Fluctuation in plasma membranes is an important biophysical process that is of both biological and technical relevance. For example, the vibrating fluctuation allowed the first observation of red blood cells (RBCs) through optical microscopy. Membrane fluctuation can also regulate many cellular functions, such as membrane adhesion and receptor kinetics. Physically, the main driving forces for membrane displacement during fluctuation are thermal noise and biologically active processes, namely, active fluctuation. While thermal fluctuations have been extensively studied over the years, there is a lack of a consistent understanding about active processes that are responsible for cellular processes in a membrane. For instance, the effect of membrane fluctuation on the ligand–receptor (un)binding dynamics is not clear till now: it is challenging to derive such correlations, mainly because there are various activity sources that need to be taken into account, such as conformational changes of channels and the structural complexity of different cells. In a recent study, Ana-Sunčana Smith and colleagues provided a mathematical solution to this challenge by establishing a cell-type independent framework to correlate membrane fluctuations and cellular processes, such as ligand–receptor binding.
The authors built a mechanical model that can couple different levels of complexity in the membrane. First, a simple microscopic model that consists of a target ligand with a distance from a membrane receptor was used to recover the Bell–Dembo rate, which is the time-average of instantaneous rates within a given molecular configuration. To account for thermal fluctuation, the simple membrane in the first step was replaced by a Gaussian membrane in which a harmonic spring was used to account for thermal fluctuations. To further account for active processes, a linear spring was added to the Gaussian membrane model. This resulted in two convoluted components in the model: Gaussian and Laplace distributed noises. The authors applied this framework to two different cell systems — human macrophages and RBCs — and calculated the ligand–receptor (un)binding rate before and after the activation. Interestingly, though the underlying biological activation processes on these two cell membranes are completely different, the proposed mechanical model can account for both active fluctuation spectra. Following this finding, a uniform formula for calculating the rates was developed. Overall, the proposed mechanical model sheds light on a unified framework for further investigating membrane fluctuations, especially those from active biological processes, and can enrich our knowledge of controlling cellular processes.
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