Résumé
The cooperative binding of molecular agents onto a substrate is pervasive in living systems. To study whether a system shows cooperativity, one can rely on a fluctuation analysis of quantities such as the number of substrate-bound units and the residence time in an occupancy state. Since the relative standard deviation from the statistical mean monotonically decreases with the number of binding sites, these techniques are only suitable for small enough systems, such as those implicated in stochastic processes inside cells. Here, we employ a general-purpose grand canonical Hamiltonian description of a small one-dimensional (1D) lattice gas with either nearest-neighbor or long-range interactions as prototypical examples of cooperativity-influenced adsorption processes. First, building upon previous work on finite-size one-dimensional Ising-type models, we elucidate how the strength and sign of the interaction potential between neighboring bound particles on the lattice determine the intensity of the fluctuations of the mean occupancy and the nature of bound particle-particle correlations. We present our theoretical results, which extend beyond standard analysis, in a novel, physically transparent form. We explore simple limiting cases of the parameter space in greater detail, examine the complete probability distribution functions for occupation, and relate these to the shape and strength of the fluctuations. Second, we leverage these relationships to compare the theoretical predictions of our model to data from single molecule experiments on bacterial flagellar motors (BFM) of Escherichia coli. In this way, we find evidence that cooperativity controls the mechano-sensitive dynamical assembly of the torque-generating units, the so-called stator units, onto the BFM and thereby arrive at an estimate of the stator-stator interaction potential from our fluctuation analysis. Furthermore, we attempt to clarify the link between occupation fluctuations and the adaptability of the BFM. Finally, we conclude that the system resides in a sweet spot of the parameter space (phase diagram) with characteristics suitable for a smoothly and widely adaptive system, while minimizing fluctuations.