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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.

The viscosity of the plasma membrane in living cells is a crucial biophysical parameter that regulates cellular functions. We categorize the plasma membrane viscosity into short-range and long-range viscosities based on the spatial scale of the cellular processes they influence. Short-range viscosity originates from the Brownian motion of membrane molecules, i.e., the nanometer-scale motion of molecules, and regulates signal transduction and membrane transport. It is reported that the short-range viscosity in living cells is almost the same or at most 10 times greater than that of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) membranes.

The dengue virus (DENV) poses a significant threat to human health, accounting for approximately 400 million infections each year. Its genome features a circular structure that facilitates replication through long-range RNA-RNA interactions, utilizing cyclization sequences located in the untranslated regions (UTRs). To gain new insights into the organization of the DENV genome, we purified the 5′ and 3′ UTRs of DENV in vitro and examined their structural and binding properties using various biophysical techniques combined with computational methods.

The glucagon-like peptide-1 receptor (GLP-1R) is a class B G protein-coupled receptor (GPCR) that plays an important role in metabolic regulation, and consequently is a target for type 2 diabetes and obesity therapeutics. Although cholesterol has been reported to be implicated in receptor activation, its interactions with the receptor during the activation cycle have not been probed. Using coarse-grained molecular dynamics simulations, we have characterized the cholesterol interactions with GLP-1R in four conformational states: the inactive, partially active, GLP-1-bound active, and exenatide-bound active conformational states.

Plectin is a giant protein of the plakin family that crosslinks the cytoskeleton of mammalian cells. It is expressed in virtually all tissues and its dysfunction is associated with various diseases such as skin blistering. There is evidence that plectin regulates the mechanical integrity of the cytoskeleton in diverse cell and tissue types. However, it is unknown how plectin modulates the mechanical response of cells depending on the frequency and amplitude of mechanical loading. Here we demonstrate the role of plectin in the viscoelastic properties of fibroblasts at small and large deformations by quantitative single-cell compression measurements.