What it means to measure a single molecule in a solution by fluorescence fluctuation spectroscopy.

Traditional methodologies in micro- and nanofluidics measure biological mechanisms as an average of a population of molecules as only their combined effect can be detected. Fluorescence fluctuation spectroscopy methods such as fluorescence correlation spectroscopy (FCS) and two-color fluorescence cross-correlation spectroscopy (FCCS) are used as alternative experimental approaches in ultrasensitive analytics at the single-molecule level. However, what is the measurement time in which one is able to study just one single molecule in solution without immobilizing it? Existing theories are inadequate since they do not predict the meaningful time as a function of the concentration of other molecules of the same kind in bulk solution. This situation produces considerable concern, and experimental hypotheses differ according to which single-molecule detection methods are thought to have greater validity. This subject is clearly at the forefront of research and should be of great interest to experimental medical scientists. As will be seen in this article, it is worthwhile to obtain a correct form of the meaningful-time relationship through theoretical means. The new ideas are comprehensively presented, and this relationship is a new concept at this time. The meaningful time for studying just one molecule without immobilization specifies the time parameter in the selfsame molecule likelihood estimator. Possible users for this concept are those working in biotechnological applications dealing with gene technology. Furthermore, the concept is of interest for a great number of medical, pharmaceutical and chemical laboratories. It may serve as a foundation for further work in single-cell biology. It is suspected that heterogeneities play a much larger role inside the cell than in free solution--a perfect opportunity for single-molecule studies and, thus, a novel hypothesis regarding structure and dynamics of cellular networks is first presented for the minimal neurotrophin network model.