture both the robustness of pattern formation and the diversity of dynamic regimes observed in vivo and in vitro. The Min system thus illustrates how to extract predictive, multiscale theory from biochemical detail, providing a foundation for understanding pattern formation in more complex and synthetic systems.

Photosynthesis, the biological process of converting light energy into chemical energy, involves light harvesting, charge separation and electron transport, proton translocation, ATP synthesis, and carbon fixation, among other processes. Adjacent photosynthetic complexes may assemble into supramolecular complexes to couple and regulate their functions. Here, we review the progress of structural biology studies of photosynthetic supramolecular complexes, such as those that have light-harvesting complexes assembled with photosystem II (PSII) or photosystem I (PSI), both PSII and PSI, or bacterial reaction center complexes. The intricate architectures of the NADH dehydrogenase-like (NDH) complex and PSI-NDH supercomplex, revealed through cryo-electron microscopy studies, provide crucial frameworks for understanding the molecular mechanisms of cyclic electron flow in cyanobacteria and plants. Furthermore, structural studies have also yielded detailed insights into the assembly and repair of PSII, regulation of ATP synthase, and carbon fixation. The review concludes with a summary of the emerging directions of structural biology studies of photosynthetic supramolecular complexes.

Chemical signaling underlies many biological processes, and membrane receptors such as G protein-coupled receptors and ligand-gated ion channels represent two of the most pharmacologically important protein families. Advances in single-molecule fluorescence techniques have transformed our understanding of molecular mechanisms, including protein folding, transcription, and ligand binding. Unlike ensemble measurements, which average over populations and obscure molecular heterogeneity, single-molecule approaches enable direct observation of individual events, revealing rare conformational states and distinguishing between mechanisms that are indistinguishable at the ensemble level. This review highlights how single-molecule FRET (smFRET) and single-molecule fluorescence ligand binding (smFLiB) provide complementary insights into ligand-dependent receptor activation and allosteric coupling. smFRET offers structural information by tracking conformational transitions, but limited observation times can hinder detection of slow or infrequent events. In contrast, smFLiB allows long-duration monitoring of ligand-receptor interactions throughout the activation pathway, though with less direct information about structural rearrangements. Through selected case studies, we illustrate how these techniques have been applied to dissect the complexity of ligand-receptor interactions with unprecedented resolution. These advances hold promise for guiding the rational design of more selective and effective therapeutics targeting membrane proteins.

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specificity and sensitivity. Quantitatively understanding how T cell receptors discern among antigens requires biophysical models and theoretical analyses of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the context of modern experimental and computational advances. Antigen potency spans a continuum and exhibits nonlinear effects within complex mixtures, challenging discrete classification and simple threshold-based models. This complexity motivates the development of models, such as adaptive kinetic proofreading, that integrate both activating and inhibitory signals. Advances in high-throughput technologies now generate large-scale, quantitative data sets, enabling the refinement of such models through statistical and machine learning approaches. This convergence of theory, data, and computation promises deeper insights into immune decision-making and opens new avenues for rational immunotherapy design.

Voltage-dependent anion channels (VDACs) of the outer mitochondrial membrane carry out bidirectional flux of metabolites and ions and serve as the first line of communication between the cytosol and mitochondria. They are now recognized as indispensable for mitochondrial function and cellular homeostasis, mitochondria-endoplasmic reticulum communication, lipid and cholesterol biogenesis, Ca2+ homeostasis, and mitochondria-mediated apoptosis. The unique structural features of VDACs are also important in redox regulation. VDAC dysregulation by interaction with amyloid-β, α-synuclein, Tau, or tubulin can lead to neurodegeneration. Here, we provide insights into the structures, isoform-specific molecular functions, cellular interactome, variations, and unique regulatory elements of VDACs and their direct implications in widespread burdens like cancer and neurodegeneration in humans. We discuss how deducing isoform-specific structure-function studies of VDACs has the potential for successful development of next-generation diagnostics-guided therapeutics.

Hopfield models, originally developed to study memory retrieval in neural networks, have become versatile tools for modeling diverse biological systems in which function emerges from collective dynamics. In this review, we provide a pedagogical introduction to both classic and modern Hopfield networks from a biophysical perspective. After presenting the underlying mathematics, we build physical intuition through three complementary interpretations of Hopfield dynamics: as noise discrimination, as a geometric construction defining a natural coordinate system in pattern space, and as gradient-like descent on an energy landscape. We then survey applications of Hopfield networks in a variety of biological settings, including cellular differentiation and epigenetic memory, molecular self-assembly, and spatial neural representations.

Over the last decade, proteomics analysis of single cells by mass spectrometry transitioned from an uncertain possibility to a set of robust and rapidly advancing technologies supporting the accurate quantification of thousands of proteins. We review the major drivers of this progress, from establishing feasibility to powerful and increasingly scalable methods. We focus on the trade-offs and synergies of different technological solutions within a coherent conceptual framework, which projects considerable room both for throughput scaling and for extending the analysis scope to functional protein measurements. We highlight the potential of these technologies to support the development of mechanistic biophysical models and to help uncover new principles.

Nanopores have become transformative tools in single-molecule chemical analysis, enabling detailed interrogation of molecular interactions and reaction dynamics. These advancements have revolutionized the characterization of chemical kinetics and stereospecificity, broadening nanopore applications. This review evaluates the principles of nanopore single-molecule chemistry, highlighting breakthroughs in chemically reactive nanopore construction via site-specific mutagenesis, semisynthetic engineering, and orthogonal modifications. Notably, we highlight the innovative strategies enabling precise subunit stoichiometry control to ensure single-molecule reactions, and the integration of machine learning for high-fidelity ionic current analysis. These developments position nanopores as versatile tools for intricate molecular detection in fundamental and applied research. Looking forward, nanopore single-molecule chemistry promises an impact on diagnostics, environmental monitoring, and precision medicine. Integration of molecular dynamics simulations, artificial intelligence-driven protein design frameworks, and microsystems technology may expand detectable species, enhancing robustness and lowering detection limits. Such advancements will deepen our understanding of chemical transformations and support meaningful real-world applications of nanopore technologies.