Annual Review of Biophysics - Early Publication

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding the patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

Over the last 30 years, fluorescence microscopy, renowned for its sensitivity and specificity, has undergone a revolution in resolving ever-smaller details. This advancement began with stimulated emission depletion (STED) microscopy and progressed with techniques such as photoactivatable localization microscopy and stochastic optical reconstruction microscopy (STORM). Single-molecule localization microscopy (SMLM), which encompasses methods like direct STORM, has significantly enhanced image resolution. Even though its speed is slower than that of STED, SMLM achieves higher resolution by overcoming photobleaching limitations, particularly through DNA point accumulation for imaging in nanoscale topography (DNA-PAINT), which continuously renews fluorescent labels. Additionally, cryo-fluorescence microscopy and advanced techniques like minimal photon fluxes imaging (MINFLUX) have pushed the boundaries toward molecular resolution SMLM. This review discusses the latest developments in SMLM, highlighting methods like resolution enhancement by sequential imaging (RESI) and PAINT-MINFLUX and exploring axial localization techniques such as supercritical angle fluorescence and metal-induced energy transfer. These advancements promise to revolutionize fluorescence microscopy, providing resolution comparable to that of electron microscopy.

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.

Low-molecular-weight oligomers formed from amyloidogenic peptides and proteins have been identified as key cytotoxins across a range of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease. Developing therapeutic strategies that target oligomers is therefore emerging as a promising approach for combating protein misfolding diseases. As such, there is a great need to understand the fundamental properties, dynamics, and mechanisms associated with oligomer formation. In this review, we discuss how chemical kinetics provides a powerful tool for studying these systems. We review the chemical kinetics approach to determining the underlying molecular pathways of protein aggregation and discuss its applications to oligomer formation and dynamics. We discuss how this approach can reveal detailed mechanisms of primary and secondary oligomer formation, including the role of interfaces in these processes. We further use this framework to describe the processes of oligomer conversion and dissociation, and highlight the distinction between on-pathway and off-pathway oligomers. Furthermore, we showcase on the basis of experimental data the diversity of pathways leading to oligomer formation in various in vitro and in silico systems. Finally, using the lens of the chemical kinetics framework, we look at the current oligomer inhibitor strategies both in vitro and in vivo.

Microbial cell factories have been developed to produce various compounds in a sustainable and economically viable manner. The yeast Saccharomyces cerevisiae has been used as a platform cell factory in industrial biotechnology with numerous advantages, including ease of operation, rapid growth, and tolerance for various industrial stressors. Advances in synthetic biology and metabolic models have accelerated the design–build–test–learn cycle in metabolic engineering, significantly facilitating the development of yeast strains with complex phenotypes, including the redirection of metabolic fluxes to desired products, the expansion of the spectrum of usable substrates, and the improvement of the physiological properties of strain. Strains with enhanced titer, rate, and yield are now competing with traditional petroleum-based industrial approaches. This review highlights recent advances and perspectives in the metabolic engineering of yeasts for the production of a variety of compounds, including fuels, chemicals, proteins, and peptides, as well as advancements in synthetic biology tools and mathematical modeling.

Double electron–electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.