A stripe-to-droplet transition driven by conformational transitions in a binary lipid-lipopolymer mixture at the air-water interface

We report the observation of an unusual stripe-droplet transition in precompressed Langmuir monolayers consisting of mixtures of poly(ethylene) glycol (PEG) amphiphiles and phospholipids. This highly reproducible and fully reversible transition occurs at approximately zero surface pressure during expansion (or compression) of the monolayer following initial compression into a two-dimensional solid phase. It is characterized by spontaneous emergence of an extended, disordered stripe-like morphology from an optically homogeneous phase during gradual expansion. These stripe patterns appear as a transient feature and continuously progress, involving gradual coarsening and ultimate transformation into a droplet morphology upon further expansion. Furthermore, varying relative concentrations of the two amphiphiles and utilizing amphiphiles with considerably longer ethylene glycol headgroups reveal that this pattern evolution occurs in narrow concentration regimes, values of which depend on ethylene oxide headgroup size. These morphological transitions are reminiscent of those seen during a passage through a critical point by variations in thermodynamic parameters (e.g., temperature or pressure) as well as those involving spinodal decomposition. While the precise mechanism cannot be ascertained using present experiments alone, our observations can be reconciled in terms of modulations in competing interactions prompted by the pancake-mushroom-brush conformational transitions of the ethylene glycol headgroup. This in turn suggests that the conformational degree of freedom represents an independent order parameter, or a switch, which can induce large-scale structural reorganization in amphiphilic monolayers. Because molecular conformational changes are pervasive in biological membranes, we speculate that such conformational transition-induced pattern evolution might provide a physical mechanism by which membrane processes are amplified.     

On the Criticality of Adaptive Boolean Network Robots

Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states.