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Understanding the role of water in membrane protein structure and function is crucial for elucidating the mechanisms that govern cellular processes. Recent experiments with the G-protein-coupled receptor (GPCR) archetype rhodopsin have shed light on polymer osmotic effects as an important metric for studying hydration in membrane protein activation. Still, to gain mechanistic insights into the multifaceted problem of membrane protein hydration involving lipids, membranes, and polymers, one needs information at atomistic resolution.

Low-angle X-ray diffraction is a powerful technique for analyzing the molecular structure of the myofilaments of striated muscle in situ. It has contributed greatly to our understanding of the relaxed, 430-Å-repeating organization of myosin heads in thick filaments in skeletal and cardiac muscle. Using X-ray diffraction, changes in filament structure can be detected on the Å length scale and millisecond time scale, leading to models that are the foundation of our understanding of the structural basis of contraction.

The epidermal growth factor receptor (EGFR) family of receptor tyrosine kinases (RTKs) plays a central role in cell signaling pathways that regulate proliferation, differentiation, and survival. Aberrant signaling within this family, often caused by mutations or overexpression, drives the progression of many cancers. EGFR and HER2, for example, serve as biomarkers for cancer detection and treatment; however, clinical outcomes still require significant improvement. This review examines the structural details of EGFR-family oligomerization, with a focus on insights gained from advanced fluorescence-based methodologies.

Micron-scale, liquid-liquid phase separation occurs in membranes of living cells, with physiological consequences. To discover which lipids might support phase separation in cell membranes and how lipids might partition between phases, miscibility phase diagrams have been mapped for model membranes. Typically, model membranes are composed of ternary mixtures of a lipid with a high melting temperature, a lipid with a low melting temperature, and cholesterol. Phospholipids in ternary mixtures are chosen primarily to favor stable membranes (phosphatidylcholines and sphingomyelins) or add charge (phosphatidylglycerols and phosphatidylserines).

Advances in functional imaging have transformed neuroscience, enabling real-time mapping of neural activity and cellular dynamics. Techniques such as light-sheet microscopy allow whole-brain recordings in model organisms like C. elegans and zebrafish, revealing mechanisms of sensorimotor processing, learning, and neural circuit formation. More recently, the vast complexity of these datasets necessitates machine learning tools for efficient analysis. Machine Learning-driven approaches improve data quality through denoising, automate segmentation of neurons and tissues, and enable analyses on complex data.

The transport of organelles is important to maintain cellular organization and function. Efficient retrograde transport of large organelles with a size of several micrometers requires high collective forces from multiple dynein motors. However, the exact transport forces and their dependence on the cargo size are unknown for large organelles. Furthermore, it is not known how many dynein motors are active during this transport and how they to generate high collective forces sufficient to overcome the cytoplasmic drag.

Biological membranes and liquid crystals are closely related because they exhibit similar types of molecular ordering and symmetry. This deep connection led many researchers in the ‘60s and ‘70s to cross back and forth between the two domains of research. Erich Sackmann crossed over early in his career, taking concepts and techniques from liquid crystal physics to membrane biology, and stayed to provide unique insights into the organisation and behaviour of membranes, helping to lay the foundations of physics of biological membranes and biophysics as we know it today.

Many biological processes can be thought of as the result of an underlying dynamics in which the system repeatedly undergoes distinct and abortive trajectories with the dynamical process only ending when some specific process, purpose, structure or function is achieved. A classic example is the way in which microtubules attach to kinetochores as a prerequisite for chromosome segregation and cell division. In this example, the dynamics is characterized by apparently futile time histories in which microtubules repeatedly grow and shrink without chromosomal attachment.

Macromolecular structure is central to biology. Yet, not all biomolecules have a well–defined fold. Intrinsically disordered regions are ubiquitous, conveying a versatility to function even in otherwise folded structures. For nucleic acids, entropic disorder is manifest in regions of incomplete base pairing (e.g., during transcription) and for long molecules (i.e., beyond the persistence length). To classify the resulting ensembles, we develop a method to cluster based on secondary structure, focusing specifically on DNA and RNA.

The concept of the circular bioeconomy is a carbon neutral, sustainable system with zero waste. One vision for such an economy is based upon lignocellulosic biomass. This lignocellulosic circular bioeconomy requires CO2 absorption from biomass growth and the efficient deconstruction of recalcitrant biomass into solubilized and fractionated biopolymers which are then used as precursors for the sustainable production of high-quality liquid fuels, chemical bioproducts and bio-based materials. Here, we summarize the roles that molecular dynamics (MD) simulations and machine learning (ML) are playing in overcoming several fundamental challenges hindering the adoption of a circular bioeconomy.