Single-Molecule Tracking in Live Cell by Simulations: Thermodynamic Jitter

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The measurement of the individual molecule or particle is one of the most demanding research trends in spectroscopy, microscopy and nanoscopy. This is not least the case because individual molecules (oligonucleotides and single-stranded DNA sequences) were first theoretically described in chemical oligonucleotide syntheses on solid supports after their release from the solid phase as early as 1994 [20], but corresponding measurements are not yet possible today. A head start is the mathematically proven theory that turns knowledge into strength, even at odds with the mediocre mainstream; for example, ref. [21], with additional examples found in ref. [11]. What new insights can be gained from the foundation of the theory on ‘single-molecule/single-particle biophysics and biochemistry based on the stochastic nature of diffusion’, that is, the new physical theory for quantifying the thermodynamic jitter of molecules/particles without immobilization on an artificial surface, on a biological structure or without significant hydrodynamic flow? For example, the thermodynamic signatures of single molecules and single particles in liquids and live cells (cytoplasm, membranes, nucleus) without immobilization or hydrodynamic flow were found in ref. [14] and shown by simulations in Figure 1 and Figure 2 of the present article. There may be too much thermodynamic jitter in the experiments with live cells and diluted liquids without immobilization or significant hydrodynamic flow under physiological conditions or at room temperature [22]. Taken together, this article presents a significant advancement in single-molecule/particle biophysics and biochemistry through simulations demonstrating localized Brownian motion and fractal diffusion. The introduction and methods sections provide a thorough background and detailed methodology. Of course, there are many types of live cells. The study does not differentiate between them, but this could be achieved by using more specific values for underlying fractal structures and distribution functions.

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